Publications

Journals

Alshamaila, Y., Papagiannidis, S., Alsawalqah, H., & Aljarah, I.. (2023). Effective use of smart cities in crisis cases: a systematic review of the literature. International journal of disaster risk reduction, 103521.
[BibTeX]

@article{alshamaila2023effective,
title={Effective use of smart cities in crisis cases: A systematic review of the literature},
author={Alshamaila, Yazn and Papagiannidis, Savvas and Alsawalqah, Hamad and Aljarah, Ibrahim},
journal={International Journal of Disaster Risk Reduction},
pages={103521},
year={2023},
publisher={Elsevier}
}

Alzaqebah, A., Aljarah, I., & Al-Kadi, O.. (2023). A hierarchical intrusion detection system based on extreme learning machine and nature-inspired optimization. Computers & security, 124, 102957.
[BibTeX]

@article{alzaqebah2023hierarchical,
title={A hierarchical intrusion detection system based on extreme learning machine and nature-inspired optimization},
author={Alzaqebah, Abdullah and Aljarah, Ibrahim and Al-Kadi, Omar},
journal={Computers \& Security},
volume={124},
pages={102957},
year={2023},
publisher={Elsevier}
}

Abu-Salih, B., Qudah, D. A., Al-Hassan, M., Ghafari, S. M., Issa, T., Aljarah, I., Beheshti, A., & Alqahtani, S.. (2022). An intelligent system for multi-topic social spam detection in microblogging. Journal of information science, 1655515221124062.
[BibTeX]

@article{abu2022intelligent,
title={An intelligent system for multi-topic social spam detection in microblogging},
author={Abu-Salih, Bilal and Qudah, Dana Al and Al-Hassan, Malak and Ghafari, Seyed Mohssen and Issa, Tomayess and Aljarah, Ibrahim and Beheshti, Amin and Alqahtani, Sulaiman},
journal={Journal of Information Science},
pages={01655515221124062},
year={2022},
publisher={SAGE Publications Sage UK: London, England}
}

Shannaq, F., Hammo, B., Faris, H., & Castillo-Valdivieso, P. A.. (2022). Offensive language detection in arabic social networks using evolutionary-based classifiers learned from fine-tuned embeddings. Ieee access, 10, 75018–75039.
[BibTeX]

@article{shannaq2022offensive,
title={Offensive Language Detection in Arabic Social Networks Using Evolutionary-Based Classifiers Learned From Fine-Tuned Embeddings},
author={Shannaq, Fatima and Hammo, Bassam and Faris, Hossam and Castillo-Valdivieso, Pedro A},
journal={IEEE Access},
volume={10},
pages={75018--75039},
year={2022},
publisher={IEEE}
}

Safi, S. A., Castillo, P. A., & Faris, H.. (2022). Cost-sensitive metaheuristic optimization-based neural network with ensemble learning for financial distress prediction. Applied sciences, 12(14), 6918.
[BibTeX]

@article{safi2022cost,
title={Cost-Sensitive Metaheuristic Optimization-Based Neural Network with Ensemble Learning for Financial Distress Prediction},
author={Safi, Salah Al-Deen and Castillo, Pedro A and Faris, Hossam},
journal={Applied Sciences},
volume={12},
number={14},
pages={6918},
year={2022},
publisher={MDPI}
}

Sharma, L. D., Bohat, V. K., Habib, M., Ala’M, A., Faris, H., & Aljarah, I.. (2022). Evolutionary inspired approach for mental stress detection using eeg signal. Expert systems with applications, 197, 116634.
[BibTeX]

@article{sharma2022evolutionary,
title={Evolutionary inspired approach for mental stress detection using EEG signal},
author={Sharma, Lakhan Dev and Bohat, Vijay Kumar and Habib, Maria and Ala’M, Al-Zoubi and Faris, Hossam and Aljarah, Ibrahim},
journal={Expert Systems with Applications},
volume={197},
pages={116634},
year={2022},
publisher={Elsevier}
}

Alzaqebah, A., Aljarah, I., Al-Kadi, O., & Damaševičius, R.. (2022). A modified grey wolf optimization algorithm for an intrusion detection system. Mathematics, 10(6), 999.
[BibTeX]

@article{alzaqebah2022modified,
title={A Modified Grey Wolf Optimization Algorithm for an Intrusion Detection System},
author={Alzaqebah, Abdullah and Aljarah, Ibrahim and Al-Kadi, Omar and Dama{\v{s}}evi{\v{c}}ius, Robertas},
journal={Mathematics},
volume={10},
number={6},
pages={999},
year={2022},
publisher={MDPI}
}

Faris, H., Faris, M., Habib, M., & Alomari, A.. (2022). Automatic symptoms identification from a massive volume of unstructured medical consultations using deep neural and bert models. Heliyon, e09683.
[BibTeX]

@article{faris2022automatic,
title={Automatic Symptoms Identification from a Massive Volume of Unstructured Medical Consultations Using Deep Neural and BERT models},
author={Faris, Hossam and Faris, Mohammad and Habib, Maria and Alomari, Alaa},
journal={Heliyon},
pages={e09683},
year={2022},
publisher={Elsevier}
}

Abu Khurma, R., Aljarah, I., Sharieh, A., Abd Elaziz, M., Damaševičius, R., & Krilavičius, T.. (2022). A review of the modification strategies of the nature inspired algorithms for feature selection problem. Mathematics, 10(3), 464.
[BibTeX]

@article{abu2022review,
title={A review of the modification strategies of the nature inspired algorithms for feature selection problem},
author={Abu Khurma, Ruba and Aljarah, Ibrahim and Sharieh, Ahmad and Abd Elaziz, Mohamed and Dama{\v{s}}evi{\v{c}}ius, Robertas and Krilavi{\v{c}}ius, Tomas},
journal={Mathematics},
volume={10},
number={3},
pages={464},
year={2022},
publisher={MDPI}
}

Shannag, F., Hammo, B. H., & Faris, H.. (2022). The design, construction and evaluation of annotated arabic cyberbullying corpus. Education and information technologies, 1–47.
[BibTeX]

@article{shannag2022design,
title={The design, construction and evaluation of annotated Arabic cyberbullying corpus},
author={Shannag, Fatima and Hammo, Bassam H and Faris, Hossam},
journal={Education and Information Technologies},
pages={1--47},
year={2022},
publisher={Springer}
}

Al-Ahmad, B. I., Ala’A, A., Kabir, M. F., Al-Tawil, M., & Aljarah, I.. (2022). Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects. Peerj computer science, 8, e857.
[BibTeX]

@article{al2022swarm,
title={Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects},
author={Al-Ahmad, Bilal I and Ala’A, Al-Zoubi and Kabir, Md Faisal and Al-Tawil, Marwan and Aljarah, Ibrahim},
journal={PeerJ Computer Science},
volume={8},
pages={e857},
year={2022},
publisher={PeerJ Inc.}
}

Al-Zoubi, A., Mora, A. M., & Faris, H.. (2022). Spam reviews detection in the time of covid-19 pandemic: background, definitions, methods and literature analysis. Applied sciences, 12(7), 3634.
[BibTeX]

@article{al2022spam,
title={Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis},
author={Al-Zoubi, Ala’M and Mora, Antonio M and Faris, Hossam},
journal={Applied Sciences},
volume={12},
number={7},
pages={3634},
year={2022},
publisher={MDPI}
}

Hussien, A. G., Hashim, F. A., Qaddoura, R., Abualigah, L., & Pop, A.. (2022). An enhanced evaporation rate water-cycle algorithm for global optimization. Processes, 10(11), 2254.
[BibTeX]

@article{hussien2022enhanced,
title={An enhanced evaporation rate water-cycle algorithm for global optimization},
author={Hussien, Abdelazim G and Hashim, Fatma A and Qaddoura, Raneem and Abualigah, Laith and Pop, Adrian},
journal={Processes},
volume={10},
number={11},
pages={2254},
year={2022},
publisher={MDPI}
}

Qaddoura, R., & Younes, M. B.. (2022). Temporal prediction of traffic characteristics on real road scenarios in amman. Journal of ambient intelligence and humanized computing, 1–16.
[BibTeX]

@article{qaddoura2022temporal,
title={Temporal prediction of traffic characteristics on real road scenarios in Amman},
author={Qaddoura, Raneem and Younes, Maram Bani},
journal={Journal of Ambient Intelligence and Humanized Computing},
pages={1--16},
year={2022},
publisher={Springer Berlin Heidelberg}
}

Obiedat, R., Qaddoura, R., Ala’M, A., Al-Qaisi, L., Harfoushi, O., Alrefai, M., & Faris, H.. (2022). Sentiment analysis of customers’ reviews using a hybrid evolutionary svm-based approach in an imbalanced data distribution. Ieee access, 10, 22260–22273.
[BibTeX]

@article{obiedat2022sentiment,
title={Sentiment analysis of customers’ reviews using a hybrid evolutionary SVM-based approach in an imbalanced data distribution},
author={Obiedat, Ruba and Qaddoura, Raneem and Ala’M, Al-Zoubi and Al-Qaisi, Laila and Harfoushi, Osama and Alrefai, Mo’ath and Faris, Hossam},
journal={IEEE Access},
volume={10},
pages={22260--22273},
year={2022},
publisher={IEEE}
}

Faris, H., Habib, M., Faris, M., Alomari, A., Castillo, P. A., & Alomari, M.. (2022). Classification of arabic healthcare questions based on word embeddings learned from massive consultations: a deep learning approach. Journal of ambient intelligence and humanized computing, 13(4), 1811–1827.
[BibTeX]

@article{faris2022classification,
title={Classification of Arabic healthcare questions based on word embeddings learned from massive consultations: a deep learning approach},
author={Faris, Hossam and Habib, Maria and Faris, Mohammad and Alomari, Alaa and Castillo, Pedro A and Alomari, Manal},
journal={Journal of Ambient Intelligence and Humanized Computing},
volume={13},
number={4},
pages={1811--1827},
year={2022},
publisher={Springer}
}

Alhmoud, L., Ala’M, A., & Aljarah, I.. (2022). Solar pv power forecasting at yarmouk university using machine learning techniques. Open engineering, 12(1), 1078–1088.
[BibTeX]

@article{alhmoud2022solar,
title={Solar PV power forecasting at Yarmouk University using machine learning techniques},
author={Alhmoud, Lina and Ala’M, Al-Zoubi and Aljarah, Ibrahim},
journal={Open Engineering},
volume={12},
number={1},
pages={1078--1088},
year={2022},
publisher={De Gruyter Open Access}
}

@article{obiedat2022sentiment,
title={Sentiment analysis of customers’ reviews using a hybrid evolutionary SVM-based approach in an imbalanced data distribution},
author={Obiedat, Ruba and Qaddoura, Raneem and Ala’M, Al-Zoubi and Al-Qaisi, Laila and Harfoushi, Osama and Alrefai, Mo’ath and Faris, Hossam},
journal={IEEE Access},
volume={10},
pages={22260--22273},
year={2022},
publisher={IEEE}
}

Awawdeh, S., Faris, H., & Hiary, H.. (2022). Evoimputer: an evolutionary approach for missing data imputation and feature selection in the context of supervised learning. Knowledge-based systems, 236, 107734.
[BibTeX]

@article{awawdeh2022evoimputer,
title={EvoImputer: An evolutionary approach for Missing Data Imputation and feature selection in the context of supervised learning},
author={Awawdeh, Shatha and Faris, Hossam and Hiary, Hazem},
journal={Knowledge-Based Systems},
volume={236},
pages={107734},
year={2022},
publisher={Elsevier}
}

Hijazi, N. M., Faris, H., & Aljarah, I.. (2021). A parallel metaheuristic approach for ensemble feature selection based on multi-core architectures. Expert systems with applications, 182, 115290.
[BibTeX]

@article{hijazi2021parallel,
title={A parallel metaheuristic approach for ensemble feature selection based on multi-core architectures},
author={Hijazi, Neveen Mohammed and Faris, Hossam and Aljarah, Ibrahim},
journal={Expert Systems with Applications},
volume={182},
pages={115290},
year={2021},
publisher={Elsevier}
}

Abuqaddom, I., Mahafzah, B. A., & Faris, H.. (2021). Oriented stochastic loss descent algorithm to train very deep multi-layer neural networks without vanishing gradients. Knowledge-based systems, 230, 107391.
[BibTeX]

@article{abuqaddom2021oriented,
title={Oriented stochastic loss descent algorithm to train very deep multi-layer neural networks without vanishing gradients},
author={Abuqaddom, Inas and Mahafzah, Basel A and Faris, Hossam},
journal={Knowledge-Based Systems},
volume={230},
pages={107391},
year={2021},
publisher={Elsevier}
}

@article{hijazi2021parallel,
title={A parallel metaheuristic approach for ensemble feature selection based on multi-core architectures},
author={Hijazi, Neveen Mohammed and Faris, Hossam and Aljarah, Ibrahim},
journal={Expert Systems with Applications},
volume={182},
pages={115290},
year={2021},
publisher={Elsevier}
}

Habib, M., Faris, M., Alomari, A., & Faris, H.. (2021). Altibbivec: a word embedding model for medical and health applications in the arabic language. Ieee access, 9, 133875–133888.
[BibTeX]

@article{habib2021altibbivec,
title={AltibbiVec: A Word Embedding Model for Medical and Health Applications in the Arabic Language},
author={Habib, Maria and Faris, Mohammad and Alomari, Alaa and Faris, Hossam},
journal={IEEE Access},
volume={9},
pages={133875--133888},
year={2021},
publisher={IEEE}
}

Alhmoud, L., Abu Khurma, R., Al-Zoubi, A., & Aljarah, I.. (2021). A real-time electrical load forecasting in jordan using an enhanced evolutionary feedforward neural network. Sensors, 21(18), 6240.
[BibTeX]

@article{alhmoud2021real,
title={A Real-Time Electrical Load Forecasting in Jordan Using an Enhanced Evolutionary Feedforward Neural Network},
author={Alhmoud, Lina and Abu Khurma, Ruba and Al-Zoubi, Ala’M and Aljarah, Ibrahim},
journal={Sensors},
volume={21},
number={18},
pages={6240},
year={2021},
publisher={MDPI}
}

Tao, H., Habib, M., Aljarah, I., Faris, H., Afan, H. A., & Yaseen, Z. M.. (2021). An intelligent evolutionary extreme gradient boosting algorithm development for modeling scour depths under submerged weir. Information sciences, 570, 172–184.
[BibTeX]

@article{tao2021intelligent,
title={An intelligent evolutionary extreme gradient boosting algorithm development for modeling scour depths under submerged weir},
author={Tao, Hai and Habib, Maria and Aljarah, Ibrahim and Faris, Hossam and Afan, Haitham Abdulmohsin and Yaseen, Zaher Mundher},
journal={Information Sciences},
volume={570},
pages={172--184},
year={2021},
publisher={Elsevier}
}

Khurma, R. A., Aljarah, I., & Sharieh, A.. (2021). A simultaneous moth flame optimizer feature selection approach based on levy flight and selection operators for medical diagnosis. Arabian journal for science and engineering, 46(9), 8415–8440.
[BibTeX]

@article{khurma2021simultaneous,
title={A simultaneous moth flame optimizer feature selection approach based on levy flight and selection operators for medical diagnosis},
author={Khurma, Ruba Abu and Aljarah, Ibrahim and Sharieh, Ahmad},
journal={Arabian Journal for Science and Engineering},
volume={46},
number={9},
pages={8415--8440},
year={2021},
publisher={Springer}
}

Alhadid, I., Khwaldeh, S., Al Rawajbeh, M., Abu-Taieh, E., Masa’deh, R., & Aljarah, I.. (2021). An intelligent web service composition and resource-optimization method using k-means clustering and knapsack algorithms. Mathematics, 9(17), 2023.
[BibTeX]

@article{alhadid2021intelligent,
title={An intelligent web service composition and resource-optimization method using K-means clustering and knapsack algorithms},
author={Alhadid, Issam and Khwaldeh, Sufian and Al Rawajbeh, Mohammad and Abu-Taieh, Evon and Masa’deh, Ra’ed and Aljarah, Ibrahim},
journal={Mathematics},
volume={9},
number={17},
pages={2023},
year={2021},
publisher={MDPI}
}

Abu Khurma, R., Almomani, I., & Aljarah, I.. (2021). Iot botnet detection using salp swarm and ant lion hybrid optimization model. Symmetry, 13(8), 1377.
[BibTeX]

@article{abu2021iot,
title={Iot botnet detection using salp swarm and ant lion hybrid optimization model},
author={Abu Khurma, Ruba and Almomani, Iman and Aljarah, Ibrahim},
journal={Symmetry},
volume={13},
number={8},
pages={1377},
year={2021},
publisher={MDPI}
}

Khurma, R. A., Alsawalqah, H., Aljarah, I., Elaziz, M. A., & Damaševičius, R.. (2021). An enhanced evolutionary software defect prediction method using island moth flame optimization. Mathematics, 9(15), 1722.
[BibTeX]

@article{khurma2021enhanced,
title={An enhanced evolutionary software defect prediction method using island moth flame optimization},
author={Khurma, Ruba Abu and Alsawalqah, Hamad and Aljarah, Ibrahim and Elaziz, Mohamed Abd and Dama{\v{s}}evi{\v{c}}ius, Robertas},
journal={Mathematics},
volume={9},
number={15},
pages={1722},
year={2021},
publisher={MDPI}
}

Obiedat, R., Al-Qaisi, L., Qaddoura, R., Harfoushi, O., & Al-Zoubi, A.. (2021). An intelligent hybrid sentiment analyzer for personal protective medical equipments based on word embedding technique: the covid-19 era. Symmetry, 13(12), 2287.
[BibTeX]

@article{obiedat2021intelligent,
title={An Intelligent Hybrid Sentiment Analyzer for Personal Protective Medical Equipments Based on Word Embedding Technique: The COVID-19 Era},
author={Obiedat, Ruba and Al-Qaisi, Laila and Qaddoura, Raneem and Harfoushi, Osama and Al-Zoubi, Ala’M},
journal={Symmetry},
volume={13},
number={12},
pages={2287},
year={2021},
publisher={MDPI}
}

Obiedat, R., Harfoushi, O., Qaddoura, R., Al-Qaisi, L., & Al-Zoubi, A.. (2021). An evolutionary-based sentiment analysis approach for enhancing government decisions during covid-19 pandemic: the case of jordan. Applied sciences, 11(19), 9080.
[BibTeX]

@article{obiedat2021evolutionary,
title={An evolutionary-based sentiment analysis approach for enhancing government decisions during COVID-19 pandemic: the case of jordan},
author={Obiedat, Ruba and Harfoushi, Osama and Qaddoura, Raneem and Al-Qaisi, Laila and Al-Zoubi, Ala’M},
journal={Applied Sciences},
volume={11},
number={19},
pages={9080},
year={2021},
publisher={MDPI}
}

Qaddoura, R., Al-Zoubi, A., Almomani, I., & Faris, H.. (2021). A multi-stage classification approach for iot intrusion detection based on clustering with oversampling. Applied sciences, 11(7), 3022.
[BibTeX]

@article{qaddoura2021multi,
title={A multi-stage classification approach for iot intrusion detection based on clustering with oversampling},
author={Qaddoura, Raneem and Al-Zoubi, Ala’M and Almomani, Iman and Faris, Hossam},
journal={Applied Sciences},
volume={11},
number={7},
pages={3022},
year={2021},
publisher={MDPI}
}

Qaddoura, R., M. Al-Zoubi, A., Faris, H., & Almomani, I.. (2021). A multi-layer classification approach for intrusion detection in iot networks based on deep learning. Sensors, 21(9), 2987.
[BibTeX]

@article{qaddoura2021multi,
title={A multi-layer classification approach for intrusion detection in iot networks based on deep learning},
author={Qaddoura, Raneem and M. Al-Zoubi, Ala’ and Faris, Hossam and Almomani, Iman},
journal={Sensors},
volume={21},
number={9},
pages={2987},
year={2021},
publisher={MDPI}
}

Almomani, I., Qaddoura, R., Habib, M., Alsoghyer, S., Al Khayer, A., Aljarah, I., & Faris, H.. (2021). Android ransomware detection based on a hybrid evolutionary approach in the context of highly imbalanced data. Ieee access, 9, 57674–57691.
[BibTeX]

@article{almomani2021android,
title={Android ransomware detection based on a hybrid evolutionary approach in the context of highly imbalanced data},
author={Almomani, Iman and Qaddoura, Raneem and Habib, Maria and Alsoghyer, Samah and Al Khayer, Alaa and Aljarah, Ibrahim and Faris, Hossam},
journal={IEEE Access},
volume={9},
pages={57674--57691},
year={2021},
publisher={IEEE}
}

Habib, M., Faris, M., Qaddoura, R., Alomari, A., & Faris, H.. (2021). A predictive text system for medical recommendations in telemedicine: a deep learning approach in the arabic context. Ieee access, 9, 85690–85708.
[BibTeX]

@article{habib2021predictive,
title={A Predictive Text System for Medical Recommendations in Telemedicine: A Deep Learning Approach in the Arabic Context},
author={Habib, Maria and Faris, Mohammad and Qaddoura, Raneem and Alomari, Alaa and Faris, Hossam},
journal={IEEE Access},
volume={9},
pages={85690--85708},
year={2021},
publisher={IEEE}
}

Qaddoura, R., Faris, H., & Aljarah, I.. (2020). An efficient evolutionary algorithm with a nearest neighbor search technique for clustering analysis. Journal of ambient intelligence and humanized computing, 1–26.
[BibTeX]

@article{qaddoura2020efficient,
title={An efficient evolutionary algorithm with a nearest neighbor search technique for clustering analysis},
author={Qaddoura, Raneem and Faris, Hossam and Aljarah, Ibrahim},
journal={Journal of Ambient Intelligence and Humanized Computing},
pages={1--26},
year={2020},
publisher={Springer Berlin Heidelberg}
}

Aljarah, I., Habib, M., Hijazi, N., Faris, H., Qaddoura, R., Hammo, B., Abushariah, M., & Alfawareh, M.. (2020). Intelligent detection of hate speech in arabic social network: a machine learning approach. Journal of information science, 165551520917651. doi:10.1177/0165551520917651
[BibTeX] [Abstract]

Nowadays, cyber hate speech is increasingly growing, which forms a serious problem worldwide by threatening the cohesion of civil societies. Hate speech relates to using expressions or phrases that are violent, offensive or insulting for a person or a minority of people. In particular, in the Arab region, the number of Arab social media users is growing rapidly, which is accompanied with high increasing rate of cyber hate speech. This drew our attention to aspire healthy online environments that are free of hatred and discrimination. Therefore, this article aims to detect cyber hate speech based on Arabic context over Twitter platform, by applying Natural Language Processing (NLP) techniques, and machine learning methods. The article considers a set of tweets related to racism, journalism, sports orientation, terrorism and Islam. Several types of features and emotions are extracted and arranged in 15 different combinations of data. The processed dataset is experimented using Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF), in which RF with the feature set of Term Frequency-Inverse Document Frequency (TF-IDF) and profile-related features achieves the best results. Furthermore, a feature importance analysis is conducted based on RF classifier in order to quantify the predictive ability of features in regard to the hate class.

@article{aljarah2020intelligent,
title={Intelligent detection of hate speech in Arabic social network: A machine learning approach},
author={Aljarah, Ibrahim and Habib, Maria and Hijazi, Neveen and Faris, Hossam and Qaddoura, Raneem and Hammo, Bassam and Abushariah, Mohammad and Alfawareh, Mohammad},
journal={Journal of Information Science},
abstract = {Nowadays, cyber hate speech is increasingly growing, which forms a serious problem worldwide by threatening the cohesion of civil societies. Hate speech relates to using expressions or phrases that are violent, offensive or insulting for a person or a minority of people. In particular, in the Arab region, the number of Arab social media users is growing rapidly, which is accompanied with high increasing rate of cyber hate speech. This drew our attention to aspire healthy online environments that are free of hatred and discrimination. Therefore, this article aims to detect cyber hate speech based on Arabic context over Twitter platform, by applying Natural Language Processing (NLP) techniques, and machine learning methods. The article considers a set of tweets related to racism, journalism, sports orientation, terrorism and Islam. Several types of features and emotions are extracted and arranged in 15 different combinations of data. The processed dataset is experimented using Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF), in which RF with the feature set of Term Frequency-Inverse Document Frequency (TF-IDF) and profile-related features achieves the best results. Furthermore, a feature importance analysis is conducted based on RF classifier in order to quantify the predictive ability of features in regard to the hate class.},
pages={0165551520917651},
year={2020},
doi = {10.1177/0165551520917651},
publisher={SAGE Publications Sage UK: London, England}
}

Qaddoura, R., Manaseer, W. A., Abushariah, M. A., & Alshraideh, M. A.. (2020). Dental radiography segmentation using expectation-maximization clustering and grasshopper optimizer. Multimedia tools and applications, 79, 22027–22045.
[BibTeX]

@article{qaddoura2020dental,
title={Dental radiography segmentation using expectation-maximization clustering and grasshopper optimizer},
author={Qaddoura, Raneem and Manaseer, Waref Al and Abushariah, Mohammad AM and Alshraideh, Mohammad Aref},
journal={Multimedia Tools and Applications},
volume={79},
pages={22027--22045},
year={2020},
publisher={Springer US}
}

Yaseen, Z., Faris, H., & Al-Ansari, N.. (2020). Hybridized extreme learning machine model with salp swarm algorithm: a novel predictive model for hydrological application. Frontiers in data-driven methods for understanding, prediction, and control of complex systems. doi:10.1155/2020/8206245
[BibTeX] [Abstract]

The capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. The classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. This current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. The results are evaluated based on several statistical measures and graphical presentations. The river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.

@Article{Yaseen2020hybridized,
author = {Yaseen, Z. and Faris, H. and Al-Ansari, N.},
title = {Hybridized Extreme Learning Machine Model with Salp Swarm Algorithm: A Novel Predictive Model for Hydrological Application},
journal = {Frontiers in Data-Driven Methods for Understanding, Prediction, and Control of Complex Systems},
year = {2020},
abstract = {The capability of the extreme learning machine (ELM) model in modeling stochastic, nonlinear, and complex hydrological engineering problems has been proven remarkably. The classical ELM training algorithm is based on a nontuned and random procedure that might not be efficient in convergence of excellent performance or possible entrapment in the local minima problem. This current study investigates the integration of a newly explored metaheuristic algorithm (i.e., Salp Swarm Algorithm (SSA)) with the ELM model to forecast monthly river flow. Twenty years of river flow data time series of the Tigris river at the Baghdad station, Iraq, is used as a case study. Different input combinations are applied for constructing the predictive models based on antecedent values. The results are evaluated based on several statistical measures and graphical presentations. The river flow forecast accuracy of SSA-ELM outperformed the classical ELM and other artificial intelligence (AI) models. Over the testing phase, the proposed SSA-ELM model yielded a satisfactory enhancement in the level accuracies (8.4 and 13.1 percentage of augmentation for RMSE and MAE, respectively) against the classical ELM model. In summary, the study ascertains that the SSA-ELM model is a qualified data-intelligent model for monthly river flow prediction at the Tigris river, Iraq.},
doi = {10.1155/2020/8206245},
publisher = {Hindawi}
}

Rawashdeh, H., Awawdeh, S., Shannag, F., Henawi, E., Faris, H., Obeid, N., & Hyett, J.. (2020). Intelligent system based on data mining techniques for prediction of preterm birth for women with cervical cerclage. Computational biology and chemistry. doi:10.1016/j.compbiolchem.2020.107233
[BibTeX] [Abstract]

Preterm birth, defined as a delivery before 37 weeks’ gestation, continues to affect 8-15% of all pregnancies and is associated with significant neonatal morbidity and mortality. Effective prediction of timing of delivery among women identified to be at significant risk for preterm birth would allow proper implementation of prophylactic therapeutic interventions. This paper aims first to develop a model that acts as a decision support system for pregnant women at high risk of delivering prematurely before having cervical cerclage. The model will predict whether the pregnancy will continue beyond 26 weeks’ gestation and the potential value of adding the cerclage in prolonging the pregnancy. The second aim is to develop a model that predicts the timing of spontaneous delivery in this high risk cohort after cerclage. The model will help treating physicians to define the chronology of management in relation to the risk of preterm birth, reducing the neonatal complications associated with it. Data from 274 pregnancies managed with cervical cerclage were included. 29 of the procedures involved multiple pregnancies. To build the first model, a data balancing technique called SMOTE was applied to overcome the problem of highly imbalanced class distribution in the dataset. After that, four classification models, namely Decision Tree, Random Forest, K-Nearest Neighbors (K-NN), and Neural Network (NN) were used to build the prediction model. The results showed that Random Forest classifier gave the best results in terms of G-mean and sensitivity with values of 0.96 and 1.00, respectively. These results were achieved at an oversampling ratio of 200%. For the second prediction model, five classification models were used to predict the time of spontaneous delivery; Linear Regression, Gaussian Process, Random Forest, K-star, and LWL classifier. The Random Forest classifier performed best, with 0.752 correlation value. In conclusion, computational models can be developed to predict the need for cerclage and the gestation of delivery after this procedure. These models have moderate/high sensitivity for clinical application.

@Article{Eshtay2020competitive,
author = {Rawashdeh, H. and Awawdeh, Sh. and Shannag, F. and Henawi, E. and Faris, H. and Obeid, N. and Hyett, J.},
title = {Intelligent system based on data mining techniques for prediction of Preterm Birth for women with Cervical Cerclage},
journal = {Computational Biology and Chemistry},
year = {2020},
abstract = {Preterm birth, defined as a delivery before 37 weeks’ gestation, continues to affect 8-15% of all pregnancies and is associated with significant neonatal morbidity and mortality. Effective prediction of timing of delivery among women identified to be at significant risk for preterm birth would allow proper implementation of prophylactic therapeutic interventions. This paper aims first to develop a model that acts as a decision support system for pregnant women at high risk of delivering prematurely before having cervical cerclage. The model will predict whether the pregnancy will continue beyond 26 weeks’ gestation and the potential value of adding the cerclage in prolonging the pregnancy. The second aim is to develop a model that predicts the timing of spontaneous delivery in this high risk cohort after cerclage. The model will help treating physicians to define the chronology of management in relation to the risk of preterm birth, reducing the neonatal complications associated with it. Data from 274 pregnancies managed with cervical cerclage were included. 29 of the procedures involved multiple pregnancies. To build the first model, a data balancing technique called SMOTE was applied to overcome the problem of highly imbalanced class distribution in the dataset. After that, four classification models, namely Decision Tree, Random Forest, K-Nearest Neighbors (K-NN), and Neural Network (NN) were used to build the prediction model. The results showed that Random Forest classifier gave the best results in terms of G-mean and sensitivity with values of 0.96 and 1.00, respectively. These results were achieved at an oversampling ratio of 200%. For the second prediction model, five classification models were used to predict the time of spontaneous delivery; Linear Regression, Gaussian Process, Random Forest, K-star, and LWL classifier. The Random Forest classifier performed best, with 0.752 correlation value. In conclusion, computational models can be developed to predict the need for cerclage and the gestation of delivery after this procedure. These models have moderate/high sensitivity for clinical application.},
doi = {10.1016/j.compbiolchem.2020.107233},
publisher = {Elsevier}
}

Eshtay, M., Faris, H., & Obeid, N.. (2020). A competitive swarm optimizer with hybrid encoding for simultaneously optimizing the weights and structure of extreme learning machines for classification problems. International journal of machine learning and cybernetics, 1–23. doi:10.1007/s13042-020-01073-y
[BibTeX] [Abstract]

Extreme Learning Machine (ELM) is a learning algorithm proposed recently to train single hidden layer feed forward networks (SLFN). It has many attractive properties that include better generalization performance and very fast learning. ELM starts by assigning random values to the input weights and hidden biases and then in one step it determines the output weights using Moore-Penrose generalized inverse. Despite the aforementioned advantages, ELM performance might be affected by the random initialization of weights and biases or by the large generated network which might contain unnecessary number of neurons. In order to increase the generalization performance and to produce more compact networks, a hybrid model that combines ELM with competitive swarm optimizer (CSO) is proposed in this paper. The proposed model (CSONN-ELM) optimizes the weights and biases and dynamically determines the most appropriate number of neurons. To evaluate the effectiveness of the CSONN-ELM, it is experimented using 23 benchmark datasets, and compared to a set of static rules extracted from literature that are used to determine the number of neurons of SLFN. Moreover, it is compared to two dynamic methods that are used to enhance the performance of ELM, that are Optimally pruned ELM (OP-ELM) and metaheuristic based ELMs (Particle Swarm Optimization-ELM and Differential Evolution-ELM). The obtained results show that the proposed method enhances the generalization performance of ELM and overcomes the static and dynamic methods.

@Article{Eshtay2020competitive,
author = {Eshtay, M. and Faris, H. and Obeid, N.},
title = {A competitive swarm optimizer with hybrid encoding for simultaneously optimizing the weights and structure of Extreme Learning Machines for classification problems},
journal = {International Journal of Machine Learning and Cybernetics},
year = {2020},
pages = {1--23},
issn = {1868-8071},
abstract = {Extreme Learning Machine (ELM) is a learning algorithm proposed recently to train single hidden layer feed forward networks (SLFN). It has many attractive properties that include better generalization performance and very fast learning. ELM starts by assigning random values to the input weights and hidden biases and then in one step it determines the output weights using Moore-Penrose generalized inverse. Despite the aforementioned advantages, ELM performance might be affected by the random initialization of weights and biases or by the large generated network which might contain unnecessary number of neurons. In order to increase the generalization performance and to produce more compact networks, a hybrid model that combines ELM with competitive swarm optimizer (CSO) is proposed in this paper. The proposed model (CSONN-ELM) optimizes the weights and biases and dynamically determines the most appropriate number of neurons. To evaluate the effectiveness of the CSONN-ELM, it is experimented using 23 benchmark datasets, and compared to a set of static rules extracted from literature that are used to determine the number of neurons of SLFN. Moreover, it is compared to two dynamic methods that are used to enhance the performance of ELM, that are Optimally pruned ELM (OP-ELM) and metaheuristic based ELMs (Particle Swarm Optimization-ELM and Differential Evolution-ELM). The obtained results show that the proposed method enhances the generalization performance of ELM and overcomes the static and dynamic methods.},
doi = {10.1007/s13042-020-01073-y},
publisher = {Springer}
}

Faris, H., Heidari, A. A., Ala’M, A., Mafarja, M., Aljarah, I., Eshtay, M., & Mirjalili, S.. (2020). Time-varying hierarchical chains of salps with random weight networks for feature selection. Expert systems with applications, 140, 112898.
[BibTeX]

@article{faris2020time,
title={Time-varying hierarchical chains of salps with random weight networks for feature selection},
author={Faris, Hossam and Heidari, Ali Asghar and Ala’M, Al-Zoubi and Mafarja, Majdi and Aljarah, Ibrahim and Eshtay, Mohammed and Mirjalili, Seyedali},
journal={Expert Systems with Applications},
volume={140},
pages={112898},
year={2020},
publisher={Elsevier}
}

Hassonah, M. A., Al-Sayyed, R., Rodan, A., Al-Zoubi, A. M., Aljarah, I., & Faris, H.. (2019). An efficient hybrid filter and evolutionary wrapper approach for sentiment analysis of various topics on twitter. Knowledge-based systems, 105353. doi:https://doi.org/10.1016/j.knosys.2019.105353
[BibTeX] [Download PDF]

@article{HASSONAH2019105353,
title = "An efficient hybrid filter and evolutionary wrapper approach for sentiment analysis of various topics on Twitter",
journal = "Knowledge-Based Systems",
pages = "105353",
year = "2019",
issn = "0950-7051",
doi = "https://doi.org/10.1016/j.knosys.2019.105353",
url = "http://www.sciencedirect.com/science/article/pii/S0950705119306148",
author = "Mohammad A. Hassonah and Rizik Al-Sayyed and Ali Rodan and Ala’ M. Al-Zoubi and Ibrahim Aljarah and Hossam Faris"
}

Qaddoura, R., Faris, H., & Aljarah, I.. (2020). An efficient clustering algorithm based on the k-nearest neighbors with an indexing ratio. International journal of machine learning and cybernetics, 11, 675–-714. doi:10.1007/s13042-019-01027-z
[BibTeX] [Abstract]

Clustering is a challenging problem that is commonly used for many applications. It aims at finding the similarity between data points and grouping similar ones into the same cluster. In this paper, we introduce a new clustering algorithm named Nearest Point with Indexing Ratio (NPIR). The algorithm tries to solve the clustering problem based on the nearest neighbor search technique by finding the nearest neighbors for the points that are already clustered based on the distance between them and cluster them accordingly. The algorithm does not consider all the nearest points at once to cluster a single point but iteratively considers only one nearest point based on an election operation using a distance vector. NPIR tries to solve some limitations of other clustering algorithms. It tries to cluster arbitrary shapes which have non-spherical clusters, clusters with unusual shapes, or clusters with different densities. NPIR is evaluated using 20 real and artificial data sets of different levels of complexity with different number of clusters and points. Results are compared with those obtained for other well-known and common clustering algorithms. The comparative study demonstrates that NPIR outperforms the other algorithms for the majority of the data sets in terms of different evaluation measures including Homogeneity Score, Completeness Score, V-measure, Adjusted Mutual Information, and Adjusted Rand Index. Furthermore, NPIR is experimented on a real-life application for segmenting mall customers for effective decision making. The source code of NPIR is available at http://evo-ml.com/2019/10/28/npir/.

@Article{qaddoura2019efficient,
author = {Qaddoura, Raneem and Faris, Hossam and Aljarah, Ibrahim},
title = {An efficient clustering algorithm based on the k-nearest neighbors with an indexing ratio},
journal = {International Journal of Machine Learning and Cybernetics},
year = {2020},
pages = {675–-714},
volume = {11},
abstract = {Clustering is a challenging problem that is commonly used for many applications. It aims at finding the similarity between data points and grouping similar ones into the same cluster. In this paper, we introduce a new clustering algorithm named Nearest Point with Indexing Ratio (NPIR). The algorithm tries to solve the clustering problem based on the nearest neighbor search technique by finding the nearest neighbors for the points that are already clustered based on the distance between them and cluster them accordingly. The algorithm does not consider all the nearest points at once to cluster a single point but iteratively considers only one nearest point based on an election operation using a distance vector. NPIR tries to solve some limitations of other clustering algorithms. It tries to cluster arbitrary shapes which have non-spherical clusters, clusters with unusual shapes, or clusters with different densities. NPIR is evaluated using 20 real and artificial data sets of different levels of complexity with different number of clusters and points. Results are compared with those obtained for other well-known and common clustering algorithms. The comparative study demonstrates that NPIR outperforms the other algorithms for the majority of the data sets in terms of different evaluation measures including Homogeneity Score, Completeness Score, V-measure, Adjusted Mutual Information, and Adjusted Rand Index. Furthermore, NPIR is experimented on a real-life application for segmenting mall customers for effective decision making. The source code of NPIR is available at http://evo-ml.com/2019/10/28/npir/.},
doi = {10.1007/s13042-019-01027-z},
publisher = {Springer}
}

Al-Betar, M. A., Aljarah, I., Awadallah, M. A., Faris, H., & Mirjalili, S.. (2019). Adaptive β- hill climbing for optimization. Soft computing. doi:10.1007/s00500-019-03887-7
[BibTeX] [Abstract]

In this paper, an adaptive version of β- hill climbing is proposed. In the original β- hill climbing, two control parameters are utilized to strike the right balance between a local-nearby exploitation and a global wide-range exploration during the search: N and β, respectively. Conventionally, these two parameters require an intensive study to find their suitable values. In order to yield an easy-to-use optimization method, this paper proposes an efficient adaptive strategy for these two parameters in a deterministic way. The proposed adaptive method is evaluated against 23 global optimization functions. The selectivity analysis to determine the optimal progressing values of N and β during the search is carried out. Furthermore, the behavior of the adaptive version is analyzed based on various problems with different complexity levels. For comparative evaluation, the adaptive version is initially compared with the original one as well as with other local search-based methods and other well-regarded methods using the same benchmark functions. Interestingly, the results produced are very competitive with the other methods. In a nutshell, the proposed adaptive β- hill climbing is able to achieve the best results on 10 out of 23 test functions. For more validation, the test functions established in IEEE-CEC2015 are used with various scaling values. The comparative results show the viability of the proposed adaptive method. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

@Article{Al-Betar2019,
author = {Al-Betar, M.A. and Aljarah, I. and Awadallah, M.A. and Faris, H. and Mirjalili, S.},
title = {Adaptive β- hill climbing for optimization},
journal = {Soft Computing},
year = {2019},
issn = {14327643},
abstract = {In this paper, an adaptive version of β- hill climbing is proposed. In the original β- hill climbing, two control parameters are utilized to strike the right balance between a local-nearby exploitation and a global wide-range exploration during the search: N and β, respectively. Conventionally, these two parameters require an intensive study to find their suitable values. In order to yield an easy-to-use optimization method, this paper proposes an efficient adaptive strategy for these two parameters in a deterministic way. The proposed adaptive method is evaluated against 23 global optimization functions. The selectivity analysis to determine the optimal progressing values of N and β during the search is carried out. Furthermore, the behavior of the adaptive version is analyzed based on various problems with different complexity levels. For comparative evaluation, the adaptive version is initially compared with the original one as well as with other local search-based methods and other well-regarded methods using the same benchmark functions. Interestingly, the results produced are very competitive with the other methods. In a nutshell, the proposed adaptive β- hill climbing is able to achieve the best results on 10 out of 23 test functions. For more validation, the test functions established in IEEE-CEC2015 are used with various scaling values. The comparative results show the viability of the proposed adaptive method. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.},
document_type = {Article},
doi = {10.1007/s00500-019-03887-7},
publisher = {Springer Verlag},
source = {Scopus},
}

Aljarah, I., Faris, H., Mirjalili, S., Al-Madi, N., Sheta, A., & Mafarja, M.. (2019). Evolving neural networks using bird swarm algorithm for data classification and regression applications. Cluster computing. doi:10.1007/s10586-019-02913-5
[BibTeX] [Abstract]

This work proposes a new evolutionary multilayer perceptron neural networks using the recently proposed Bird Swarm Algorithm. The problem of finding the optimal connection weights and neuron biases is first formulated as a minimization problem with mean square error as the objective function. The BSA is then used to estimate the global optimum for this problem. A comprehensive comparative study is conducted using 13 classification datasets, three function approximation datasets, and one real-world case study (Tennessee Eastman chemical reactor problem) to benchmark the performance of the proposed evolutionary neural network. The results are compared with well-regarded conventional and evolutionary trainers and show that the proposed method provides very competitive results. The paper also considers a deep analysis of the results, revealing the flexibility, robustness, and reliability of the proposed trainer when applied to different datasets. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

@Article{Aljarah2019,
author = {Aljarah, I. and Faris, H. and Mirjalili, S. and Al-Madi, N. and Sheta, A. and Mafarja, M.},
title = {Evolving neural networks using bird swarm algorithm for data classification and regression applications},
journal = {Cluster Computing},
year = {2019},
issn = {13867857},
abstract = {This work proposes a new evolutionary multilayer perceptron neural networks using the recently proposed Bird Swarm Algorithm. The problem of finding the optimal connection weights and neuron biases is first formulated as a minimization problem with mean square error as the objective function. The BSA is then used to estimate the global optimum for this problem. A comprehensive comparative study is conducted using 13 classification datasets, three function approximation datasets, and one real-world case study (Tennessee Eastman chemical reactor problem) to benchmark the performance of the proposed evolutionary neural network. The results are compared with well-regarded conventional and evolutionary trainers and show that the proposed method provides very competitive results. The paper also considers a deep analysis of the results, revealing the flexibility, robustness, and reliability of the proposed trainer when applied to different datasets. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.},
document_type = {Article},
doi = {10.1007/s10586-019-02913-5},
publisher = {Springer New York LLC},
source = {Scopus},
}

Aljarah, I., Mafarja, M., Heidari, A. A., Faris, H., & Mirjalili, S.. (2019). Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach. Knowledge and information systems, 1–33. doi:10.1007/s10115-019-01358-x
[BibTeX] [Abstract]

Grey wolf optimizer (GWO) is known as one of the recent popular metaheuristic algorithms inspired from the social collaboration and team hunting activities of grey wolves in nature. This algorithm benefits from stochastic operators, but it is still prone to stagnation in local optima and premature convergence when solving problems with a large number of variables (e.g., clustering problems). To alleviate this shortcoming, the GWO algorithm is hybridized with the well-known tabu search (TS). To investigate the performance of the proposed hybrid GWO and TS (GWOTS), it is compared with well-regarded metaheuristics on various clustering datasets. The comprehensive experiments and analysis verify that the proposed GWOTS shows an improved performance compared to GWO and can be utilized for clustering applications.

@article{aljarah2019clustering,
title={Clustering analysis using a novel locality-informed grey wolf-inspired clustering approach},
author={Aljarah, Ibrahim and Mafarja, Majdi and Heidari, Ali Asghar and Faris, Hossam and Mirjalili, Seyedali},
journal={Knowledge and Information Systems},
pages={1--33},
abstract = {Grey wolf optimizer (GWO) is known as one of the recent popular metaheuristic algorithms inspired from the social collaboration and team hunting activities of grey wolves in nature. This algorithm benefits from stochastic operators, but it is still prone to stagnation in local optima and premature convergence when solving problems with a large number of variables (e.g., clustering problems). To alleviate this shortcoming, the GWO algorithm is hybridized with the well-known tabu search (TS). To investigate the performance of the proposed hybrid GWO and TS (GWOTS), it is compared with well-regarded metaheuristics on various clustering datasets. The comprehensive experiments and analysis verify that the proposed GWOTS shows an improved performance compared to GWO and can be utilized for clustering applications.},
doi = {10.1007/s10115-019-01358-x},
year={2019},
publisher={Springer}
}

Faris, H., Al-Zoubi, A. M., Heidari, A. A., Aljarah, I., Mafarja, M., Hassonah, M. A., & Fujita, H.. (2019). An intelligent system for spam detection and identification of the most relevant features based on evolutionary random weight networks. Information fusion, 48, 67-83. doi:10.1016/j.inffus.2018.08.002
[BibTeX] [Abstract]

With the incremental use of emails as an essential and popular communication mean over the Internet, there comes a serious threat that impacts the Internet and the society. This problem is known as spam. By receiving spam messages, Internet users are exposed to security issues, and minors are exposed to inappropriate contents. Moreover, spam messages waste resources in terms of storage, bandwidth, and productivity. What makes the problem worse is that spammers keep inventing new techniques to dodge spam filters. On the other side, the massive data flow of hundreds of millions of individuals, and the large number of attributes make the problem more cumbersome and complex. Therefore, proposing evolutionary and adaptable spam detection models becomes a necessity. In this paper, an intelligent detection system that is based on Genetic Algorithm (GA) and Random Weight Network (RWN) is proposed to deal with email spam detection tasks. In addition, an automatic identification capability is also embedded in the proposed system to detect the most relevant features during the detection process. The proposed system is intensively evaluated through a series of extensive experiments based on three email corpora. The experimental results confirm that the proposed system can achieve remarkable results in terms of accuracy, precision, and recall. Furthermore, the proposed detection system can automatically identify the most relevant features of the spam emails. © 2018 Elsevier B.V.

@Article{Faris2019,
author = {Faris, H. and Al-Zoubi, A.M. and Heidari, A.A. and Aljarah, I. and Mafarja, M. and Hassonah, M.A. and Fujita, H.},
title = {An intelligent system for spam detection and identification of the most relevant features based on evolutionary Random Weight Networks},
journal = {Information Fusion},
year = {2019},
volume = {48},
pages = {67-83},
issn = {15662535},
abstract = {With the incremental use of emails as an essential and popular communication mean over the Internet, there comes a serious threat that impacts the Internet and the society. This problem is known as spam. By receiving spam messages, Internet users are exposed to security issues, and minors are exposed to inappropriate contents. Moreover, spam messages waste resources in terms of storage, bandwidth, and productivity. What makes the problem worse is that spammers keep inventing new techniques to dodge spam filters. On the other side, the massive data flow of hundreds of millions of individuals, and the large number of attributes make the problem more cumbersome and complex. Therefore, proposing evolutionary and adaptable spam detection models becomes a necessity. In this paper, an intelligent detection system that is based on Genetic Algorithm (GA) and Random Weight Network (RWN) is proposed to deal with email spam detection tasks. In addition, an automatic identification capability is also embedded in the proposed system to detect the most relevant features during the detection process. The proposed system is intensively evaluated through a series of extensive experiments based on three email corpora. The experimental results confirm that the proposed system can achieve remarkable results in terms of accuracy, precision, and recall. Furthermore, the proposed detection system can automatically identify the most relevant features of the spam emails. © 2018 Elsevier B.V.},
document_type = {Article},
doi = {10.1016/j.inffus.2018.08.002},
publisher = {Elsevier B.V.},
source = {Scopus},
}

With the incremental use of emails as an essential and popular communication mean over the Internet, there comes a serious threat that impacts the Internet and the society. This problem is known as spam. By receiving spam messages, Internet users are exposed to security issues, and minors are exposed to inappropriate contents. Moreover, spam messages waste resources in terms of storage, bandwidth, and productivity. What makes the problem worse is that spammers keep inventing new techniques to dodge spam filters. On the other side, the massive data flow of hundreds of millions of individuals, and the large number of attributes make the problem more cumbersome and complex. Therefore, proposing evolutionary and adaptable spam detection models becomes a necessity. In this paper, an intelligent detection system that is based on Genetic Algorithm (GA) and Random Weight Network (RWN) is proposed to deal with email spam detection tasks. In addition, an automatic identification capability is also embedded in the proposed system to detect the most relevant features during the detection process. The proposed system is intensively evaluated through a series of extensive experiments based on three email corpora. The experimental results confirm that the proposed system can achieve remarkable results in terms of accuracy, precision, and recall. Furthermore, the proposed detection system can automatically identify the most relevant features of the spam emails. © 2018 Elsevier B.V.

@Article{Faris201967,
author = {Faris, H. and Al-Zoubi, A.M. and Heidari, A.A. and Aljarah, I. and Mafarja, M. and Hassonah, M.A. and Fujita, H.},
title = {An intelligent system for spam detection and identification of the most relevant features based on evolutionary Random Weight Networks},
journal = {Information Fusion},
year = {2019},
volume = {48},
pages = {67-83},
issn = {15662535},
abstract = {With the incremental use of emails as an essential and popular communication mean over the Internet, there comes a serious threat that impacts the Internet and the society. This problem is known as spam. By receiving spam messages, Internet users are exposed to security issues, and minors are exposed to inappropriate contents. Moreover, spam messages waste resources in terms of storage, bandwidth, and productivity. What makes the problem worse is that spammers keep inventing new techniques to dodge spam filters. On the other side, the massive data flow of hundreds of millions of individuals, and the large number of attributes make the problem more cumbersome and complex. Therefore, proposing evolutionary and adaptable spam detection models becomes a necessity. In this paper, an intelligent detection system that is based on Genetic Algorithm (GA) and Random Weight Network (RWN) is proposed to deal with email spam detection tasks. In addition, an automatic identification capability is also embedded in the proposed system to detect the most relevant features during the detection process. The proposed system is intensively evaluated through a series of extensive experiments based on three email corpora. The experimental results confirm that the proposed system can achieve remarkable results in terms of accuracy, precision, and recall. Furthermore, the proposed detection system can automatically identify the most relevant features of the spam emails. © 2018 Elsevier B.V.},
document_type = {Article},
doi = {10.1016/j.inffus.2018.08.002},
publisher = {Elsevier B.V.},
source = {Scopus},
}

Heidari, A. A., Aljarah, I., Faris, H., Chen, H., Luo, J., & Mirjalili, S.. (2019). An enhanced associative learning-based exploratory whale optimizer for global optimization. Neural computing and applications. doi:10.1007/s00521-019-04015-0
[BibTeX] [Abstract]

Whale optimization algorithm (WOA) is a recent nature-inspired metaheuristic that mimics the cooperative life of humpback whales and their spiral-shaped hunting mechanism. In this research, it is first argued that the exploitation tendency of WOA is limited and can be considered as one of the main drawbacks of this algorithm. In order to mitigate the problems of immature convergence and stagnation problems, the exploitative and exploratory capabilities of modified WOA in conjunction with a learning mechanism are improved. In this regard, the proposed WOA with associative learning approaches is combined with a recent variant of hill climbing local search to further enhance the exploitation process. The improved algorithm is then employed to tackle a wide range of numerical optimization problems. The results are compared with different well-known and novel techniques on multi-dimensional classic problems and new CEC 2017 test suite. The extensive experiments and statistical tests show the superiority of the proposed BMWOA compared to WOA and several well-established algorithms. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

@Article{Heidari2019,
author = {Heidari, A.A. and Aljarah, I. and Faris, H. and Chen, H. and Luo, J. and Mirjalili, S.},
title = {An enhanced associative learning-based exploratory whale optimizer for global optimization},
journal = {Neural Computing and Applications},
year = {2019},
issn = {09410643},
abstract = {Whale optimization algorithm (WOA) is a recent nature-inspired metaheuristic that mimics the cooperative life of humpback whales and their spiral-shaped hunting mechanism. In this research, it is first argued that the exploitation tendency of WOA is limited and can be considered as one of the main drawbacks of this algorithm. In order to mitigate the problems of immature convergence and stagnation problems, the exploitative and exploratory capabilities of modified WOA in conjunction with a learning mechanism are improved. In this regard, the proposed WOA with associative learning approaches is combined with a recent variant of hill climbing local search to further enhance the exploitation process. The improved algorithm is then employed to tackle a wide range of numerical optimization problems. The results are compared with different well-known and novel techniques on multi-dimensional classic problems and new CEC 2017 test suite. The extensive experiments and statistical tests show the superiority of the proposed BMWOA compared to WOA and several well-established algorithms. © 2019, Springer-Verlag London Ltd., part of Springer Nature.},
document_type = {Article},
doi = {10.1007/s00521-019-04015-0},
publisher = {Springer London},
source = {Scopus},
}

Mafarja, M., Aljarah, I., Faris, H., Hammouri, A. I., Al-Zoubi, A. M., & Mirjalili, S.. (2019). Binary grasshopper optimisation algorithm approaches for feature selection problems. Expert systems with applications, 117, 267-286. doi:10.1016/j.eswa.2018.09.015
[BibTeX] [Abstract]

Feature Selection (FS) is a challenging machine learning-related task that aims at reducing the number of features by removing irrelevant, redundant and noisy data while maintaining an acceptable level of classification accuracy. FS can be considered as an optimisation problem. Due to the difficulty of this problem and having a large number of local solutions, stochastic optimisation algorithms are promising techniques to solve this problem. As a seminal attempt, binary variants of the recent Grasshopper Optimisation Algorithm (GOA) are proposed in this work and employed to select the optimal feature subset for classification purposes within a wrapper-based framework. Two mechanisms are employed to design a binary GOA, the first one is based on Sigmoid and V-shaped transfer functions, and will be indicated by BGOA-S and BGOA-V, respectively. While the second mechanism uses a novel technique that combines the best solution obtained so far. In addition, a mutation operator is employed to enhance the exploration phase in BGOA algorithm (BGOA-M). The proposed methods are evaluated using 25 standard UCI datasets and compared with 8 well-regarded metaheuristic wrapper-based approaches, and six well known filter-based (e.g., correlation FS) approaches. The comparative results show the superior performance of the BGOA and BGOA-M methods compared to other similar techniques in the literature. © 2018 Elsevier Ltd

@Article{Mafarja2019,
author = {Mafarja, M. and Aljarah, I. and Faris, H. and Hammouri, A.I. and Al-Zoubi, A.M. and Mirjalili, S.},
title = {Binary grasshopper optimisation algorithm approaches for feature selection problems},
journal = {Expert Systems with Applications},
year = {2019},
volume = {117},
pages = {267-286},
issn = {09574174},
abstract = {Feature Selection (FS) is a challenging machine learning-related task that aims at reducing the number of features by removing irrelevant, redundant and noisy data while maintaining an acceptable level of classification accuracy. FS can be considered as an optimisation problem. Due to the difficulty of this problem and having a large number of local solutions, stochastic optimisation algorithms are promising techniques to solve this problem. As a seminal attempt, binary variants of the recent Grasshopper Optimisation Algorithm (GOA) are proposed in this work and employed to select the optimal feature subset for classification purposes within a wrapper-based framework. Two mechanisms are employed to design a binary GOA, the first one is based on Sigmoid and V-shaped transfer functions, and will be indicated by BGOA-S and BGOA-V, respectively. While the second mechanism uses a novel technique that combines the best solution obtained so far. In addition, a mutation operator is employed to enhance the exploration phase in BGOA algorithm (BGOA-M). The proposed methods are evaluated using 25 standard UCI datasets and compared with 8 well-regarded metaheuristic wrapper-based approaches, and six well known filter-based (e.g., correlation FS) approaches. The comparative results show the superior performance of the BGOA and BGOA-M methods compared to other similar techniques in the literature. © 2018 Elsevier Ltd},
coden = {ESAPE},
document_type = {Article},
doi = {10.1016/j.eswa.2018.09.015},
publisher = {Elsevier Ltd},
source = {Scopus},
}

Feature Selection (FS) is a challenging machine learning-related task that aims at reducing the number of features by removing irrelevant, redundant and noisy data while maintaining an acceptable level of classification accuracy. FS can be considered as an optimisation problem. Due to the difficulty of this problem and having a large number of local solutions, stochastic optimisation algorithms are promising techniques to solve this problem. As a seminal attempt, binary variants of the recent Grasshopper Optimisation Algorithm (GOA) are proposed in this work and employed to select the optimal feature subset for classification purposes within a wrapper-based framework. Two mechanisms are employed to design a binary GOA, the first one is based on Sigmoid and V-shaped transfer functions, and will be indicated by BGOA-S and BGOA-V, respectively. While the second mechanism uses a novel technique that combines the best solution obtained so far. In addition, a mutation operator is employed to enhance the exploration phase in BGOA algorithm (BGOA-M). The proposed methods are evaluated using 25 standard UCI datasets and compared with 8 well-regarded metaheuristic wrapper-based approaches, and six well known filter-based (e.g., correlation FS) approaches. The comparative results show the superior performance of the BGOA and BGOA-M methods compared to other similar techniques in the literature. © 2018 Elsevier Ltd

@Article{Mafarja2019267,
author = {Mafarja, M. and Aljarah, I. and Faris, H. and Hammouri, A.I. and Al-Zoubi, A.M. and Mirjalili, S.},
title = {Binary grasshopper optimisation algorithm approaches for feature selection problems},
journal = {Expert Systems with Applications},
year = {2019},
volume = {117},
pages = {267-286},
issn = {09574174},
abstract = {Feature Selection (FS) is a challenging machine learning-related task that aims at reducing the number of features by removing irrelevant, redundant and noisy data while maintaining an acceptable level of classification accuracy. FS can be considered as an optimisation problem. Due to the difficulty of this problem and having a large number of local solutions, stochastic optimisation algorithms are promising techniques to solve this problem. As a seminal attempt, binary variants of the recent Grasshopper Optimisation Algorithm (GOA) are proposed in this work and employed to select the optimal feature subset for classification purposes within a wrapper-based framework. Two mechanisms are employed to design a binary GOA, the first one is based on Sigmoid and V-shaped transfer functions, and will be indicated by BGOA-S and BGOA-V, respectively. While the second mechanism uses a novel technique that combines the best solution obtained so far. In addition, a mutation operator is employed to enhance the exploration phase in BGOA algorithm (BGOA-M). The proposed methods are evaluated using 25 standard UCI datasets and compared with 8 well-regarded metaheuristic wrapper-based approaches, and six well known filter-based (e.g., correlation FS) approaches. The comparative results show the superior performance of the BGOA and BGOA-M methods compared to other similar techniques in the literature. © 2018 Elsevier Ltd},
coden = {ESAPE},
document_type = {Article},
doi = {10.1016/j.eswa.2018.09.015},
publisher = {Elsevier Ltd},
source = {Scopus},
}

AlAgha, A. S., Faris, H., Hammo, B. H., & Al-Zoubi, A. M.. (2018). Identifying β-thalassemia carriers using a data mining approach: the case of the gaza strip, palestine. Artificial intelligence in medicine, 88, 70-83. doi:10.1016/j.artmed.2018.04.009
[BibTeX] [Abstract]

Thalassemia is considered one of the most common genetic blood disorders that has received excessive attention in the medical research fields worldwide. Under this context, one of the greatest challenges for healthcare professionals is to correctly differentiate normal individuals from asymptomatic thalassemia carriers. Usually, thalassemia diagnosis is based on certain measurable characteristic changes to blood cell counts and related indices. These characteristic changes can be derived easily when performing a complete blood count test (CBC) using a special fully automated blood analyzer or counter. However, the reliability of the CBC test alone is questionable with possible candidate characteristics that could be seen in other disorders, leading to misdiagnosis of thalassemia. Therefore, other costly and time-consuming tests should be performed that may cause serious consequences due to the delay in the correct diagnosis. To help overcoming these challenging diagnostic issues, this work presents a new novel dataset collected from Palestine Avenir Foundation for persons tested for thalassemia. We aim to compile a gold standard dataset for thalassemia and make it available for researchers in this field. Moreover, we use this dataset to predict the specific type of thalassemia known as beta thalassemia (β-thalassemia) based on hybrid data mining model. The proposed model consists of two main steps. First, to overcome the problem of the highly imbalanced class distribution in the dataset, a balancing technique called SMOTE is proposed and applied to handle this problem. In the second step, four classification models, namely k-nearest neighbors (k-NN), naïve Bayesian (NB), decision tree (DT) and the multilayer perceptron (MLP) neural network are used to differentiate between normal persons and those patients carrying β-thalassemia. Different evaluation metrics are used to assess the performance of the proposed model. The experimental results show that the SMOTE oversampling method can effectively improve the identification ratio of β-thalassemia carriers in a highly imbalanced class distribution. The results reveal also that the NB classifier achieved the best performance in differentiating between normal and β-thalassemia carriers at oversampling SMOTE ratio of 400%. This combination shows a specificity of 99.47% and a sensitivity of 98.81%. © 2018 Elsevier B.V.

@Article{AlAgha201870,
author = {AlAgha, A.S. and Faris, H. and Hammo, B.H. and Al-Zoubi, A.M.},
title = {Identifying β-thalassemia carriers using a data mining approach: The case of the Gaza Strip, Palestine},
journal = {Artificial Intelligence in Medicine},
year = {2018},
volume = {88},
pages = {70-83},
issn = {09333657},
abstract = {Thalassemia is considered one of the most common genetic blood disorders that has received excessive attention in the medical research fields worldwide. Under this context, one of the greatest challenges for healthcare professionals is to correctly differentiate normal individuals from asymptomatic thalassemia carriers. Usually, thalassemia diagnosis is based on certain measurable characteristic changes to blood cell counts and related indices. These characteristic changes can be derived easily when performing a complete blood count test (CBC) using a special fully automated blood analyzer or counter. However, the reliability of the CBC test alone is questionable with possible candidate characteristics that could be seen in other disorders, leading to misdiagnosis of thalassemia. Therefore, other costly and time-consuming tests should be performed that may cause serious consequences due to the delay in the correct diagnosis. To help overcoming these challenging diagnostic issues, this work presents a new novel dataset collected from Palestine Avenir Foundation for persons tested for thalassemia. We aim to compile a gold standard dataset for thalassemia and make it available for researchers in this field. Moreover, we use this dataset to predict the specific type of thalassemia known as beta thalassemia (β-thalassemia) based on hybrid data mining model. The proposed model consists of two main steps. First, to overcome the problem of the highly imbalanced class distribution in the dataset, a balancing technique called SMOTE is proposed and applied to handle this problem. In the second step, four classification models, namely k-nearest neighbors (k-NN), naïve Bayesian (NB), decision tree (DT) and the multilayer perceptron (MLP) neural network are used to differentiate between normal persons and those patients carrying β-thalassemia. Different evaluation metrics are used to assess the performance of the proposed model. The experimental results show that the SMOTE oversampling method can effectively improve the identification ratio of β-thalassemia carriers in a highly imbalanced class distribution. The results reveal also that the NB classifier achieved the best performance in differentiating between normal and β-thalassemia carriers at oversampling SMOTE ratio of 400%. This combination shows a specificity of 99.47% and a sensitivity of 98.81%. © 2018 Elsevier B.V.},
coden = {AIMEE},
document_type = {Article},
doi = {10.1016/j.artmed.2018.04.009},
publisher = {Elsevier B.V.},
source = {Scopus},
}

Al-Betar, M. A., Awadallah, M. A., Faris, H., Aljarah, I., & Hammouri, A. I.. (2018). Natural selection methods for grey wolf optimizer. Expert systems with applications, 113, 481-498. doi:10.1016/j.eswa.2018.07.022
[BibTeX] [Abstract]

The selection process is the most attractive operator in the optimization algorithms. It normally mimics the natural selection of survival of the fittest principle. When the selection is too greedy, the selection pressure will be high and therefore the search becomes biased toward exploitation. In contrast, when the selection has a tendency to be random, the selection pressure will be low and thus the exploration is observed more. The selection process in Grey Wolf Optimizer (GWO) tends to be too greedy since the search is driven by the three best solutions. In this paper, different selection methods extracted from other evolutionary algorithms (EAs) are investigated for GWO. Along with the original selection method of GWO called greedy-based selection method, five others selection methods which are proportional, tournament, universal sampling, linear rank, and random selection methods are investigated. Accordingly, six versions of GWO are proposed which are Greedy-based GWO (GGWO), Proportional-based GWO (PGWO), Tournament-based GWO (TGWO), Universal sampling-based GWO (UGWO), Linear rank-based GWO (LGWO), Random-based GWO (RGWO). The six versions are evaluated using 23 test functions circulated in the literature with different characteristics and complexity. The sensitivity analysis of the control parameters of some new versions are discussed. Interestingly, TGWO achieved the best results for almost all benchmark functions. This proves that the selection methods have a high impact on the performance of GWO. © 2018 Elsevier Ltd

@Article{Al-Betar2018481,
author = {Al-Betar, M.A. and Awadallah, M.A. and Faris, H. and Aljarah, I. and Hammouri, A.I.},
title = {Natural selection methods for Grey Wolf Optimizer},
journal = {Expert Systems with Applications},
year = {2018},
volume = {113},
pages = {481-498},
issn = {09574174},
abstract = {The selection process is the most attractive operator in the optimization algorithms. It normally mimics the natural selection of survival of the fittest principle. When the selection is too greedy, the selection pressure will be high and therefore the search becomes biased toward exploitation. In contrast, when the selection has a tendency to be random, the selection pressure will be low and thus the exploration is observed more. The selection process in Grey Wolf Optimizer (GWO) tends to be too greedy since the search is driven by the three best solutions. In this paper, different selection methods extracted from other evolutionary algorithms (EAs) are investigated for GWO. Along with the original selection method of GWO called greedy-based selection method, five others selection methods which are proportional, tournament, universal sampling, linear rank, and random selection methods are investigated. Accordingly, six versions of GWO are proposed which are Greedy-based GWO (GGWO), Proportional-based GWO (PGWO), Tournament-based GWO (TGWO), Universal sampling-based GWO (UGWO), Linear rank-based GWO (LGWO), Random-based GWO (RGWO). The six versions are evaluated using 23 test functions circulated in the literature with different characteristics and complexity. The sensitivity analysis of the control parameters of some new versions are discussed. Interestingly, TGWO achieved the best results for almost all benchmark functions. This proves that the selection methods have a high impact on the performance of GWO. © 2018 Elsevier Ltd},
coden = {ESAPE},
document_type = {Article},
doi = {10.1016/j.eswa.2018.07.022},
publisher = {Elsevier Ltd},
source = {Scopus},
}

Al-Betar, M. A., Awadallah, M. A., Faris, H., Yang, X. -S., Tajudin Khader, A., & Alomari, O. A.. (2018). Bat-inspired algorithms with natural selection mechanisms for global optimization. Neurocomputing, 273, 448-465. doi:10.1016/j.neucom.2017.07.039
[BibTeX] [Abstract]

In this paper, alternative selection mechanisms in the bat-inspired algorithm for global optimization problems are studied. The bat-inspired algorithm is a recent swarm-based intelligent system which mimics the echolocation system of micro-bats. In the bat-inspired algorithm, the bats randomly fly around the best bat locations found during the search so as to improve their hunting of prey. In practice, one bat location from a set of best bat locations is selected. Thereafter, that best bat location is used by local search with a random walk strategy to inform other bats about the prey location. This selection mechanism can be improved using other natural selection mechanisms adopted from other advanced algorithms like Genetic Algorithm. Therefore, six selection mechanisms are studied to choose the best bat location: global-best, tournament, proportional, linear rank, exponential rank, and random. Consequently, six versions of bat-inspired algorithm are proposed and studied which are global-best bat-inspired algorithm (GBA), tournament bat-inspired algorithm (TBA), proportional bat-inspired algorithm (PBA), linear rank bat-inspired algorithm (LBA), exponential rank bat-inspired algorithm (EBA), and random bat-inspired algorithm (RBA). Using two sets of global optimization functions, the bat-inspired versions are evaluated and the sensitivity analyses of each version to its parameters studied. Our results suggest that there are positive effects of the selection mechanisms on the performance of the classical bat-inspired algorithm which is GBA. For comparative evaluation, eighteen methods are selected using 25 IEEE-CEC2005 functions. The results show that the bat-inspired versions with various selection schemes observing the “survival-of-the-fittest” principle are largely competitive to established methods. © 2017 Elsevier B.V.

@Article{Al-Betar2018448,
author = {Al-Betar, M.A. and Awadallah, M.A. and Faris, H. and Yang, X.-S. and Tajudin Khader, A. and Alomari, O.A.},
title = {Bat-inspired algorithms with natural selection mechanisms for global optimization},
journal = {Neurocomputing},
year = {2018},
volume = {273},
pages = {448-465},
issn = {09252312},
abstract = {In this paper, alternative selection mechanisms in the bat-inspired algorithm for global optimization problems are studied. The bat-inspired algorithm is a recent swarm-based intelligent system which mimics the echolocation system of micro-bats. In the bat-inspired algorithm, the bats randomly fly around the best bat locations found during the search so as to improve their hunting of prey. In practice, one bat location from a set of best bat locations is selected. Thereafter, that best bat location is used by local search with a random walk strategy to inform other bats about the prey location. This selection mechanism can be improved using other natural selection mechanisms adopted from other advanced algorithms like Genetic Algorithm. Therefore, six selection mechanisms are studied to choose the best bat location: global-best, tournament, proportional, linear rank, exponential rank, and random. Consequently, six versions of bat-inspired algorithm are proposed and studied which are global-best bat-inspired algorithm (GBA), tournament bat-inspired algorithm (TBA), proportional bat-inspired algorithm (PBA), linear rank bat-inspired algorithm (LBA), exponential rank bat-inspired algorithm (EBA), and random bat-inspired algorithm (RBA). Using two sets of global optimization functions, the bat-inspired versions are evaluated and the sensitivity analyses of each version to its parameters studied. Our results suggest that there are positive effects of the selection mechanisms on the performance of the classical bat-inspired algorithm which is GBA. For comparative evaluation, eighteen methods are selected using 25 IEEE-CEC2005 functions. The results show that the bat-inspired versions with various selection schemes observing the “survival-of-the-fittest” principle are largely competitive to established methods. © 2017 Elsevier B.V.},
coden = {NRCGE},
document_type = {Article},
doi = {10.1016/j.neucom.2017.07.039},
publisher = {Elsevier B.V.},
source = {Scopus},
}

Aljarah, I., Al-Zoubi, A. M., Faris, H., Hassonah, M. A., Mirjalili, S., & Saadeh, H.. (2018). Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm. Cognitive computation, 10(3), 478-495. doi:10.1007/s12559-017-9542-9
[BibTeX] [Abstract]

Support vector machine (SVM) is considered to be one of the most powerful learning algorithms and is used for a wide range of real-world applications. The efficiency of SVM algorithm and its performance mainly depends on the kernel type and its parameters. Furthermore, the feature subset selection that is used to train the SVM model is another important factor that has a major influence on it classification accuracy. The feature subset selection is a very important step in machine learning, specially when dealing with high-dimensional data sets. Most of the previous researches handled these important factors separately. In this paper, we propose a hybrid approach based on the Grasshopper optimisation algorithm (GOA), which is a recent algorithm inspired by the biological behavior shown in swarms of grasshoppers. The goal of the proposed approach is to optimize the parameters of the SVM model, and locate the best features subset simultaneously. Eighteen low- and high-dimensional benchmark data sets are used to evaluate the accuracy of the proposed approach. For verification, the proposed approach is compared with seven well-regarded algorithms. Furthermore, the proposed approach is compared with grid search, which is the most popular technique for tuning SVM parameters. The experimental results show that the proposed approach outperforms all of the other techniques in most of the data sets in terms of classification accuracy, while minimizing the number of selected features. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

@Article{Aljarah2018478,
author = {Aljarah, I. and Al-Zoubi, A.M. and Faris, H. and Hassonah, M.A. and Mirjalili, S. and Saadeh, H.},
title = {Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm},
journal = {Cognitive Computation},
year = {2018},
volume = {10},
number = {3},
pages = {478-495},
issn = {18669956},
abstract = {Support vector machine (SVM) is considered to be one of the most powerful learning algorithms and is used for a wide range of real-world applications. The efficiency of SVM algorithm and its performance mainly depends on the kernel type and its parameters. Furthermore, the feature subset selection that is used to train the SVM model is another important factor that has a major influence on it classification accuracy. The feature subset selection is a very important step in machine learning, specially when dealing with high-dimensional data sets. Most of the previous researches handled these important factors separately. In this paper, we propose a hybrid approach based on the Grasshopper optimisation algorithm (GOA), which is a recent algorithm inspired by the biological behavior shown in swarms of grasshoppers. The goal of the proposed approach is to optimize the parameters of the SVM model, and locate the best features subset simultaneously. Eighteen low- and high-dimensional benchmark data sets are used to evaluate the accuracy of the proposed approach. For verification, the proposed approach is compared with seven well-regarded algorithms. Furthermore, the proposed approach is compared with grid search, which is the most popular technique for tuning SVM parameters. The experimental results show that the proposed approach outperforms all of the other techniques in most of the data sets in terms of classification accuracy, while minimizing the number of selected features. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.},
document_type = {Article},
doi = {10.1007/s12559-017-9542-9},
publisher = {Springer New York LLC},
source = {Scopus},
}

Support vector machine (SVM) is considered to be one of the most powerful learning algorithms and is used for a wide range of real-world applications. The efficiency of SVM algorithm and its performance mainly depends on the kernel type and its parameters. Furthermore, the feature subset selection that is used to train the SVM model is another important factor that has a major influence on it classification accuracy. The feature subset selection is a very important step in machine learning, specially when dealing with high-dimensional data sets. Most of the previous researches handled these important factors separately. In this paper, we propose a hybrid approach based on the Grasshopper optimisation algorithm (GOA), which is a recent algorithm inspired by the biological behavior shown in swarms of grasshoppers. The goal of the proposed approach is to optimize the parameters of the SVM model, and locate the best features subset simultaneously. Eighteen low- and high-dimensional benchmark data sets are used to evaluate the accuracy of the proposed approach. For verification, the proposed approach is compared with seven well-regarded algorithms. Furthermore, the proposed approach is compared with grid search, which is the most popular technique for tuning SVM parameters. The experimental results show that the proposed approach outperforms all of the other techniques in most of the data sets in terms of classification accuracy, while minimizing the number of selected features. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

@Article{Aljarah2018a,
author = {Aljarah, I. and Al-Zoubi, A.M. and Faris, H. and Hassonah, M.A. and Mirjalili, S. and Saadeh, H.},
title = {Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm},
journal = {Cognitive Computation},
year = {2018},
volume = {10},
number = {3},
pages = {478-495},
issn = {18669956},
abstract = {Support vector machine (SVM) is considered to be one of the most powerful learning algorithms and is used for a wide range of real-world applications. The efficiency of SVM algorithm and its performance mainly depends on the kernel type and its parameters. Furthermore, the feature subset selection that is used to train the SVM model is another important factor that has a major influence on it classification accuracy. The feature subset selection is a very important step in machine learning, specially when dealing with high-dimensional data sets. Most of the previous researches handled these important factors separately. In this paper, we propose a hybrid approach based on the Grasshopper optimisation algorithm (GOA), which is a recent algorithm inspired by the biological behavior shown in swarms of grasshoppers. The goal of the proposed approach is to optimize the parameters of the SVM model, and locate the best features subset simultaneously. Eighteen low- and high-dimensional benchmark data sets are used to evaluate the accuracy of the proposed approach. For verification, the proposed approach is compared with seven well-regarded algorithms. Furthermore, the proposed approach is compared with grid search, which is the most popular technique for tuning SVM parameters. The experimental results show that the proposed approach outperforms all of the other techniques in most of the data sets in terms of classification accuracy, while minimizing the number of selected features. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.},
document_type = {Article},
doi = {10.1007/s12559-017-9542-9},
publisher = {Springer New York LLC},
source = {Scopus},
}

Aljarah, I., Faris, H., & Mirjalili, S.. (2018). Optimizing connection weights in neural networks using the whale optimization algorithm. Soft computing, 22(1). doi:10.1007/s00500-016-2442-1
[BibTeX] [Abstract]

The learning process of artificial neural networks is considered as one of the most difficult challenges in machine learning and has attracted many researchers recently. The main difficulty of training a neural network is the nonlinear nature and the unknown best set of main controlling parameters (weights and biases). The main disadvantages of the conventional training algorithms are local optima stagnation and slow convergence speed. This makes stochastic optimization algorithm reliable alternative to alleviate these drawbacks. This work proposes a new training algorithm based on the recently proposed whale optimization algorithm (WOA). It has been proved that this algorithm is able to solve a wide range of optimization problems and outperform the current algorithms. This motivated our attempts to benchmark its performance in training feedforward neural networks. For the first time in the literature, a set of 20 datasets with different levels of difficulty are chosen to test the proposed WOA-based trainer. The results are verified by comparisons with back-propagation algorithm and six evolutionary techniques. The qualitative and quantitative results prove that the proposed trainer is able to outperform the cur rent algorithms on the majority of datasets in terms of both local optima avoidance and convergence speed. © Springer-Verlag Berlin Heidelberg 2016.

@Article{Aljarah2018,
author = {Aljarah, I. and Faris, H. and Mirjalili, S.},
title = {Optimizing connection weights in neural networks using the whale optimization algorithm},
journal = {Soft Computing},
year = {2018},
volume = {22},
number = {1},
issn = {14327643},
abstract = {The learning process of artificial neural networks is considered as one of the most difficult challenges in machine learning and has attracted many researchers recently. The main difficulty of training a neural network is the nonlinear nature and the unknown best set of main controlling parameters (weights and biases). The main disadvantages of the conventional training algorithms are local optima stagnation and slow convergence speed. This makes stochastic optimization algorithm reliable alternative to alleviate these drawbacks. This work proposes a new training algorithm based on the recently proposed whale optimization algorithm (WOA). It has been proved that this algorithm is able to solve a wide range of optimization problems and outperform the current algorithms. This motivated our attempts to benchmark its performance in training feedforward neural networks. For the first time in the literature, a set of 20 datasets with different levels of difficulty are chosen to test the proposed WOA-based trainer. The results are verified by comparisons with back-propagation algorithm and six evolutionary techniques. The qualitative and quantitative results prove that the proposed trainer is able to outperform the cur rent algorithms on the majority of datasets in terms of both local optima avoidance and convergence speed. © Springer-Verlag Berlin Heidelberg 2016.},
document_type = {Article},
doi = {10.1007/s00500-016-2442-1},
publisher = {Springer Verlag},
source = {Scopus},
}

Aljarah, I., Faris, H., Mirjalili, S., & Al-Madi, N.. (2018). Training radial basis function networks using biogeography-based optimizer. Neural computing and applications, 29(7), 529-553. doi:10.1007/s00521-016-2559-2
[BibTeX] [Abstract]

Training artificial neural networks is considered as one of the most challenging machine learning problems. This is mainly due to the presence of a large number of solutions and changes in the search space for different datasets. Conventional training techniques mostly suffer from local optima stagnation and degraded convergence, which make them impractical for datasets with many features. The literature shows that stochastic population-based optimization techniques suit this problem better and are reliably alternative because of high local optima avoidance and flexibility. For the first time, this work proposes a new learning mechanism for radial basis function networks based on biogeography-based optimizer as one of the most well-regarded optimizers in the literature. To prove the efficacy of the proposed methodology, it is employed to solve 12 well-known datasets and compared to 11 current training algorithms including gradient-based and stochastic approaches. The paper considers changing the number of neurons and investigating the performance of algorithms on radial basis function networks with different number of parameters as well. A statistical test is also conducted to judge about the significance of the results. The results show that the biogeography-based optimizer trainer is able to substantially outperform the current training algorithms on all datasets in terms of classification accuracy, speed of convergence, and entrapment in local optima. In addition, the comparison of trainers on radial basis function networks with different neurons size reveal that the biogeography-based optimizer trainer is able to train radial basis function networks with different number of structural parameters effectively. © 2016, The Natural Computing Applications Forum.

@Article{Aljarah2018529,
author = {Aljarah, I. and Faris, H. and Mirjalili, S. and Al-Madi, N.},
title = {Training radial basis function networks using biogeography-based optimizer},
journal = {Neural Computing and Applications},
year = {2018},
volume = {29},
number = {7},
pages = {529-553},
issn = {09410643},
abstract = {Training artificial neural networks is considered as one of the most challenging machine learning problems. This is mainly due to the presence of a large number of solutions and changes in the search space for different datasets. Conventional training techniques mostly suffer from local optima stagnation and degraded convergence, which make them impractical for datasets with many features. The literature shows that stochastic population-based optimization techniques suit this problem better and are reliably alternative because of high local optima avoidance and flexibility. For the first time, this work proposes a new learning mechanism for radial basis function networks based on biogeography-based optimizer as one of the most well-regarded optimizers in the literature. To prove the efficacy of the proposed methodology, it is employed to solve 12 well-known datasets and compared to 11 current training algorithms including gradient-based and stochastic approaches. The paper considers changing the number of neurons and investigating the performance of algorithms on radial basis function networks with different number of parameters as well. A statistical test is also conducted to judge about the significance of the results. The results show that the biogeography-based optimizer trainer is able to substantially outperform the current training algorithms on all datasets in terms of classification accuracy, speed of convergence, and entrapment in local optima. In addition, the comparison of trainers on radial basis function networks with different neurons size reveal that the biogeography-based optimizer trainer is able to train radial basis function networks with different number of structural parameters effectively. © 2016, The Natural Computing Applications Forum.},
document_type = {Article},
doi = {10.1007/s00521-016-2559-2},
publisher = {Springer London},
source = {Scopus},
}

Aljarah, I., Mafarja, M., Heidari, A. A., Faris, H., Zhang, Y., & Mirjalili, S.. (2018). Asynchronous accelerating multi-leader salp chains for feature selection. Applied soft computing journal, 71, 964-979. doi:10.1016/j.asoc.2018.07.040
[BibTeX] [Abstract]

Feature selection is an imperative preprocessing step that can positively affect the performance of machine learning techniques. Searching for the optimal feature subset amongst an unabridged dataset is a challenging problem, especially for large-scale datasets. In this research, a binary Salp Swarm Algorithm (SSA) with asynchronous updating rules and a new leadership structure is proposed. To set the best leadership structure, several extensive experiments are performed to determine the most effective number of leaders in the social organization of the artificial salp chain. Inspired from the behavior of a termite colony (TC) in dividing the termites into four types, the salp chain is then divided into several sub-chains, where the salps in each sub-chain can follow a different strategy to adaptively update their locations. Three different updating strategies are employed in this paper. The proposed algorithm is tested and validated on 20 well-known datasets from the UCI repository. The results and comparisons verify that utilizing half of the salps as leaders of the chain can significantly improve the performance of SSA in terms of accuracy metric. Furthermore, dynamically tuning the single parameter of algorithm enable it to more effectively explore the search space in dealing with different feature selection datasets. © 2018 Elsevier B.V.

@Article{Aljarah2018964,
author = {Aljarah, I. and Mafarja, M. and Heidari, A.A. and Faris, H. and Zhang, Y. and Mirjalili, S.},
title = {Asynchronous accelerating multi-leader salp chains for feature selection},
journal = {Applied Soft Computing Journal},
year = {2018},
volume = {71},
pages = {964-979},
issn = {15684946},
abstract = {Feature selection is an imperative preprocessing step that can positively affect the performance of machine learning techniques. Searching for the optimal feature subset amongst an unabridged dataset is a challenging problem, especially for large-scale datasets. In this research, a binary Salp Swarm Algorithm (SSA) with asynchronous updating rules and a new leadership structure is proposed. To set the best leadership structure, several extensive experiments are performed to determine the most effective number of leaders in the social organization of the artificial salp chain. Inspired from the behavior of a termite colony (TC) in dividing the termites into four types, the salp chain is then divided into several sub-chains, where the salps in each sub-chain can follow a different strategy to adaptively update their locations. Three different updating strategies are employed in this paper. The proposed algorithm is tested and validated on 20 well-known datasets from the UCI repository. The results and comparisons verify that utilizing half of the salps as leaders of the chain can significantly improve the performance of SSA in terms of accuracy metric. Furthermore, dynamically tuning the single parameter of algorithm enable it to more effectively explore the search space in dealing with different feature selection datasets. © 2018 Elsevier B.V.},
document_type = {Article},
doi = {10.1016/j.asoc.2018.07.040},
publisher = {Elsevier Ltd},
source = {Scopus},
}

Al-Madi, N., Faris, H., & Abukhurma, R.. (2018). Cost-sensitive genetic programming for churn prediction and identification of the influencing factors in telecommunication market. International journal of advanced science and technology, 120, 13-28. doi:10.14257/ijast.2018.120.02
[BibTeX] [Abstract] [Download PDF]

Churn prediction is crucial for companies in order to build an efficient customer retention plans and apply successful marketing strategies. However, from data mining point of view, churn prediction is considered as a complex and complicated task due to the highly imbalanced distribution of the class labels where the ratio of the churning customers are much smaller than the ratio of the loyal ones. Genetic programming has proven its efficiency in prediction problems, since it solves them as an optimized classification problem with the ability of identifying the relevant features. In this paper, we propose the application of a genetic programming with cost sensitive learning (GP-CSL) that solves the churn prediction task as a classification problem with a penalty cost for prediction errors. Comprehensive experiments on real telecommunication dataset are applied using the proposed GP-CSL technique to evaluate its effectiveness in predicting the customers vulnerable to churn. The obtained results indicate promising churn detection rates compared with other well-known classifiers. Moreover, a formal analysis of the most relevant features in the dataset is performed using different penalty cost matrices and other classical fitness functions. The analysis results show that 4 to 5 features out of 10 features are the most relevant ones. In terms of business, this could help decision makers to determine the most influential factors on customers churn, and consequently help them in planning effective retention campaigns. © 2018 SERSC Australia.

@Article{al2018cost-sensitive,
author = {Al-Madi, N. and Faris, H. and Abukhurma, R.},
title = {Cost-sensitive genetic programming for churn prediction and identification of the influencing factors in telecommunication market},
journal = {International Journal of Advanced Science and Technology},
year = {2018},
volume = {120},
pages = {13-28},
issn = {20054238},
abstract = {Churn prediction is crucial for companies in order to build an efficient customer retention plans and apply successful marketing strategies. However, from data mining point of view, churn prediction is considered as a complex and complicated task due to the highly imbalanced distribution of the class labels where the ratio of the churning customers are much smaller than the ratio of the loyal ones. Genetic programming has proven its efficiency in prediction problems, since it solves them as an optimized classification problem with the ability of identifying the relevant features. In this paper, we propose the application of a genetic programming with cost sensitive learning (GP-CSL) that solves the churn prediction task as a classification problem with a penalty cost for prediction errors. Comprehensive experiments on real telecommunication dataset are applied using the proposed GP-CSL technique to evaluate its effectiveness in predicting the customers vulnerable to churn. The obtained results indicate promising churn detection rates compared with other well-known classifiers. Moreover, a formal analysis of the most relevant features in the dataset is performed using different penalty cost matrices and other classical fitness functions. The analysis results show that 4 to 5 features out of 10 features are the most relevant ones. In terms of business, this could help decision makers to determine the most influential factors on customers churn, and consequently help them in planning effective retention campaigns. © 2018 SERSC Australia.},
document_type = {Article},
url = {http://evo-ml.com/wp-content/uploads/2019/08/al2018cost-sensitive.pdf},
doi = {10.14257/ijast.2018.120.02},
publisher = {Science and Engineering Research Support Society},
source = {Scopus},
}

Alrefai, M., Faris, H., & Aljarah, I.. (2018). Sentiment analysis for arabic language: a brief survey of approaches and techniques. International journal of advanced science and technology, 119, 13-24. doi:10.14257/ijast.2018.119.02
[BibTeX] [Abstract]

With the emergence of Web 2.0 technology and the expansion of on-line social networks, current Internet users have the ability to add their reviews, ratings and opinions on social media and on commercial and news web sites. Sentiment analysis aims to classify these reviews in an automatic way. In the literature, there are numerous approaches proposed for automatic sentiment analysis for different language contexts. Each language has its own properties that makes the sentiment analysis more challenging. In this regard, this work presents a comprehensive survey of existing Arabic sentiment analysis studies, and covers the various approaches and techniques proposed in the literature. Moreover, we highlight the main difficulties and challenges of Arabic sentiment analysis, and the proposed techniques in literature to overcome these barriers. © 2018 SERSC Australia.

@Article{Alrefai201813,
author = {Alrefai, M. and Faris, H. and Aljarah, I.},
title = {Sentiment analysis for arabic language: A brief survey of approaches and techniques},
journal = {International Journal of Advanced Science and Technology},
year = {2018},
volume = {119},
pages = {13-24},
issn = {20054238},
abstract = {With the emergence of Web 2.0 technology and the expansion of on-line social networks, current Internet users have the ability to add their reviews, ratings and opinions on social media and on commercial and news web sites. Sentiment analysis aims to classify these reviews in an automatic way. In the literature, there are numerous approaches proposed for automatic sentiment analysis for different language contexts. Each language has its own properties that makes the sentiment analysis more challenging. In this regard, this work presents a comprehensive survey of existing Arabic sentiment analysis studies, and covers the various approaches and techniques proposed in the literature. Moreover, we highlight the main difficulties and challenges of Arabic sentiment analysis, and the proposed techniques in literature to overcome these barriers. © 2018 SERSC Australia.},
document_type = {Article},
doi = {10.14257/ijast.2018.119.02},
publisher = {Science and Engineering Research Support Society},
source = {Scopus},
}

Al-Zoubi, A. M., Faris, H., Alqatawna, J., & Hassonah, M. A.. (2018). Evolving support vector machines using whale optimization algorithm for spam profiles detection on online social networks in different lingual contexts. Knowledge-based systems, 153, 91-104. doi:10.1016/j.knosys.2018.04.025
[BibTeX] [Abstract]

Detecting spam profiles is considered as one of the most challenging issues in online social networks. The reason is that these profiles are not just a source for unwanted or bad advertisements, but could be a serious threat; as they could initiate malicious activities against other users. Realizing this threat, there is an incremental need for accurate and efficient spam detection models for online social networks. In this paper, a hybrid machine learning model based on Support Vector Machines and one of the recent metaheuristic algorithms called Whale Optimization Algorithm is proposed for the task of identifying spammers in online social networks. The proposed model performs automatic detection of spammers and gives an insight on the most influencing features during the detection process. Moreover, the model is applied and tested on different lingual datasets, where four datasets are collected from Twitter in four languages: Arabic, English, Spanish, and Korean. The experiments and results show that the proposed model outperforms many other algorithms in terms of accuracy, and provides very challenging results in terms of precision, recall, f-measure and AUC. While it also helps in identifying the most influencing features in the detection process. © 2018 Elsevier B.V.

@Article{Al-Zoubi201891,
author = {Al-Zoubi, A.M. and Faris, H. and Alqatawna, J. and Hassonah, M.A.},
title = {Evolving Support Vector Machines using Whale Optimization Algorithm for spam profiles detection on online social networks in different lingual contexts},
journal = {Knowledge-Based Systems},
year = {2018},
volume = {153},
pages = {91-104},
issn = {09507051},
abstract = {Detecting spam profiles is considered as one of the most challenging issues in online social networks. The reason is that these profiles are not just a source for unwanted or bad advertisements, but could be a serious threat; as they could initiate malicious activities against other users. Realizing this threat, there is an incremental need for accurate and efficient spam detection models for online social networks. In this paper, a hybrid machine learning model based on Support Vector Machines and one of the recent metaheuristic algorithms called Whale Optimization Algorithm is proposed for the task of identifying spammers in online social networks. The proposed model performs automatic detection of spammers and gives an insight on the most influencing features during the detection process. Moreover, the model is applied and tested on different lingual datasets, where four datasets are collected from Twitter in four languages: Arabic, English, Spanish, and Korean. The experiments and results show that the proposed model outperforms many other algorithms in terms of accuracy, and provides very challenging results in terms of precision, recall, f-measure and AUC. While it also helps in identifying the most influencing features in the detection process. © 2018 Elsevier B.V.},
coden = {KNSYE},
document_type = {Article},
doi = {10.1016/j.knosys.2018.04.025},
publisher = {Elsevier B.V.},
source = {Scopus},
}

Eshtay, M., Faris, H., & Obeid, N.. (2018). Improving extreme learning machine by competitive swarm optimization and its application for medical diagnosis problems. Expert systems with applications, 104, 134-152. doi:10.1016/j.eswa.2018.03.024
[BibTeX] [Abstract]

Extreme Learning Machine (ELM) is swiftly gaining popularity as a way to train Single hidden Layer Feedforward Networks (SLFN) for its attractive properties. ELM is a fast learning network with remarkable generalization performance. Although ELM generally can outperform traditional gradient descent-based algorithms such as Backpropagation, its performance can be highly affected by the random selection of the input weights and hidden biases of SLFN. Moreover, ELM networks tend to have more hidden neurons due to this random selection. In this paper, we propose a new model that uses Competitive Swarm Optimizer (CSO) to optimize the values of the input weights and hidden neurons of ELM. Two versions of ELM are considered: the classical ELM and the regularized version. The goal of the model is to increase the generalization performance, stabilize the classifier, and to produce more compact networks by reducing the number of neurons in the hidden layer. The proposed model is experimented based on 15 medical classification problems. Experimental results demonstrate that the proposed model can achieve better generalization performance with smaller number of hidden neurons and with higher stability. In addition, it requires much less training time compared to other metaheuristic based ELMs. © 2018 Elsevier Ltd

@Article{Eshtay2018134,
author = {Eshtay, M. and Faris, H. and Obeid, N.},
title = {Improving Extreme Learning Machine by Competitive Swarm Optimization and its application for medical diagnosis problems},
journal = {Expert Systems with Applications},
year = {2018},
volume = {104},
pages = {134-152},
issn = {09574174},
abstract = {Extreme Learning Machine (ELM) is swiftly gaining popularity as a way to train Single hidden Layer Feedforward Networks (SLFN) for its attractive properties. ELM is a fast learning network with remarkable generalization performance. Although ELM generally can outperform traditional gradient descent-based algorithms such as Backpropagation, its performance can be highly affected by the random selection of the input weights and hidden biases of SLFN. Moreover, ELM networks tend to have more hidden neurons due to this random selection. In this paper, we propose a new model that uses Competitive Swarm Optimizer (CSO) to optimize the values of the input weights and hidden neurons of ELM. Two versions of ELM are considered: the classical ELM and the regularized version. The goal of the model is to increase the generalization performance, stabilize the classifier, and to produce more compact networks by reducing the number of neurons in the hidden layer. The proposed model is experimented based on 15 medical classification problems. Experimental results demonstrate that the proposed model can achieve better generalization performance with smaller number of hidden neurons and with higher stability. In addition, it requires much less training time compared to other metaheuristic based ELMs. © 2018 Elsevier Ltd},
coden = {ESAPE},
document_type = {Article},
doi = {10.1016/j.eswa.2018.03.024},
publisher = {Elsevier Ltd},
source = {Scopus},
}

Faris, H.. (2018). A hybrid swarm intelligent neural network model for customer churn prediction and identifying the influencing factors. Information (switzerland), 9(11). doi:10.3390/info9110288
[BibTeX] [Abstract]

Customer churn is one of the most challenging problems for telecommunication companies. In fact, this is because customers are considered as the real asset for the companies. Therefore, more companies are increasing their investments in developing practical solutions that aim at predicting customer churn before it happens. Identifying which customer is about to churn will significantly help the companies in providing solutions to keep their customers and optimize their marketing campaigns. In this work, an intelligent hybrid model based on Particle Swarm Optimization and Feedforward neural network is proposed for churn prediction. PSO is used to tune the weights of the input features and optimize the structure of the neural network simultaneously to increase the prediction power. In addition, the proposed model handles the imbalanced class distribution of the data using an advanced oversampling technique. Evaluation results show that the proposed model can significantly improve the coverage rate of churn customers in comparison with other state-of-the-art classifiers. Moreover, the model has high interpretability, where the assigned feature weights can give an indicator about the importance of their corresponding features in the classification process. © 2018 by the authors.

@Article{Faris2018,
author = {Faris, H.},
title = {A hybrid swarm intelligent neural network model for customer churn prediction and identifying the influencing factors},
journal = {Information (Switzerland)},
year = {2018},
volume = {9},
number = {11},
issn = {20782489},
abstract = {Customer churn is one of the most challenging problems for telecommunication companies. In fact, this is because customers are considered as the real asset for the companies. Therefore, more companies are increasing their investments in developing practical solutions that aim at predicting customer churn before it happens. Identifying which customer is about to churn will significantly help the companies in providing solutions to keep their customers and optimize their marketing campaigns. In this work, an intelligent hybrid model based on Particle Swarm Optimization and Feedforward neural network is proposed for churn prediction. PSO is used to tune the weights of the input features and optimize the structure of the neural network simultaneously to increase the prediction power. In addition, the proposed model handles the imbalanced class distribution of the data using an advanced oversampling technique. Evaluation results show that the proposed model can significantly improve the coverage rate of churn customers in comparison with other state-of-the-art classifiers. Moreover, the model has high interpretability, where the assigned feature weights can give an indicator about the importance of their corresponding features in the classification process. © 2018 by the authors.},
art_number = {288},
document_type = {Article},
doi = {10.3390/info9110288},
publisher = {MDPI AG},
source = {Scopus},
}

Faris, H., Aljarah, I., Al-Betar, M. A., & Mirjalili, S.. (2018). Grey wolf optimizer: a review of recent variants and applications. Neural computing and applications, 30(2), 413-435. doi:10.1007/s00521-017-3272-5
[BibTeX] [Abstract]

Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated. © 2017, The Natural Computing Applications Forum.

@Article{Faris2018413,
author = {Faris, H. and Aljarah, I. and Al-Betar, M.A. and Mirjalili, S.},
title = {Grey wolf optimizer: a review of recent variants and applications},
journal = {Neural Computing and Applications},
year = {2018},
volume = {30},
number = {2},
pages = {413-435},
issn = {09410643},
abstract = {Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated. © 2017, The Natural Computing Applications Forum.},
document_type = {Review},
doi = {10.1007/s00521-017-3272-5},
publisher = {Springer London},
source = {Scopus},
}

Faris, H., Aljarah, I., & Mirjalili, S.. (2018). Improved monarch butterfly optimization for unconstrained global search and neural network training. Applied intelligence, 48(2), 445-464. doi:10.1007/s10489-017-0967-3
[BibTeX] [Abstract]

This work is a seminal attempt to address the drawbacks of the recently proposed monarch butterfly optimization (MBO) algorithm. This algorithm suffers from premature convergence, which makes it less suitable for solving real-world problems. The position updating of MBO is modified to involve previous solutions in addition to the best solution obtained thus far. To prove the efficiency of the Improved MBO (IMBO), a set of 23 well-known test functions is employed. The statistical results show that IMBO benefits from high local optima avoidance and fast convergence speed which helps this algorithm to outperform basic MBO and another recent variant of this algorithm called greedy strategy and self-adaptive crossover operator MBO (GCMBO). The results of the proposed algorithm are compared with nine other approaches in the literature for verification. The comparative analysis shows that IMBO provides very competitive results and tends to outperform current algorithms. To demonstrate the applicability of IMBO at solving challenging practical problems, it is also employed to train neural networks as well. The IMBO-based trainer is tested on 15 popular classification datasets obtained from the University of California at Irvine (UCI) Machine Learning Repository. The results are compared to a variety of techniques in the literature including the original MBO and GCMBO. It is observed that IMBO improves the learning of neural networks significantly, proving the merits of this algorithm for solving challenging problems. © 2017, Springer Science+Business Media New York.

@Article{Faris2018445,
author = {Faris, H. and Aljarah, I. and Mirjalili, S.},
title = {Improved monarch butterfly optimization for unconstrained global search and neural network training},
journal = {Applied Intelligence},
year = {2018},
volume = {48},
number = {2},
pages = {445-464},
issn = {0924669X},
abstract = {This work is a seminal attempt to address the drawbacks of the recently proposed monarch butterfly optimization (MBO) algorithm. This algorithm suffers from premature convergence, which makes it less suitable for solving real-world problems. The position updating of MBO is modified to involve previous solutions in addition to the best solution obtained thus far. To prove the efficiency of the Improved MBO (IMBO), a set of 23 well-known test functions is employed. The statistical results show that IMBO benefits from high local optima avoidance and fast convergence speed which helps this algorithm to outperform basic MBO and another recent variant of this algorithm called greedy strategy and self-adaptive crossover operator MBO (GCMBO). The results of the proposed algorithm are compared with nine other approaches in the literature for verification. The comparative analysis shows that IMBO provides very competitive results and tends to outperform current algorithms. To demonstrate the applicability of IMBO at solving challenging practical problems, it is also employed to train neural networks as well. The IMBO-based trainer is tested on 15 popular classification datasets obtained from the University of California at Irvine (UCI) Machine Learning Repository. The results are compared to a variety of techniques in the literature including the original MBO and GCMBO. It is observed that IMBO improves the learning of neural networks significantly, proving the merits of this algorithm for solving challenging problems. © 2017, Springer Science+Business Media New York.},
coden = {APITE},
document_type = {Article},
doi = {10.1007/s10489-017-0967-3},
publisher = {Springer New York LLC},
source = {Scopus},
}

Faris, H., Hassonah, M. A., Al-Zoubi, A. M., Mirjalili, S., & Aljarah, I.. (2018). A multi-verse optimizer approach for feature selection and optimizing svm parameters based on a robust system architecture. Neural computing and applications, 30(8), 2355-2369. doi:10.1007/s00521-016-2818-2
[BibTeX] [Abstract]

Support vector machine (SVM) is a well-regarded machine learning algorithm widely applied to classification tasks and regression problems. SVM was founded based on the statistical learning theory and structural risk minimization. Despite the high prediction rate of this technique in a wide range of real applications, the efficiency of SVM and its classification accuracy highly depends on the parameter setting as well as the subset feature selection. This work proposes a robust approach based on a recent nature-inspired metaheuristic called multi-verse optimizer (MVO) for selecting optimal features and optimizing the parameters of SVM simultaneously. In fact, the MVO algorithm is employed as a tuner to manipulate the main parameters of SVM and find the optimal set of features for this classifier. The proposed approach is implemented and tested on two different system architectures. MVO is benchmarked and compared with four classic and recent metaheuristic algorithms using ten binary and multi-class labeled datasets. Experimental results demonstrate that MVO can effectively reduce the number of features while maintaining a high prediction accuracy. © 2017, The Natural Computing Applications Forum.

@Article{Faris20182355,
author = {Faris, H. and Hassonah, M.A. and Al-Zoubi, A.M. and Mirjalili, S. and Aljarah, I.},
title = {A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture},
journal = {Neural Computing and Applications},
year = {2018},
volume = {30},
number = {8},
pages = {2355-2369},
issn = {09410643},
abstract = {Support vector machine (SVM) is a well-regarded machine learning algorithm widely applied to classification tasks and regression problems. SVM was founded based on the statistical learning theory and structural risk minimization. Despite the high prediction rate of this technique in a wide range of real applications, the efficiency of SVM and its classification accuracy highly depends on the parameter setting as well as the subset feature selection. This work proposes a robust approach based on a recent nature-inspired metaheuristic called multi-verse optimizer (MVO) for selecting optimal features and optimizing the parameters of SVM simultaneously. In fact, the MVO algorithm is employed as a tuner to manipulate the main parameters of SVM and find the optimal set of features for this classifier. The proposed approach is implemented and tested on two different system architectures. MVO is benchmarked and compared with four classic and recent metaheuristic algorithms using ten binary and multi-class labeled datasets. Experimental results demonstrate that MVO can effectively reduce the number of features while maintaining a high prediction accuracy. © 2017, The Natural Computing Applications Forum.},
document_type = {Article},
doi = {10.1007/s00521-016-2818-2},
publisher = {Springer London},
source = {Scopus},
}

Support vector machine (SVM) is a well-regarded machine learning algorithm widely applied to classification tasks and regression problems. SVM was founded based on the statistical learning theory and structural risk minimization. Despite the high prediction rate of this technique in a wide range of real applications, the efficiency of SVM and its classification accuracy highly depends on the parameter setting as well as the subset feature selection. This work proposes a robust approach based on a recent nature-inspired metaheuristic called multi-verse optimizer (MVO) for selecting optimal features and optimizing the parameters of SVM simultaneously. In fact, the MVO algorithm is employed as a tuner to manipulate the main parameters of SVM and find the optimal set of features for this classifier. The proposed approach is implemented and tested on two different system architectures. MVO is benchmarked and compared with four classic and recent metaheuristic algorithms using ten binary and multi-class labeled datasets. Experimental results demonstrate that MVO can effectively reduce the number of features while maintaining a high prediction accuracy. © 2017, The Natural Computing Applications Forum.

@Article{Faris2018c,
author = {Faris, H. and Hassonah, M.A. and Al-Zoubi, A.M. and Mirjalili, S. and Aljarah, I.},
title = {A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture},
journal = {Neural Computing and Applications},
year = {2018},
volume = {30},
number = {8},
pages = {2355-2369},
issn = {09410643},
abstract = {Support vector machine (SVM) is a well-regarded machine learning algorithm widely applied to classification tasks and regression problems. SVM was founded based on the statistical learning theory and structural risk minimization. Despite the high prediction rate of this technique in a wide range of real applications, the efficiency of SVM and its classification accuracy highly depends on the parameter setting as well as the subset feature selection. This work proposes a robust approach based on a recent nature-inspired metaheuristic called multi-verse optimizer (MVO) for selecting optimal features and optimizing the parameters of SVM simultaneously. In fact, the MVO algorithm is employed as a tuner to manipulate the main parameters of SVM and find the optimal set of features for this classifier. The proposed approach is implemented and tested on two different system architectures. MVO is benchmarked and compared with four classic and recent metaheuristic algorithms using ten binary and multi-class labeled datasets. Experimental results demonstrate that MVO can effectively reduce the number of features while maintaining a high prediction accuracy. © 2017, The Natural Computing Applications Forum.},
document_type = {Article},
doi = {10.1007/s00521-016-2818-2},
publisher = {Springer London},
source = {Scopus},
}

Faris, H., Mafarja, M. M., Heidari, A. A., Aljarah, I., Al-Zoubi, A. M., Mirjalili, S., & Fujita, H.. (2018). An efficient binary salp swarm algorithm with crossover scheme for feature selection problems. Knowledge-based systems, 154, 43-67. doi:10.1016/j.knosys.2018.05.009
[BibTeX] [Abstract]

Searching for the (near) optimal subset of features is a challenging problem in the process of feature selection (FS). In the literature, Swarm Intelligence (SI) algorithms show superior performance in solving this problem. This motivated our attempts to test the performance of the newly proposed Salp Swarm Algorithm (SSA) in this area. As such, two new wrapper FS approaches that use SSA as the search strategy are proposed. In the first approach, eight transfer functions are employed to convert the continuous version of SSA to binary. In the second approach, the crossover operator is used in addition to the transfer functions to replace the average operator and enhance the exploratory behavior of the algorithm. The proposed approaches are benchmarked on 22 well-known UCI datasets and the results are compared with 5 FS methods: Binary Grey Wolf Optimizer (BGWO), Binary Gravitational Search Algorithms (BGSA), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), and Genetic Algorithm (GA). The paper also considers an extensive study of the parameter setting for the proposed technique. From the results, it is observed that the proposed approach significantly outperforms others on around 90% of the datasets. © 2018 Elsevier B.V.

@Article{Faris201843,
author = {Faris, H. and Mafarja, M.M. and Heidari, A.A. and Aljarah, I. and Al-Zoubi, A.M. and Mirjalili, S. and Fujita, H.},
title = {An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems},
journal = {Knowledge-Based Systems},
year = {2018},
volume = {154},
pages = {43-67},
issn = {09507051},
abstract = {Searching for the (near) optimal subset of features is a challenging problem in the process of feature selection (FS). In the literature, Swarm Intelligence (SI) algorithms show superior performance in solving this problem. This motivated our attempts to test the performance of the newly proposed Salp Swarm Algorithm (SSA) in this area. As such, two new wrapper FS approaches that use SSA as the search strategy are proposed. In the first approach, eight transfer functions are employed to convert the continuous version of SSA to binary. In the second approach, the crossover operator is used in addition to the transfer functions to replace the average operator and enhance the exploratory behavior of the algorithm. The proposed approaches are benchmarked on 22 well-known UCI datasets and the results are compared with 5 FS methods: Binary Grey Wolf Optimizer (BGWO), Binary Gravitational Search Algorithms (BGSA), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), and Genetic Algorithm (GA). The paper also considers an extensive study of the parameter setting for the proposed technique. From the results, it is observed that the proposed approach significantly outperforms others on around 90% of the datasets. © 2018 Elsevier B.V.},
coden = {KNSYE},
document_type = {Article},
doi = {10.1016/j.knosys.2018.05.009},
publisher = {Elsevier B.V.},
source = {Scopus},
}

Searching for the (near) optimal subset of features is a challenging problem in the process of feature selection (FS). In the literature, Swarm Intelligence (SI) algorithms show superior performance in solving this problem. This motivated our attempts to test the performance of the newly proposed Salp Swarm Algorithm (SSA) in this area. As such, two new wrapper FS approaches that use SSA as the search strategy are proposed. In the first approach, eight transfer functions are employed to convert the continuous version of SSA to binary. In the second approach, the crossover operator is used in addition to the transfer functions to replace the average operator and enhance the exploratory behavior of the algorithm. The proposed approaches are benchmarked on 22 well-known UCI datasets and the results are compared with 5 FS methods: Binary Grey Wolf Optimizer (BGWO), Binary Gravitational Search Algorithms (BGSA), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), and Genetic Algorithm (GA). The paper also considers an extensive study of the parameter setting for the proposed technique. From the results, it is observed that the proposed approach significantly outperforms others on around 90% of the datasets. © 2018 Elsevier B.V.

@Article{Faris2018b,
author = {Faris, H. and Mafarja, M.M. and Heidari, A.A. and Aljarah, I. and Al-Zoubi, A.M. and Mirjalili, S. and Fujita, H.},
title = {An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems},
journal = {Knowledge-Based Systems},
year = {2018},
volume = {154},
pages = {43-67},
issn = {09507051},
abstract = {Searching for the (near) optimal subset of features is a challenging problem in the process of feature selection (FS). In the literature, Swarm Intelligence (SI) algorithms show superior performance in solving this problem. This motivated our attempts to test the performance of the newly proposed Salp Swarm Algorithm (SSA) in this area. As such, two new wrapper FS approaches that use SSA as the search strategy are proposed. In the first approach, eight transfer functions are employed to convert the continuous version of SSA to binary. In the second approach, the crossover operator is used in addition to the transfer functions to replace the average operator and enhance the exploratory behavior of the algorithm. The proposed approaches are benchmarked on 22 well-known UCI datasets and the results are compared with 5 FS methods: Binary Grey Wolf Optimizer (BGWO), Binary Gravitational Search Algorithms (BGSA), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), and Genetic Algorithm (GA). The paper also considers an extensive study of the parameter setting for the proposed technique. From the results, it is observed that the proposed approach significantly outperforms others on around 90% of the datasets. © 2018 Elsevier B.V.},
coden = {KNSYE},
document_type = {Article},
doi = {10.1016/j.knosys.2018.05.009},
publisher = {Elsevier B.V.},
source = {Scopus},
}

Hassanat, A. B., Altarawneh, G., Tarawneh, A. S., Faris, H., Alhasanat, M. B., De Voogt, A., Al-Rawashdeh, B., Alshamaileh, M., & Prasath, S. V. B.. (2018). On computerizing the ancient game of āb. International journal of gaming and computer-mediated simulations, 10(3), 20-40. doi:10.4018/IJGCMS.2018070102
[BibTeX] [Abstract]

The ancient game of āb is a war and race game. It is played by two teams, each consisting of at Least one player. In addition to presenting the game and its rules, the authors develop three versions of the game: human versus human, human versus computer, and computer versus computer. The authors employ a Genetic Algorithm (GA) to help the computer to choose the ‘best’ move to play. The computer game is designed allowing two degrees of difficulty: Beginners and Advanced. The results of several experiments show the strategic properties of this game, the strength of the proposed method by making the computer play the game intelligently, and the potential of generalizing their approach to other similar games. Copyright © 2018, IGI Global.

@Article{Hassanat201820,
author = {Hassanat, A.B. and Altarawneh, G. and Tarawneh, A.S. and Faris, H. and Alhasanat, M.B. and De Voogt, A. and Al-Rawashdeh, B. and Alshamaileh, M. and Prasath, S.V.B.},
title = {On computerizing the ancient game of āb},
journal = {International Journal of Gaming and Computer-Mediated Simulations},
year = {2018},
volume = {10},
number = {3},
pages = {20-40},
issn = {19423888},
abstract = {The ancient game of āb is a war and race game. It is played by two teams, each consisting of at Least one player. In addition to presenting the game and its rules, the authors develop three versions of the game: human versus human, human versus computer, and computer versus computer. The authors employ a Genetic Algorithm (GA) to help the computer to choose the ‘best’ move to play. The computer game is designed allowing two degrees of difficulty: Beginners and Advanced. The results of several experiments show the strategic properties of this game, the strength of the proposed method by making the computer play the game intelligently, and the potential of generalizing their approach to other similar games. Copyright © 2018, IGI Global.},
document_type = {Article},
doi = {10.4018/IJGCMS.2018070102},
publisher = {IGI Global},
source = {Scopus},
}

Hassanat, A. B., Prasath, V. B. S., Abbadi, M. A., Abu-Qdari, S. A., & Faris, H.. (2018). An improved genetic algorithm with a new initialization mechanism based on regression techniques. Information (switzerland), 9(7). doi:10.3390/info9070167
[BibTeX] [Abstract]

Genetic algorithm (GA) is one of the well-known techniques from the area of evolutionary computation that plays a significant role in obtaining meaningful solutions to complex problems with large search space. GAs involve three fundamental operations after creating an initial population, namely selection, crossover, and mutation. The first task in GAs is to create an appropriate initial population. Traditionally GAs with randomly selected population is widely used as it is simple and efficient; however, the generated population may contain poor fitness. Low quality or poor fitness of individuals may lead to take long time to converge to an optimal (or near-optimal) solution. Therefore, the fitness or quality of initial population of individuals plays a significant role in determining an optimal or near-optimal solution. In this work, we propose a new method for the initial population seeding based on linear regression analysis of the problem tackled by the GA; in this paper, the traveling salesman problem (TSP). The proposed Regression-based technique divides a given large scale TSP problem into smaller sub-problems. This is done using the regression line and its perpendicular line, which allow for clustering the cities into four sub-problems repeatedly, the location of each city determines which category/cluster the city belongs to, the algorithm works repeatedly until the size of the subproblem becomes very small, four cities or less for instance, these cities are more likely neighboring each other, so connecting them to each other creates a somehow good solution to start with, this solution is mutated several times to form the initial population. We analyze the performance of the GA when using traditional population seeding techniques, such as the random and nearest neighbors, along with the proposed regression-based technique. The experiments are carried out using some of the well-known TSP instances obtained from the TSPLIB, which is the standard library for TSP problems. Quantitative analysis is carried out using the statistical test tools: analysis of variance (ANOVA), Duncan multiple range test (DMRT), and least significant difference (LSD). The experimental results show that the performance of the GA that uses the proposed regression-based technique for population seeding outperforms other GAs that uses traditional population seeding techniques such as the random and the nearest neighbor based techniques in terms of error rate, and average convergence. © 2018 by the authors.

@Article{Hassanat2018,
author = {Hassanat, A.B. and Prasath, V.B.S. and Abbadi, M.A. and Abu-Qdari, S.A. and Faris, H.},
title = {An improved Genetic Algorithm with a new initialization mechanism based on Regression techniques},
journal = {Information (Switzerland)},
year = {2018},
volume = {9},
number = {7},
issn = {20782489},
abstract = {Genetic algorithm (GA) is one of the well-known techniques from the area of evolutionary computation that plays a significant role in obtaining meaningful solutions to complex problems with large search space. GAs involve three fundamental operations after creating an initial population, namely selection, crossover, and mutation. The first task in GAs is to create an appropriate initial population. Traditionally GAs with randomly selected population is widely used as it is simple and efficient; however, the generated population may contain poor fitness. Low quality or poor fitness of individuals may lead to take long time to converge to an optimal (or near-optimal) solution. Therefore, the fitness or quality of initial population of individuals plays a significant role in determining an optimal or near-optimal solution. In this work, we propose a new method for the initial population seeding based on linear regression analysis of the problem tackled by the GA; in this paper, the traveling salesman problem (TSP). The proposed Regression-based technique divides a given large scale TSP problem into smaller sub-problems. This is done using the regression line and its perpendicular line, which allow for clustering the cities into four sub-problems repeatedly, the location of each city determines which category/cluster the city belongs to, the algorithm works repeatedly until the size of the subproblem becomes very small, four cities or less for instance, these cities are more likely neighboring each other, so connecting them to each other creates a somehow good solution to start with, this solution is mutated several times to form the initial population. We analyze the performance of the GA when using traditional population seeding techniques, such as the random and nearest neighbors, along with the proposed regression-based technique. The experiments are carried out using some of the well-known TSP instances obtained from the TSPLIB, which is the standard library for TSP problems. Quantitative analysis is carried out using the statistical test tools: analysis of variance (ANOVA), Duncan multiple range test (DMRT), and least significant difference (LSD). The experimental results show that the performance of the GA that uses the proposed regression-based technique for population seeding outperforms other GAs that uses traditional population seeding techniques such as the random and the nearest neighbor based techniques in terms of error rate, and average convergence. © 2018 by the authors.},
art_number = {167},
document_type = {Article},
doi = {10.3390/info9070167},
publisher = {MDPI AG},
source = {Scopus},
}

Heidari, A. A., Faris, H., Aljarah, I., & Mirjalili, S.. (2018). An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft computing. doi:10.1007/s00500-018-3424-2
[BibTeX] [Abstract]

This paper proposes a new hybrid stochastic training algorithm using the recently proposed grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural networks. The GOA algorithm is an emerging technique with a high potential in tackling optimization problems based on its flexible and adaptive searching mechanisms. It can demonstrate a satisfactory performance by escaping from local optima and balancing the exploration and exploitation trends. The proposed GOAMLP model is then applied to five important datasets: breast cancer, parkinson, diabetes, coronary heart disease, and orthopedic patients. The results are deeply validated in comparison with eight recent and well-regarded algorithms qualitatively and quantitatively. It is shown and proved that the proposed stochastic training algorithm GOAMLP is substantially beneficial in improving the classification rate of MLPs. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

@Article{Heidari2018,
author = {Heidari, A.A. and Faris, H. and Aljarah, I. and Mirjalili, S.},
title = {An efficient hybrid multilayer perceptron neural network with grasshopper optimization},
journal = {Soft Computing},
year = {2018},
issn = {14327643},
abstract = {This paper proposes a new hybrid stochastic training algorithm using the recently proposed grasshopper optimization algorithm (GOA) for multilayer perceptrons (MLPs) neural networks. The GOA algorithm is an emerging technique with a high potential in tackling optimization problems based on its flexible and adaptive searching mechanisms. It can demonstrate a satisfactory performance by escaping from local optima and balancing the exploration and exploitation trends. The proposed GOAMLP model is then applied to five important datasets: breast cancer, parkinson, diabetes, coronary heart disease, and orthopedic patients. The results are deeply validated in comparison with eight recent and well-regarded algorithms qualitatively and quantitatively. It is shown and proved that the proposed stochastic training algorithm GOAMLP is substantially beneficial in improving the classification rate of MLPs. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.},
document_type = {Article in Press},
doi = {10.1007/s00500-018-3424-2},
publisher = {Springer Verlag},
source = {Scopus},
}

Herzallah, W., Faris, H., & Adwan, O.. (2018). Feature engineering for detecting spammers on twitter: modelling and analysis. Journal of information science, 44(2), 230-247. doi:10.1177/0165551516684296
[BibTeX] [Abstract]

Twitter is a social networking website that has gained a lot of popularity around the world in the last decade. This popularity made Twitter a common target for spammers and malicious users to spread unwanted advertisements, viruses and phishing attacks. In this article, we review the latest research works to determine the most effective features that were investigated for spam detection in the literature. These features are collected to build a comprehensive data set that can be used to develop more robust and accurate spammer detection models. The new data set is tested using popular classifiers (Naive Bayes, support vector machines, multilayer perceptron neural networks, Decision Trees, Random forests and k-Nearest Neighbour). The prediction performance of these classifiers is evaluated and compared based on different evaluation metrics. Moreover, a further analysis is carried out to identify the features that have higher impact on the accuracy of spam detection. Three different techniques are used and compared for this analysis: change of mean square error (CoM), information gain (IG) and Relief-F method. Top five features identified by each technique are used again to build the detection models. Experimental results show that most of the developed classifiers obtained high evaluation results based on the comprehensive data set constructed in this work. Experiments also reveal the important role of some features like the reputation of the account, average length of the tweet, average mention per tweet, age of the account, and the average time between posts in the process of identifying spammers in the social network. © 2017, © The Author(s) 2017.

@Article{Herzallah2018230,
author = {Herzallah, W. and Faris, H. and Adwan, O.},
title = {Feature engineering for detecting spammers on Twitter: Modelling and analysis},
journal = {Journal of Information Science},
year = {2018},
volume = {44},
number = {2},
pages = {230-247},
issn = {01655515},
abstract = {Twitter is a social networking website that has gained a lot of popularity around the world in the last decade. This popularity made Twitter a common target for spammers and malicious users to spread unwanted advertisements, viruses and phishing attacks. In this article, we review the latest research works to determine the most effective features that were investigated for spam detection in the literature. These features are collected to build a comprehensive data set that can be used to develop more robust and accurate spammer detection models. The new data set is tested using popular classifiers (Naive Bayes, support vector machines, multilayer perceptron neural networks, Decision Trees, Random forests and k-Nearest Neighbour). The prediction performance of these classifiers is evaluated and compared based on different evaluation metrics. Moreover, a further analysis is carried out to identify the features that have higher impact on the accuracy of spam detection. Three different techniques are used and compared for this analysis: change of mean square error (CoM), information gain (IG) and Relief-F method. Top five features identified by each technique are used again to build the detection models. Experimental results show that most of the developed classifiers obtained high evaluation results based on the comprehensive data set constructed in this work. Experiments also reveal the important role of some features like the reputation of the account, average length of the tweet, average mention per tweet, age of the account, and the average time between posts in the process of identifying spammers in the social network. © 2017, © The Author(s) 2017.},
coden = {JISCD},
document_type = {Article},
doi = {10.1177/0165551516684296},
publisher = {SAGE Publications Ltd},
source = {Scopus},
}

Mafarja, M., Aljarah, I., Heidari, A. A., Faris, H., Fournier-Viger, P., Li, X., & Mirjalili, S.. (2018). Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowledge-based systems, 161, 185-204. doi:10.1016/j.knosys.2018.08.003
[BibTeX] [Abstract]

The Dragonfly Algorithm (DA) is a recently proposed heuristic search algorithm that was shown to have excellent performance for numerous optimization problems. In this paper, a wrapper-feature selection algorithm is proposed based on the Binary Dragonfly Algorithm (BDA). The key component of the BDA is the transfer function that maps a continuous search space to a discrete search space. In this study, eight transfer functions, categorized into two families (S-shaped and V-shaped functions) are integrated into the BDA and evaluated using eighteen benchmark datasets obtained from the UCI data repository. The main contribution of this paper is the proposal of time-varying S-shaped and V-shaped transfer functions to leverage the impact of the step vector on balancing exploration and exploitation. During the early stages of the optimization process, the probability of changing the position of an element is high, which facilitates the exploration of new solutions starting from the initial population. On the other hand, the probability of changing the position of an element becomes lower towards the end of the optimization process. This behavior is obtained by considering the current iteration number as a parameter of transfer functions. The performance of the proposed approaches is compared with that of other state-of-art approaches including the DA, binary grey wolf optimizer (bGWO), binary gravitational search algorithm (BGSA), binary bat algorithm (BBA), particle swarm optimization (PSO), and genetic algorithm in terms of classification accuracy, sensitivity, specificity, area under the curve, and number of selected attributes. Results show that the time-varying S-shaped BDA approach outperforms compared approaches. © 2018

@Article{Mafarja2018185,
author = {Mafarja, M. and Aljarah, I. and Heidari, A.A. and Faris, H. and Fournier-Viger, P. and Li, X. and Mirjalili, S.},
title = {Binary dragonfly optimization for feature selection using time-varying transfer functions},
journal = {Knowledge-Based Systems},
year = {2018},
volume = {161},
pages = {185-204},
issn = {09507051},
abstract = {The Dragonfly Algorithm (DA) is a recently proposed heuristic search algorithm that was shown to have excellent performance for numerous optimization problems. In this paper, a wrapper-feature selection algorithm is proposed based on the Binary Dragonfly Algorithm (BDA). The key component of the BDA is the transfer function that maps a continuous search space to a discrete search space. In this study, eight transfer functions, categorized into two families (S-shaped and V-shaped functions) are integrated into the BDA and evaluated using eighteen benchmark datasets obtained from the UCI data repository. The main contribution of this paper is the proposal of time-varying S-shaped and V-shaped transfer functions to leverage the impact of the step vector on balancing exploration and exploitation. During the early stages of the optimization process, the probability of changing the position of an element is high, which facilitates the exploration of new solutions starting from the initial population. On the other hand, the probability of changing the position of an element becomes lower towards the end of the optimization process. This behavior is obtained by considering the current iteration number as a parameter of transfer functions. The performance of the proposed approaches is compared with that of other state-of-art approaches including the DA, binary grey wolf optimizer (bGWO), binary gravitational search algorithm (BGSA), binary bat algorithm (BBA), particle swarm optimization (PSO), and genetic algorithm in terms of classification accuracy, sensitivity, specificity, area under the curve, and number of selected attributes. Results show that the time-varying S-shaped BDA approach outperforms compared approaches. © 2018},
coden = {KNSYE},
document_type = {Article},
doi = {10.1016/j.knosys.2018.08.003},
publisher = {Elsevier B.V.},
source = {Scopus},
}

Mafarja, M., Aljarah, I., Heidari, A. A., Hammouri, A. I., Faris, H., Al-Zoubi, A. M., & Mirjalili, S.. (2018). Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowledge-based systems, 145, 25-45. doi:10.1016/j.knosys.2017.12.037
[BibTeX] [Abstract]

Searching for the optimal subset of features is known as a challenging problem in feature selection process. To deal with the difficulties involved in this problem, a robust and reliable optimization algorithm is required. In this paper, Grasshopper Optimization Algorithm (GOA) is employed as a search strategy to design a wrapper-based feature selection method. The GOA is a recent population-based metaheuristic that mimics the swarming behaviors of grasshoppers. In this work, an efficient optimizer based on the simultaneous use of the GOA, selection operators, and Evolutionary Population Dynamics (EPD) is proposed in the form of four different strategies to mitigate the immature convergence and stagnation drawbacks of the conventional GOA. In the first two approaches, one of the top three agents and a randomly generated one are selected to reposition a solution from the worst half of the population. In the third and fourth approaches, to give a chance to the low fitness solutions in reforming the population, Roulette Wheel Selection (RWS) and Tournament Selection (TS) are utilized to select the guiding agent from the first half. The proposed GOA_EPD approaches are employed to tackle various feature selection tasks. The proposed approaches are benchmarked on 22 UCI datasets. The comprehensive results and various comparisons reveal that the EPD has a remarkable impact on the efficacy of the GOA and using the selection mechanism enhanced the capability of the proposed approach to outperform other optimizers and find the best solutions with improved convergence trends. Furthermore, the comparative experiments demonstrate the superiority of the proposed approaches when compared to other similar methods in the literature. © 2017

@Article{Mafarja201825,
author = {Mafarja, M. and Aljarah, I. and Heidari, A.A. and Hammouri, A.I. and Faris, H. and Al-Zoubi, A.M. and Mirjalili, S.},
title = {Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems},
journal = {Knowledge-Based Systems},
year = {2018},
volume = {145},
pages = {25-45},
issn = {09507051},
abstract = {Searching for the optimal subset of features is known as a challenging problem in feature selection process. To deal with the difficulties involved in this problem, a robust and reliable optimization algorithm is required. In this paper, Grasshopper Optimization Algorithm (GOA) is employed as a search strategy to design a wrapper-based feature selection method. The GOA is a recent population-based metaheuristic that mimics the swarming behaviors of grasshoppers. In this work, an efficient optimizer based on the simultaneous use of the GOA, selection operators, and Evolutionary Population Dynamics (EPD) is proposed in the form of four different strategies to mitigate the immature convergence and stagnation drawbacks of the conventional GOA. In the first two approaches, one of the top three agents and a randomly generated one are selected to reposition a solution from the worst half of the population. In the third and fourth approaches, to give a chance to the low fitness solutions in reforming the population, Roulette Wheel Selection (RWS) and Tournament Selection (TS) are utilized to select the guiding agent from the first half. The proposed GOA_EPD approaches are employed to tackle various feature selection tasks. The proposed approaches are benchmarked on 22 UCI datasets. The comprehensive results and various comparisons reveal that the EPD has a remarkable impact on the efficacy of the GOA and using the selection mechanism enhanced the capability of the proposed approach to outperform other optimizers and find the best solutions with improved convergence trends. Furthermore, the comparative experiments demonstrate the superiority of the proposed approaches when compared to other similar methods in the literature. © 2017},
coden = {KNSYE},
document_type = {Article},
doi = {10.1016/j.knosys.2017.12.037},
publisher = {Elsevier B.V.},
source = {Scopus},
}

Searching for the optimal subset of features is known as a challenging problem in feature selection process. To deal with the difficulties involved in this problem, a robust and reliable optimization algorithm is required. In this paper, Grasshopper Optimization Algorithm (GOA) is employed as a search strategy to design a wrapper-based feature selection method. The GOA is a recent population-based metaheuristic that mimics the swarming behaviors of grasshoppers. In this work, an efficient optimizer based on the simultaneous use of the GOA, selection operators, and Evolutionary Population Dynamics (EPD) is proposed in the form of four different strategies to mitigate the immature convergence and stagnation drawbacks of the conventional GOA. In the first two approaches, one of the top three agents and a randomly generated one are selected to reposition a solution from the worst half of the population. In the third and fourth approaches, to give a chance to the low fitness solutions in reforming the population, Roulette Wheel Selection (RWS) and Tournament Selection (TS) are utilized to select the guiding agent from the first half. The proposed GOA_EPD approaches are employed to tackle various feature selection tasks. The proposed approaches are benchmarked on 22 UCI datasets. The comprehensive results and various comparisons reveal that the EPD has a remarkable impact on the efficacy of the GOA and using the selection mechanism enhanced the capability of the proposed approach to outperform other optimizers and find the best solutions with improved convergence trends. Furthermore, the comparative experiments demonstrate the superiority of the proposed approaches when compared to other similar methods in the literature. © 2017

@Article{Mafarja2018a,
author = {Mafarja, M. and Aljarah, I. and Heidari, A.A. and Hammouri, A.I. and Faris, H. and Al-Zoubi, A.M. and Mirjalili, S.},
title = {Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems},
journal = {Knowledge-Based Systems},
year = {2018},
volume = {145},
pages = {25-45},
issn = {09507051},
abstract = {Searching for the optimal subset of features is known as a challenging problem in feature selection process. To deal with the difficulties involved in this problem, a robust and reliable optimization algorithm is required. In this paper, Grasshopper Optimization Algorithm (GOA) is employed as a search strategy to design a wrapper-based feature selection method. The GOA is a recent population-based metaheuristic that mimics the swarming behaviors of grasshoppers. In this work, an efficient optimizer based on the simultaneous use of the GOA, selection operators, and Evolutionary Population Dynamics (EPD) is proposed in the form of four different strategies to mitigate the immature convergence and stagnation drawbacks of the conventional GOA. In the first two approaches, one of the top three agents and a randomly generated one are selected to reposition a solution from the worst half of the population. In the third and fourth approaches, to give a chance to the low fitness solutions in reforming the population, Roulette Wheel Selection (RWS) and Tournament Selection (TS) are utilized to select the guiding agent from the first half. The proposed GOA_EPD approaches are employed to tackle various feature selection tasks. The proposed approaches are benchmarked on 22 UCI datasets. The comprehensive results and various comparisons reveal that the EPD has a remarkable impact on the efficacy of the GOA and using the selection mechanism enhanced the capability of the proposed approach to outperform other optimizers and find the best solutions with improved convergence trends. Furthermore, the comparative experiments demonstrate the superiority of the proposed approaches when compared to other similar methods in the literature. © 2017},
coden = {KNSYE},
document_type = {Article},
doi = {10.1016/j.knosys.2017.12.037},
publisher = {Elsevier B.V.},
source = {Scopus},
}

Mirjalili, S. Z., Mirjalili, S., Saremi, S., Faris, H., & Aljarah, I.. (2018). Grasshopper optimization algorithm for multi-objective optimization problems. Applied intelligence, 48(4), 805-820. doi:10.1007/s10489-017-1019-8
[BibTeX] [Abstract]

This work proposes a new multi-objective algorithm inspired from the navigation of grass hopper swarms in nature. A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone. A mechanism is then proposed to use the model in approximating the global optimum in a single-objective search space. Afterwards, an archive and target selection technique are integrated to the algorithm to estimate the Pareto optimal front for multi-objective problems. To benchmark the performance of the algorithm proposed, a set of diverse standard multi-objective test problems is utilized. The results are compared with the most well-regarded and recent algorithms in the literature of evolutionary multi-objective optimization using three performance indicators quantitatively and graphs qualitatively. The results show that the proposed algorithm is able to provide very competitive results in terms of accuracy of obtained Pareto optimal solutions and their distribution. © 2017, Springer Science+Business Media, LLC.

@Article{Mirjalili2018805,
author = {Mirjalili, S.Z. and Mirjalili, S. and Saremi, S. and Faris, H. and Aljarah, I.},
title = {Grasshopper optimization algorithm for multi-objective optimization problems},
journal = {Applied Intelligence},
year = {2018},
volume = {48},
number = {4},
pages = {805-820},
issn = {0924669X},
abstract = {This work proposes a new multi-objective algorithm inspired from the navigation of grass hopper swarms in nature. A mathematical model is first employed to model the interaction of individuals in the swam including attraction force, repulsion force, and comfort zone. A mechanism is then proposed to use the model in approximating the global optimum in a single-objective search space. Afterwards, an archive and target selection technique are integrated to the algorithm to estimate the Pareto optimal front for multi-objective problems. To benchmark the performance of the algorithm proposed, a set of diverse standard multi-objective test problems is utilized. The results are compared with the most well-regarded and recent algorithms in the literature of evolutionary multi-objective optimization using three performance indicators quantitatively and graphs qualitatively. The results show that the proposed algorithm is able to provide very competitive results in terms of accuracy of obtained Pareto optimal solutions and their distribution. © 2017, Springer Science+Business Media, LLC.},
coden = {APITE},
document_type = {Article},
doi = {10.1007/s10489-017-1019-8},
publisher = {Springer New York LLC},
source = {Scopus},
}

Shukri, S., Faris, H., Aljarah, I., Mirjalili, S., & Abraham, A.. (2018). Evolutionary static and dynamic clustering algorithms based on multi-verse optimizer. Engineering applications of artificial intelligence, 72, 54-66. doi:10.1016/j.engappai.2018.03.013
[BibTeX] [Abstract]

Clustering based on nature-inspired algorithms is considered as one of the fast growing areas that aims to benefit from such algorithms to formulate a clustering problem as an optimization problem. In this work, the search capabilities of a recent nature-inspired algorithm called Multi-verse Optimizer (MVO) is utilized to optimize clustering problems in two different approaches. The first one is a static clustering approach that works on a predefined number of clusters. The main objective of this approach is to maximize the distances between different clusters and to minimize the distances between the members in each cluster. In an attempt to overcome one of the major drawbacks of the traditional clustering algorithms, the second proposed approach is a dynamic clustering algorithm, in which the number of clusters is automatically detected without any prior information. The proposed approaches are tested using 12 real and artificial datasets and compared with several traditional and nature-inspired based clustering algorithms. The results show that static and dynamic MVO algorithms outperform the other clustering techniques on the majority of datasets. © 2018 Elsevier Ltd

@Article{Shukri201854,
author = {Shukri, S. and Faris, H. and Aljarah, I. and Mirjalili, S. and Abraham, A.},
title = {Evolutionary static and dynamic clustering algorithms based on multi-verse optimizer},
journal = {Engineering Applications of Artificial Intelligence},
year = {2018},
volume = {72},
pages = {54-66},
issn = {09521976},
abstract = {Clustering based on nature-inspired algorithms is considered as one of the fast growing areas that aims to benefit from such algorithms to formulate a clustering problem as an optimization problem. In this work, the search capabilities of a recent nature-inspired algorithm called Multi-verse Optimizer (MVO) is utilized to optimize clustering problems in two different approaches. The first one is a static clustering approach that works on a predefined number of clusters. The main objective of this approach is to maximize the distances between different clusters and to minimize the distances between the members in each cluster. In an attempt to overcome one of the major drawbacks of the traditional clustering algorithms, the second proposed approach is a dynamic clustering algorithm, in which the number of clusters is automatically detected without any prior information. The proposed approaches are tested using 12 real and artificial datasets and compared with several traditional and nature-inspired based clustering algorithms. The results show that static and dynamic MVO algorithms outperform the other clustering techniques on the majority of datasets. © 2018 Elsevier Ltd},
coden = {EAAIE},
document_type = {Article},
doi = {10.1016/j.engappai.2018.03.013},
publisher = {Elsevier Ltd},
source = {Scopus},
}

Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M.. (2017). Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Advances in engineering software, 114, 163-191. doi:10.1016/j.advengsoft.2017.07.002
[BibTeX] [Abstract]

This work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA) and Multi-objective Salp Swarm Algorithm (MSSA) for solving optimization problems with single and multiple objectives. The main inspiration of SSA and MSSA is the swarming behaviour of salps when navigating and foraging in oceans. These two algorithms are tested on several mathematical optimization functions to observe and confirm their effective behaviours in finding the optimal solutions for optimization problems. The results on the mathematical functions show that the SSA algorithm is able to improve the initial random solutions effectively and converge towards the optimum. The results of MSSA show that this algorithm can approximate Pareto optimal solutions with high convergence and coverage. The paper also considers solving several challenging and computationally expensive engineering design problems (e.g. airfoil design and marine propeller design) using SSA and MSSA. The results of the real case studies demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces. © 2017 Elsevier Ltd

@Article{Mirjalili2017163,
author = {Mirjalili, S. and Gandomi, A.H. and Mirjalili, S.Z. and Saremi, S. and Faris, H. and Mirjalili, S.M.},
title = {Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems},
journal = {Advances in Engineering Software},
year = {2017},
volume = {114},
pages = {163-191},
issn = {09659978},
abstract = {This work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA) and Multi-objective Salp Swarm Algorithm (MSSA) for solving optimization problems with single and multiple objectives. The main inspiration of SSA and MSSA is the swarming behaviour of salps when navigating and foraging in oceans. These two algorithms are tested on several mathematical optimization functions to observe and confirm their effective behaviours in finding the optimal solutions for optimization problems. The results on the mathematical functions show that the SSA algorithm is able to improve the initial random solutions effectively and converge towards the optimum. The results of MSSA show that this algorithm can approximate Pareto optimal solutions with high convergence and coverage. The paper also considers solving several challenging and computationally expensive engineering design problems (e.g. airfoil design and marine propeller design) using SSA and MSSA. The results of the real case studies demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces. © 2017 Elsevier Ltd},
coden = {AESOD},
document_type = {Article},
doi = {10.1016/j.advengsoft.2017.07.002},
publisher = {Elsevier Ltd},
source = {Scopus},
}

Faris, H., Aljarah, I., Al-Madi, N., & Mirjalili, S.. (2016). Optimizing the learning process of feedforward neural networks using lightning search algorithm. International journal on artificial intelligence tools, 25(6). doi:10.1142/S0218213016500330
[BibTeX] [Abstract]

Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets. © 2016 World Scientific Publishing Company.

@Article{Faris2016,
author = {Faris, H. and Aljarah, I. and Al-Madi, N. and Mirjalili, S.},
title = {Optimizing the Learning Process of Feedforward Neural Networks Using Lightning Search Algorithm},
journal = {International Journal on Artificial Intelligence Tools},
year = {2016},
volume = {25},
number = {6},
issn = {02182130},
abstract = {Evolutionary Neural Networks are proven to be beneficial in solving challenging datasets mainly due to the high local optima avoidance. Stochastic operators in such techniques reduce the probability of stagnation in local solutions and assist them to supersede conventional training algorithms such as Back Propagation (BP) and Levenberg-Marquardt (LM). According to the No-Free-Lunch (NFL), however, there is no optimization technique for solving all optimization problems. This means that a Neural Network trained by a new algorithm has the potential to solve a new set of problems or outperform the current techniques in solving existing problems. This motivates our attempts to investigate the efficiency of the recently proposed Evolutionary Algorithm called Lightning Search Algorithm (LSA) in training Neural Network for the first time in the literature. The LSA-based trainer is benchmarked on 16 popular medical diagnosis problems and compared to BP, LM, and 6 other evolutionary trainers. The quantitative and qualitative results show that the LSA algorithm is able to show not only better local solutions avoidance but also faster convergence speed compared to the other algorithms employed. In addition, the statistical test conducted proves that the LSA-based trainer is significantly superior in comparison with the current algorithms on the majority of datasets. © 2016 World Scientific Publishing Company.},
art_number = {1650033},
document_type = {Article},
doi = {10.1142/S0218213016500330},
publisher = {World Scientific Publishing Co. Pte Ltd},
source = {Scopus},
}

Faris, H., Aljarah, I., & Mirjalili, S.. (2016). Training feedforward neural networks using multi-verse optimizer for binary classification problems. Applied intelligence, 45(2), 322-332. doi:10.1007/s10489-016-0767-1
[BibTeX] [Abstract]

This paper employs the recently proposed nature-inspired algorithm called Multi-Verse Optimizer (MVO) for training the Multi-layer Perceptron (MLP) neural network. The new training approach is benchmarked and evaluated using nine different bio-medical datasets selected from the UCI machine learning repository. The results are compared to five classical and recent evolutionary metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), FireFly (FF) Algorithm and Cuckoo Search (CS). In addition, the results are compared with two well-regarded conventional gradient-based training methods: the conventional Back-Propagation (BP) and the Levenberg-Marquardt (LM) algorithms. The comparative study demonstrates that MVO is very competitive and outperforms other training algorithms in the majority of datasets in terms of improved local optima avoidance and convergence speed. © 2016, Springer Science+Business Media New York.

@Article{Faris2016322,
author = {Faris, H. and Aljarah, I. and Mirjalili, S.},
title = {Training feedforward neural networks using multi-verse optimizer for binary classification problems},
journal = {Applied Intelligence},
year = {2016},
volume = {45},
number = {2},
pages = {322-332},
issn = {0924669X},
abstract = {This paper employs the recently proposed nature-inspired algorithm called Multi-Verse Optimizer (MVO) for training the Multi-layer Perceptron (MLP) neural network. The new training approach is benchmarked and evaluated using nine different bio-medical datasets selected from the UCI machine learning repository. The results are compared to five classical and recent evolutionary metaheuristic algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), FireFly (FF) Algorithm and Cuckoo Search (CS). In addition, the results are compared with two well-regarded conventional gradient-based training methods: the conventional Back-Propagation (BP) and the Levenberg-Marquardt (LM) algorithms. The comparative study demonstrates that MVO is very competitive and outperforms other training algorithms in the majority of datasets in terms of improved local optima avoidance and convergence speed. © 2016, Springer Science+Business Media New York.},
coden = {APITE},
document_type = {Article},
doi = {10.1007/s10489-016-0767-1},
publisher = {Springer New York LLC},
source = {Scopus},
}

Book Chapters

Hassanat, A. B., Altarawneh, G., Tarawneh, A. S., Faris, H., Alhasanat, M. B., de Voogt, A., Al-Rawashdeh, B., Alshamaileh, M., & Prasath, S. V.. (2023). On computerizing the ancient game of ṭ\=ab. In Research anthology on game design, development, usage, and social impact (pp. 1321–1344). Igi global.
[BibTeX]

@incollection{hassanat2023computerizing,
title={On Computerizing the Ancient Game of Ṭ{\=a}b},
author={Hassanat, Ahmad B and Altarawneh, Ghada and Tarawneh, Ahmad S and Faris, Hossam and Alhasanat, Mahmoud B and de Voogt, Alex and Al-Rawashdeh, Baker and Alshamaileh, Mohammed and Prasath, Surya VB},
booktitle={Research Anthology on Game Design, Development, Usage, and Social Impact},
pages={1321--1344},
year={2023},
publisher={IGI Global}
}

Qaddoura, R., & El-Emam, N. N.. (2021). Privacy preservation tools and techniques in artificial intelligence. In Cybersecurity: ambient technologies, iot, and industry 4.0 implications (pp. 161). Crc press.
[BibTeX]

@incollection{qaddoura2021privacy,
title={Privacy Preservation Tools and Techniques in Artificial Intelligence},
author={Qaddoura, Raneem and El-Emam, Nameer N},
booktitle={Cybersecurity: Ambient Technologies, IoT, and Industry 4.0 Implications},
pages={161},
year={2021},
publisher={CRC Press}
}

Qaddoura, R., Aljarah, I., Faris, H., & Almomani, I.. (2021). A classification approach based on evolutionary clustering and its application for ransomware detection. Evolutionary data clustering: algorithms and applications, 237-248.
[BibTeX]

@article{qaddouraclassification,
title={A Classification Approach Based on Evolutionary Clustering and Its Application for Ransomware Detection},
author={Qaddoura, Raneem and Aljarah, Ibrahim and Faris, Hossam and Almomani, Iman},
journal={Evolutionary Data Clustering: Algorithms and Applications},
pages={237-248},
year={2021},
publisher={Springer Nature}
}

Qaddoura, R., Aljarah, I., Faris, H., & Mirjalili, S.. (2021). A grey wolf-based clustering algorithm for medical diagnosis problems. Evolutionary data clustering: algorithms and applications, 73-87.
[BibTeX]

@article{qaddouragrey,
title={A Grey Wolf-Based Clustering Algorithm for Medical Diagnosis Problems},
author={Qaddoura, Raneem and Aljarah, Ibrahim and Faris, Hossam and Mirjalili, Seyedali},
journal={Evolutionary Data Clustering: Algorithms and Applications},
pages={73-87},
year={2021},
publisher={Springer Nature}
}

Yaghi, R. I., Faris, H., Aljarah, I., Ala’M, A., Heidari, A. A., & Mirjalili, S.. (2020). Link prediction using evolutionary neural network models. In Evolutionary machine learning techniques (pp. 85–111). Springer.
[BibTeX]

@incollection{yaghi2020link,
title={Link Prediction Using Evolutionary Neural Network Models},
author={Yaghi, Rawan I and Faris, Hossam and Aljarah, Ibrahim and Ala’M, Al-Zoubi and Heidari, Ali Asghar and Mirjalili, Seyedali},
booktitle={Evolutionary Machine Learning Techniques},
pages={85--111},
year={2020},
publisher={Springer}
}

Namous, F., Faris, H., Heidari, A. A., Khalafat, M., Alkhawaldeh, R. S., & Ghatasheh, N.. (2020). Evolutionary and swarm-based feature selection for imbalanced data classification. In Evolutionary machine learning techniques (pp. 231–250). Springer.
[BibTeX]

@incollection{namous2020evolutionary,
title={Evolutionary and Swarm-Based Feature Selection for Imbalanced Data Classification},
author={Namous, Feras and Faris, Hossam and Heidari, Ali Asghar and Khalafat, Monther and Alkhawaldeh, Rami S and Ghatasheh, Nazeeh},
booktitle={Evolutionary Machine Learning Techniques},
pages={231--250},
year={2020},
publisher={Springer}
}

Faris, R., Faris, H., AL-Oqla, F. M., Dalalah, D., & others. (2020). Evolving genetic programming models for predicting quantities of adhesive wear in low and medium carbon steel. In Evolutionary machine learning techniques (pp. 113–127). Springer.
[BibTeX]

@incollection{faris2020evolving,
title={Evolving Genetic Programming Models for Predicting Quantities of Adhesive Wear in Low and Medium Carbon Steel},
author={Faris, Rana and Faris, Hossam and AL-Oqla, Faris M and Dalalah, Doraid and others},
booktitle={Evolutionary Machine Learning Techniques},
pages={113--127},
year={2020},
publisher={Springer}
}

Mirjalili, S., Faris, H., & Aljarah, I.. (2020). Introduction to evolutionary machine learning techniques. In Evolutionary machine learning techniques (pp. 1–7). Springer.
[BibTeX]

@incollection{mirjalili2020introduction,
title={Introduction to Evolutionary Machine Learning Techniques},
author={Mirjalili, Seyedali and Faris, Hossam and Aljarah, Ibrahim},
booktitle={Evolutionary Machine Learning Techniques},
pages={1--7},
year={2020},
publisher={Springer}
}

Habib, M., Aljarah, I., Faris, H., & Mirjalili, S.. (2020). Multi-objective particle swarm optimization: theory, literature review, and application in feature selection for medical diagnosis. In Evolutionary machine learning techniques (pp. 175–201). Springer.
[BibTeX]

@incollection{habib2020multi,
title={Multi-objective Particle Swarm Optimization: Theory, Literature Review, and Application in Feature Selection for Medical Diagnosis},
author={Habib, Maria and Aljarah, Ibrahim and Faris, Hossam and Mirjalili, Seyedali},
booktitle={Evolutionary Machine Learning Techniques},
pages={175--201},
year={2020},
publisher={Springer}
}

Ala’M, A., Heidari, A. A., Habib, M., Faris, H., Aljarah, I., & Hassonah, M. A.. (2020). Salp chain-based optimization of support vector machines and feature weighting for medical diagnostic information systems. In Evolutionary machine learning techniques (pp. 11–34). Springer.
[BibTeX]

@incollection{ala2020salp,
title={Salp Chain-Based Optimization of Support Vector Machines and Feature Weighting for Medical Diagnostic Information Systems},
author={Ala’M, Al-Zoubi and Heidari, Ali Asghar and Habib, Maria and Faris, Hossam and Aljarah, Ibrahim and Hassonah, Mohammad A},
booktitle={Evolutionary Machine Learning Techniques},
pages={11--34},
year={2020},
publisher={Springer}
}

Habib, M., Aljarah, I., Faris, H., & Mirjalili, S.. (2020). Multi-objective particle swarm optimization for botnet detection in internet of things. In Evolutionary machine learning techniques (pp. 203–229). Springer.
[BibTeX]

@incollection{habib2020multi,
title={Multi-objective Particle Swarm Optimization for Botnet Detection in Internet of Things},
author={Habib, Maria and Aljarah, Ibrahim and Faris, Hossam and Mirjalili, Seyedali},
booktitle={Evolutionary Machine Learning Techniques},
pages={203--229},
year={2020},
publisher={Springer}
}

Aljarah, I., Mafarja, M., Heidari, A. A., Faris, H., & Mirjalili, S.. (2020). Multi-verse optimizer: theory, literature review, and application in data clustering. Studies in computational intelligence, 811, 123-141. doi:10.1007/978-3-030-12127-3_8
[BibTeX] [Abstract]

Multi-verse optimizer (MVO) is considered one of the recent metaheuristics. MVO algorithm is inspired from the theory of multi-verse in astrophysics. This chapter discusses the theoretical foundation, operations, and main strengths behind this algorithm. Moreover, a detailed literature review is conducted to discuss several variants of the MVO algorithm. In addition, the main applications of MVO are also thoroughly described. The chapter also investigates the application of the MVO algorithm in tackling data clustering tasks. The proposed algorithm is benchmarked by several datasets, qualitatively and quantitatively. The experimental results show that the proposed MVO-based clustering algorithm outperforms several similar algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA) in terms of clustering purity, clustering homogeneity, and clustering completeness. © Springer Nature Switzerland AG 2020.

@Article{Aljarah2020123,
author = {Aljarah, I. and Mafarja, M. and Heidari, A.A. and Faris, H. and Mirjalili, S.},
title = {Multi-verse optimizer: Theory, literature review, and application in data clustering},
journal = {Studies in Computational Intelligence},
year = {2020},
volume = {811},
pages = {123-141},
issn = {1860949X},
abstract = {Multi-verse optimizer (MVO) is considered one of the recent metaheuristics. MVO algorithm is inspired from the theory of multi-verse in astrophysics. This chapter discusses the theoretical foundation, operations, and main strengths behind this algorithm. Moreover, a detailed literature review is conducted to discuss several variants of the MVO algorithm. In addition, the main applications of MVO are also thoroughly described. The chapter also investigates the application of the MVO algorithm in tackling data clustering tasks. The proposed algorithm is benchmarked by several datasets, qualitatively and quantitatively. The experimental results show that the proposed MVO-based clustering algorithm outperforms several similar algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA) in terms of clustering purity, clustering homogeneity, and clustering completeness. © Springer Nature Switzerland AG 2020.},
document_type = {Book Chapter},
doi = {10.1007/978-3-030-12127-3_8},
publisher = {Springer Verlag},
source = {Scopus},
}

Faris, H., Mirjalili, S., Aljarah, I., Mafarja, M., & Heidari, A. A.. (2020). Salp swarm algorithm: theory, literature review, and application in extreme learning machines. Studies in computational intelligence, 811, 185-199. doi:10.1007/978-3-030-12127-3_11
[BibTeX] [Abstract]

Salp Swarm Algorithm (SSA) is a recent metaheuristic inspired by the swarming behavior of salps in oceans. SSA has demonstrated its efficiency in various applications since its proposal. In this chapter, the algorithm, its operators, and some of the remarkable works that utilized this algorithm are presented. Moreover, the application of SSA in optimizing the Extreme Learning Machine (ELM) is investigated to improve its accuracy and overcome the shortcomings of its conventional training method. For verification, the algorithm is tested on 10 benchmark datasets and compared to two other well-known training methods. Comparison results show that SSA based training methods outperforms other methods in terms of accuracy and is very competitive in terms of prediction stability. © Springer Nature Switzerland AG 2020.

@Article{Faris2020185,
author = {Faris, H. and Mirjalili, S. and Aljarah, I. and Mafarja, M. and Heidari, A.A.},
title = {Salp swarm algorithm: Theory, literature review, and application in extreme learning machines},
journal = {Studies in Computational Intelligence},
year = {2020},
volume = {811},
pages = {185-199},
issn = {1860949X},
abstract = {Salp Swarm Algorithm (SSA) is a recent metaheuristic inspired by the swarming behavior of salps in oceans. SSA has demonstrated its efficiency in various applications since its proposal. In this chapter, the algorithm, its operators, and some of the remarkable works that utilized this algorithm are presented. Moreover, the application of SSA in optimizing the Extreme Learning Machine (ELM) is investigated to improve its accuracy and overcome the shortcomings of its conventional training method. For verification, the algorithm is tested on 10 benchmark datasets and compared to two other well-known training methods. Comparison results show that SSA based training methods outperforms other methods in terms of accuracy and is very competitive in terms of prediction stability. © Springer Nature Switzerland AG 2020.},
document_type = {Book Chapter},
doi = {10.1007/978-3-030-12127-3_11},
publisher = {Springer Verlag},
source = {Scopus},
}

Heidari, A. A., Faris, H., Mirjalili, S., Aljarah, I., & Mafarja, M.. (2020). Ant lion optimizer: theory, literature review, and application in multi-layer perceptron neural networks. Studies in computational intelligence, 811, 23-46. doi:10.1007/978-3-030-12127-3_3
[BibTeX] [Abstract]

This chapter proposes an efficient hybrid training technique (ALOMLP) based on the Ant Lion Optimizer (ALO) to be utilized in dealing with Multi-Layer Perceptrons (MLPs) neural networks. ALO is a well-regarded swarm-based meta-heuristic inspired by the intelligent hunting tricks of antlions in nature. In this chapter, the theoretical backgrounds of ALO are explained in details first. Then, a comprehensive literature review is provided based on recent well-established works from 2015 to 2018. In addition, a convenient encoding scheme is presented and the objective formula is defined, mathematically. The proposed training model based on ALO algorithm is substantiated on sixteen standard datasets. The efficiency of ALO is compared with differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and population-based incremental learning (PBIL) in terms of best, worst, average, and median accuracies. Furthermore, the convergence propensities are monitored and analyzed for all competitors. The experiments show that the ALOMLP outperforms GA, PBIL, DE, and PSO in classifying the majority of datasets and provides improved accuracy results and convergence rates. © Springer Nature Switzerland AG 2020.

@Article{Heidari202023,
author = {Heidari, A.A. and Faris, H. and Mirjalili, S. and Aljarah, I. and Mafarja, M.},
title = {Ant lion optimizer: Theory, literature review, and application in multi-layer perceptron neural networks},
journal = {Studies in Computational Intelligence},
year = {2020},
volume = {811},
pages = {23-46},
issn = {1860949X},
abstract = {This chapter proposes an efficient hybrid training technique (ALOMLP) based on the Ant Lion Optimizer (ALO) to be utilized in dealing with Multi-Layer Perceptrons (MLPs) neural networks. ALO is a well-regarded swarm-based meta-heuristic inspired by the intelligent hunting tricks of antlions in nature. In this chapter, the theoretical backgrounds of ALO are explained in details first. Then, a comprehensive literature review is provided based on recent well-established works from 2015 to 2018. In addition, a convenient encoding scheme is presented and the objective formula is defined, mathematically. The proposed training model based on ALO algorithm is substantiated on sixteen standard datasets. The efficiency of ALO is compared with differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and population-based incremental learning (PBIL) in terms of best, worst, average, and median accuracies. Furthermore, the convergence propensities are monitored and analyzed for all competitors. The experiments show that the ALOMLP outperforms GA, PBIL, DE, and PSO in classifying the majority of datasets and provides improved accuracy results and convergence rates. © Springer Nature Switzerland AG 2020.},
document_type = {Book Chapter},
doi = {10.1007/978-3-030-12127-3_3},
publisher = {Springer Verlag},
source = {Scopus},
}

Mafarja, M., Heidari, A. A., Faris, H., Mirjalili, S., & Aljarah, I.. (2020). Dragonfly algorithm: theory, literature review, and application in feature selection. Studies in computational intelligence, 811, 47-67. doi:10.1007/978-3-030-12127-3_4
[BibTeX] [Abstract]

In this chapter, a wrapper-based feature selection algorithm is designed and substantiated based on the binary variant of Dragonfly Algorithm (BDA). DA is a successful, well-established metaheuristic that revealed superior efficacy in dealing with various optimization problems including feature selection. In this chapter we are going first present the inspirations and methamatical modeds of DA in details. Then, the performance of this algorithm is tested on a special type of datasets that contain a huge number of features with low number of samples. This type of datasets makes the optimization process harder, because of the large search space, and the lack of adequate samples to train the model. The experimental results showed the ability of DA to deal with this type of datasets better than other optimizers in the literature. Moreover, an extensive literature review for the DA is provided in this chapter. © Springer Nature Switzerland AG 2020.

@Article{Mafarja202047,
author = {Mafarja, M. and Heidari, A.A. and Faris, H. and Mirjalili, S. and Aljarah, I.},
title = {Dragonfly algorithm: Theory, literature review, and application in feature selection},
journal = {Studies in Computational Intelligence},
year = {2020},
volume = {811},
pages = {47-67},
issn = {1860949X},
abstract = {In this chapter, a wrapper-based feature selection algorithm is designed and substantiated based on the binary variant of Dragonfly Algorithm (BDA). DA is a successful, well-established metaheuristic that revealed superior efficacy in dealing with various optimization problems including feature selection. In this chapter we are going first present the inspirations and methamatical modeds of DA in details. Then, the performance of this algorithm is tested on a special type of datasets that contain a huge number of features with low number of samples. This type of datasets makes the optimization process harder, because of the large search space, and the lack of adequate samples to train the model. The experimental results showed the ability of DA to deal with this type of datasets better than other optimizers in the literature. Moreover, an extensive literature review for the DA is provided in this chapter. © Springer Nature Switzerland AG 2020.},
document_type = {Book Chapter},
doi = {10.1007/978-3-030-12127-3_4},
publisher = {Springer Verlag},
source = {Scopus},
}

Mirjalili, S., Aljarah, I., Mafarja, M., Heidari, A. A., & Faris, H.. (2020). Grey wolf optimizer: theory, literature review, and application in computational fluid dynamics problems. Studies in computational intelligence, 811, 87-105. doi:10.1007/978-3-030-12127-3_6
[BibTeX] [Abstract]

This chapter first discusses inspirations, methematicam models, and an in-depth literature of the recently proposed Grey Wolf Optimizer (GWO). Then, several experiments are conducted to analyze and benchmark the performance of different variants and improvements of this algorithm. The chapter also investigates the application of the GWO variants in finding an optimal design for a ship propeller. © Springer Nature Switzerland AG 2020.

@Article{Mirjalili202087,
author = {Mirjalili, S. and Aljarah, I. and Mafarja, M. and Heidari, A.A. and Faris, H.},
title = {Grey wolf optimizer: Theory, literature review, and application in computational fluid dynamics problems},
journal = {Studies in Computational Intelligence},
year = {2020},
volume = {811},
pages = {87-105},
issn = {1860949X},
abstract = {This chapter first discusses inspirations, methematicam models, and an in-depth literature of the recently proposed Grey Wolf Optimizer (GWO). Then, several experiments are conducted to analyze and benchmark the performance of different variants and improvements of this algorithm. The chapter also investigates the application of the GWO variants in finding an optimal design for a ship propeller. © Springer Nature Switzerland AG 2020.},
document_type = {Book Chapter},
doi = {10.1007/978-3-030-12127-3_6},
publisher = {Springer Verlag},
source = {Scopus},
}

Mirjalili, S., Song Dong, J., Sadiq, A. S., & Faris, H.. (2020). Genetic algorithm: theory, literature review, and application in image reconstruction. Studies in computational intelligence, 811, 69-85. doi:10.1007/978-3-030-12127-3_5
[BibTeX] [Abstract]

Genetic Algorithm (GA) is one of the most well-regarded evolutionary algorithms in the history. This algorithm mimics Darwinian theory of survival of the fittest in nature. This chapter presents the most fundamental concepts, operators, and mathematical models of this algorithm. The most popular improvements in the main component of this algorithm (selection, crossover, and mutation) are given too. The chapter also investigates the application of this technique in the field of image processing. In fact, the GA algorithm is employed to reconstruct a binary image from a completely random image. © Springer Nature Switzerland AG 2020.

@Article{Mirjalili202069,
author = {Mirjalili, S. and Song Dong, J. and Sadiq, A.S. and Faris, H.},
title = {Genetic algorithm: Theory, literature review, and application in image reconstruction},
journal = {Studies in Computational Intelligence},
year = {2020},
volume = {811},
pages = {69-85},
issn = {1860949X},
abstract = {Genetic Algorithm (GA) is one of the most well-regarded evolutionary algorithms in the history. This algorithm mimics Darwinian theory of survival of the fittest in nature. This chapter presents the most fundamental concepts, operators, and mathematical models of this algorithm. The most popular improvements in the main component of this algorithm (selection, crossover, and mutation) are given too. The chapter also investigates the application of this technique in the field of image processing. In fact, the GA algorithm is employed to reconstruct a binary image from a completely random image. © Springer Nature Switzerland AG 2020.},
document_type = {Book Chapter},
doi = {10.1007/978-3-030-12127-3_5},
publisher = {Springer Verlag},
source = {Scopus},
}

Faris, H., Aljarah, I., & Mirjalili, S.. (2017). Evolving radial basis function networks using moth-flame optimizer Elsevier inc.. doi:10.1016/B978-0-12-811318-9.00028-4
[BibTeX] [Abstract]

This book chapter proposes a new training algorithms for Radial Basis Function (RBF) using a recently proposed optimization algorithm called Moth-Flame Optimizer (MFO). After formulating MFO as RBFN trainer, seven standard binary classifications are employed as case studies. The MFO-based trainer is compared with Particle Swarm Algorithm (PSO), Genetic Algorithm (GA), Bat Algorithm (BA), and newrb. The results show that the proposed trainer is able to show superior results on the majority of case studies. The observation of convergence behavior proves that this new trainer benefits from accelerating convergence speed as well. © 2017 Elsevier Inc. All rights reserved.

@Book{Faris2017537,
title = {Evolving Radial Basis Function Networks Using Moth-Flame Optimizer},
publisher = {Elsevier Inc.},
year = {2017},
author = {Faris, H. and Aljarah, I. and Mirjalili, S.},
isbn = {9780128113196; 9780128113189},
abstract = {This book chapter proposes a new training algorithms for Radial Basis Function (RBF) using a recently proposed optimization algorithm called Moth-Flame Optimizer (MFO). After formulating MFO as RBFN trainer, seven standard binary classifications are employed as case studies. The MFO-based trainer is compared with Particle Swarm Algorithm (PSO), Genetic Algorithm (GA), Bat Algorithm (BA), and newrb. The results show that the proposed trainer is able to show superior results on the majority of case studies. The observation of convergence behavior proves that this new trainer benefits from accelerating convergence speed as well. © 2017 Elsevier Inc. All rights reserved.},
document_type = {Book Chapter},
doi = {10.1016/B978-0-12-811318-9.00028-4},
journal = {Handbook of Neural Computation},
pages = {537-550},
source = {Scopus},
}

Conferences

Qaddoura, R., & Biltawi, M.. (2022). Improving fraud detection in an imbalanced class distribution using different oversampling techniques. Paper presented at the Engineering international conference on electrical, energy, and artificial intelligence (eiceeai).
[BibTeX]

@inproceedings{qaddoura2022improving,
title={Improving Fraud Detection in an Imbalanced Class Distribution using Different Oversampling Techniques},
author={Qaddoura, Raneem and Biltawi, Mariam},
booktitle={Engineering International Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)},
year={2022}
}

Al-Farouqi, N., Al-Athami, M., & Qaddoura, R.. (2022). Predicting autism disorder of an imbalanced dataset using neural network. Paper presented at the International conference on computational intelligence and communication networks.
[BibTeX]

@inproceedings{al2022predicting,
title={Predicting Autism Disorder of an Imbalanced Dataset Using Neural Network},
author={Al-Farouqi, Najwan and Al-Athami, Muayad and Qaddoura, Raneem},
booktitle={International Conference on Computational Intelligence and Communication Networks},
year={2022}
}

Biltawi, M., & Qaddoura, R.. (2022). The impact of feature selection on the regression task for life expectancy prediction. Paper presented at the The international conference on emerging trends in computing and engineering applications.
[BibTeX]

@inproceedings{biltawi2022impact,
title={The Impact of Feature Selection on the Regression Task for Life Expectancy Prediction},
author={Biltawi, Mariam and Qaddoura, Raneem},
booktitle={The International Conference on Emerging Trends in Computing and Engineering Applications},
year={2022}
}

Qaddoura, R., Bani Younes, M., & Boukerche, A.. (2022). Machine learning based prediction with parameters tuning of multi-label real road vehicles characteristics. Paper presented at the Proceedings of the 19th acm international symposium on performance evaluation of wireless ad hoc, sensor, & ubiquitous networks.
[BibTeX]

@inproceedings{qaddoura2022machine,
title={Machine learning based prediction with parameters tuning of multi-label real road vehicles characteristics},
author={Qaddoura, Raneem and Bani Younes, Maram and Boukerche, Azzedine},
booktitle={Proceedings of the 19th ACM International Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, \& Ubiquitous Networks},
pages={41--47},
year={2022}
}

Qaddoura, R., Bani Younes, M., & Boukerche, A.. (2021). Predicting traffic characteristics of real road scenarios in jordan and gulf region. Paper presented at the Proceedings of the 17th acm symposium on qos and security for wireless and mobile networks.
[BibTeX]

@inproceedings{qaddoura2021predicting,
title={Predicting traffic characteristics of real road scenarios in Jordan and Gulf region},
author={Qaddoura, Raneem and Bani Younes, Maram and Boukerche, Azzedine},
booktitle={Proceedings of the 17th ACM symposium on QoS and security for wireless and mobile networks},
pages={115--121},
year={2021}
}

Qaddoura, R., Ala’M, A., Almomani, I., & Faris, H.. (2021). Predicting different types of imbalanced intrusion activities based on a multi-stage deep learning approach. Paper presented at the 2021 international conference on information technology (icit).
[BibTeX]

@inproceedings{qaddoura2021predicting,
title={Predicting different types of imbalanced intrusion activities based on a multi-stage deep learning approach},
author={Qaddoura, Raneem and Ala’M, Al-Zoubi and Almomani, Iman and Faris, Hossam},
booktitle={2021 International Conference on Information Technology (ICIT)},
pages={858--863},
year={2021},
organization={IEEE}
}

Dang, A. T., Qaddoura, R., Al-Zoubi, A., Faris, H., & Castillo, P. A.. (2022). Evocc: an open-source classification-based nature-inspired optimization clustering framework in python. Paper presented at the International conference on the applications of evolutionary computation (part of evostar).
[BibTeX]

@inproceedings{dang2022evocc,
title={EvoCC: An Open-Source Classification-Based Nature-Inspired Optimization Clustering Framework in Python},
author={Dang, Anh T and Qaddoura, Raneem and Al-Zoubi, Ala’M and Faris, Hossam and Castillo, Pedro A},
booktitle={International Conference on the Applications of Evolutionary Computation (Part of EvoStar)},
pages={77--92},
year={2022},
organization={Springer}
}

Khurma, R. A., Aljarah, I., Castillo, P. A., & Sabri, K. E.. (2022). An enhanced opposition-based evolutionary feature selection approach. Paper presented at the International conference on the applications of evolutionary computation (part of evostar).
[BibTeX]

@inproceedings{khurma2022enhanced,
title={An Enhanced Opposition-Based Evolutionary Feature Selection Approach},
author={Khurma, Ruba Abu and Aljarah, Ibrahim and Castillo, Pedro A and Sabri, Khair Eddin},
booktitle={International Conference on the Applications of Evolutionary Computation (Part of EvoStar)},
pages={3--14},
year={2022},
organization={Springer}
}

Khurma, R. A., Awadallah, M. A., & Aljarah, I.. (2021). Binary harris hawks optimisation filter based approach for feature selection. Paper presented at the 2021 palestinian international conference on information and communication technology (picict).
[BibTeX]

@inproceedings{khurma2021binary,
title={Binary Harris Hawks Optimisation Filter Based Approach for Feature Selection},
author={Khurma, Ruba Abu and Awadallah, Mohammed A and Aljarah, Ibrahim},
booktitle={2021 Palestinian International Conference on Information and Communication Technology (PICICT)},
pages={59--64},
year={2021},
organization={IEEE}
}

Qaddoura, R., Faris, H., Aljarah, I., Merelo, J., & Castillo, P. A.. Empirical evaluation of distance measures for nearest point with indexing ratio clustering algorithm. .
[BibTeX]

@Conference{qaddouraempirical,
title={Empirical Evaluation of Distance Measures for Nearest Point with Indexing Ratio Clustering Algorithm},
author={Qaddoura, Raneem and Faris, Hossam and Aljarah, Ibrahim and Merelo, JJ and Castillo, Pedro A}
}

Qaddoura, R., Faris, H., Aljarah, I., & Castillo, P. A.. (2020). Evocluster: an open-source nature-inspired optimization clustering framework in python. Paper presented at the International conference on the applications of evolutionary computation (part of evostar). doi:10.1007/978-3-030-43722-0_2
[BibTeX] [Abstract]

EvoCluster is an open source and cross-platform framework implemented in Python which includes the most well-known and recent nature-inspired metaheuristic optimizers that are customized to perform partitional clustering tasks. The goal of this framework is to provide a user-friendly and customizable implementation of the metaheuristic based clustering algorithms which can be utilized by experienced and non-experienced users for different applications. The framework can also be used by researchers who can benefit from the implementation of the metaheuristic optimizers for their research studies. EvoCluster can be extended by designing other optimizers, including more objective functions, adding other evaluation measures, and using more data sets. The current implementation of the framework includes ten metaheristic optimizers, thirty datasets, five objective functions, and twelve evaluation measures. The source code of EvoCluster is publicly available at (http://evo-ml.com/2019/10/25/evocluster/).

@inproceedings{qaddoura2020evocluster,
title={EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework in Python},
author={Qaddoura, Raneem and Faris, Hossam and Aljarah, Ibrahim and Castillo, Pedro A},
booktitle={International Conference on the Applications of Evolutionary Computation (Part of EvoStar)},
pages={20--36},
year={2020},
organization={Springer},
doi = {10.1007/978-3-030-43722-0_2},
abstract = {EvoCluster is an open source and cross-platform framework implemented in Python which includes the most well-known and recent nature-inspired metaheuristic optimizers that are customized to perform partitional clustering tasks. The goal of this framework is to provide a user-friendly and customizable implementation of the metaheuristic based clustering algorithms which can be utilized by experienced and non-experienced users for different applications. The framework can also be used by researchers who can benefit from the implementation of the metaheuristic optimizers for their research studies. EvoCluster can be extended by designing other optimizers, including more objective functions, adding other evaluation measures, and using more data sets. The current implementation of the framework includes ten metaheristic optimizers, thirty datasets, five objective functions, and twelve evaluation measures. The source code of EvoCluster is publicly available at (http://evo-ml.com/2019/10/25/evocluster/).}
}

Ahmed, S., Mafarja, M., Faris, H., & Aljarah, I.. (2018). Feature selection using salp swarm algorithm with chaos. . doi:10.1145/3206185.3206198
[BibTeX] [Abstract]

The performance of classification algorithms is highly sensitive to the data dimensionality. High dimensionality may cause many problems to a classifier like overfitting and high computational time. Feature selection (FS) is a key solution to both problems. It aims to reduce the number of features by removing the irrelevant, redundant and noisy data, while trying to keep an acceptable classification accuracy. FS can be formulated as an optimization problem. Metaheuristic algorithms have shown superior performance in solving this type of problems. In this work, a chaotic version of Salp Swarm Algorithm (SSA) is proposed, which is considered one of the recent metaheuristic algorithms. The proposed approach is applied for the first time on feature selection problems. Four different chaotic maps are used to control the balance between the exploration and exploitation in the proposed approach. The proposed approaches are evaluated using twelve real datasets. The comparative results shows that the chaotic maps significantly enhances the performance of the SSA algorithm and outperforms other similar approaches in the literature. © 2018 Association for Computing Machinery.

@Conference{Ahmed201865,
author = {Ahmed, S. and Mafarja, M. and Faris, H. and Aljarah, I.},
title = {Feature selection using salp swarm algorithm with chaos},
year = {2018},
pages = {65-69},
publisher = {Association for Computing Machinery},
abstract = {The performance of classification algorithms is highly sensitive to the data dimensionality. High dimensionality may cause many problems to a classifier like overfitting and high computational time. Feature selection (FS) is a key solution to both problems. It aims to reduce the number of features by removing the irrelevant, redundant and noisy data, while trying to keep an acceptable classification accuracy. FS can be formulated as an optimization problem. Metaheuristic algorithms have shown superior performance in solving this type of problems. In this work, a chaotic version of Salp Swarm Algorithm (SSA) is proposed, which is considered one of the recent metaheuristic algorithms. The proposed approach is applied for the first time on feature selection problems. Four different chaotic maps are used to control the balance between the exploration and exploitation in the proposed approach. The proposed approaches are evaluated using twelve real datasets. The comparative results shows that the chaotic maps significantly enhances the performance of the SSA algorithm and outperforms other similar approaches in the literature. © 2018 Association for Computing Machinery.},
document_type = {Conference Paper},
doi = {10.1145/3206185.3206198},
isbn = {9781450364126},
journal = {ACM International Conference Proceeding Series},
source = {Scopus},
}

Al Shorman, A. R., Faris, H., Castillo, P. A., Merelo, J. J., & Al-Madi, N.. (2018). The influence of input data standardization methods on the prediction accuracy of genetic programming generated classifiers. .
[BibTeX] [Abstract] [Download PDF]

Genetic programming (GP) is a powerful classification technique. It is interpretable and it can dynamically build very complex expressions that maximize or minimize some fitness functions. It has a capacity to model very complex problems in the area of Machine Learning, Data Mining and Pattern Recognition. Nevertheless, GP has a high computational complexity time. On the other side, data standardization is one of the most important pre-processing steps in machine learning. The purpose of this step is to unify the scale of all input features to have equal contribution to the model. The objective of this paper is to investigate the influence of input data standardization methods on GP, and how it affects its prediction accuracy. Six different methods of input data standardization were checked in order to determine which one allows to achieve the most accurate result with lowest computational cost. The simulations have been implemented on ten benchmarked datasets with three different scenarios (varying the population size and number of generations). The results showed that the computational efficiency of GP is highly enhanced when coupled with some standardization methods, specifically Min-Max method for scenario I and Vector method for scenario II, and scenario III. Whereas, Manhattan and Z-Score methods had the worst results for all three scenarios. Copyright 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

@Conference{AlShorman201879,
author = {Al Shorman, A.R. and Faris, H. and Castillo, P.A. and Merelo, J.J. and Al-Madi, N.},
title = {The influence of input data standardization methods on the prediction accuracy of genetic programming generated classifiers},
year = {2018},
pages = {79-85},
publisher = {SciTePress},
abstract = {Genetic programming (GP) is a powerful classification technique. It is interpretable and it can dynamically build very complex expressions that maximize or minimize some fitness functions. It has a capacity to model very complex problems in the area of Machine Learning, Data Mining and Pattern Recognition. Nevertheless, GP has a high computational complexity time. On the other side, data standardization is one of the most important pre-processing steps in machine learning. The purpose of this step is to unify the scale of all input features to have equal contribution to the model. The objective of this paper is to investigate the influence of input data standardization methods on GP, and how it affects its prediction accuracy. Six different methods of input data standardization were checked in order to determine which one allows to achieve the most accurate result with lowest computational cost. The simulations have been implemented on ten benchmarked datasets with three different scenarios (varying the population size and number of generations). The results showed that the computational efficiency of GP is highly enhanced when coupled with some standardization methods, specifically Min-Max method for scenario I and Vector method for scenario II, and scenario III. Whereas, Manhattan and Z-Score methods had the worst results for all three scenarios. Copyright 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.},
document_type = {Conference Paper},
url = {http://evo-ml.com/wp-content/uploads/2019/08/AlShorman201879.pdf},
isbn = {9789897583278},
journal = {IJCCI 2018 - Proceedings of the 10th International Joint Conference on Computational Intelligence},
source = {Scopus},
}

Barham, R., & Aljarah, I.. (2018). Link prediction based on whale optimization algorithm. . doi:10.1109/ICTCS.2017.41
[BibTeX] [Abstract]

Link prediction problem has a wide variety of useful applications in diverse fields. For example, in bio-informatics, such as protein-protein interaction metabolic and diseases-gene networks, there are links between nodes, which indicate they have an interaction relationship. This paper presents a possible solution to the well known link prediction problem using a Whale Optimization algorithm (WOA), which is considered as one of the recent optimization algorithms. The link prediction problem is formulated as an optimization problem to predict the links in any type of the networks. Experimental results of the proposed algorithm (WOA-LP) on a number of real networks are good evidence that the proposed approach can enhance the link prediction accuracy. © 2017 IEEE.

@Conference{Barham201855,
author = {Barham, R. and Aljarah, I.},
title = {Link Prediction Based on Whale Optimization Algorithm},
year = {2018},
volume = {2018-January},
pages = {55-60},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Link prediction problem has a wide variety of useful applications in diverse fields. For example, in bio-informatics, such as protein-protein interaction metabolic and diseases-gene networks, there are links between nodes, which indicate they have an interaction relationship. This paper presents a possible solution to the well known link prediction problem using a Whale Optimization algorithm (WOA), which is considered as one of the recent optimization algorithms. The link prediction problem is formulated as an optimization problem to predict the links in any type of the networks. Experimental results of the proposed algorithm (WOA-LP) on a number of real networks are good evidence that the proposed approach can enhance the link prediction accuracy. © 2017 IEEE.},
document_type = {Conference Paper},
doi = {10.1109/ICTCS.2017.41},
isbn = {9781538605271},
journal = {Proceedings - 2017 International Conference on New Trends in Computing Sciences, ICTCS 2017},
source = {Scopus},
}

Hijawi, W., Alqatawna, J., & Faris, H.. (2018). Toward a detection framework for android botnet. . doi:10.1109/ICTCS.2017.48
[BibTeX] [Abstract]

Android is one of the most popular and widespread operating systems for smartphones. It has several millions of applications that are published at either official or unofficial stores. Botnet applications are kind of malware that can be published using these stores and downloaded by the victims on their smartphones. In this paper, we propose Android botnet detection method based a new set of discriminating features extracted based from the analysis of Android permissions (i.e. Protection levels for all available Android permissions). Then we compared the prediction power of different machine learning models before and after adding these features to the state-of-art requested permissions features in Android. We used four popular ML classifiers (i.e. Random Forest, MultiLayer Perceptron neural networks, Decision trees, and Naïve Bayes) for our experiments and we found that the new set of features have a tiny improvement on the performance in the case of decision trees and Random forest classifiers. © 2017 IEEE.

@Conference{Hijawi2018197,
author = {Hijawi, W. and Alqatawna, J. and Faris, H.},
title = {Toward a detection framework for android botnet},
year = {2018},
volume = {2018-January},
pages = {197-202},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Android is one of the most popular and widespread operating systems for smartphones. It has several millions of applications that are published at either official or unofficial stores. Botnet applications are kind of malware that can be published using these stores and downloaded by the victims on their smartphones. In this paper, we propose Android botnet detection method based a new set of discriminating features extracted based from the analysis of Android permissions (i.e. Protection levels for all available Android permissions). Then we compared the prediction power of different machine learning models before and after adding these features to the state-of-art requested permissions features in Android. We used four popular ML classifiers (i.e. Random Forest, MultiLayer Perceptron neural networks, Decision trees, and Naïve Bayes) for our experiments and we found that the new set of features have a tiny improvement on the performance in the case of decision trees and Random forest classifiers. © 2017 IEEE.},
document_type = {Conference Paper},
doi = {10.1109/ICTCS.2017.48},
isbn = {9781538605271},
journal = {Proceedings - 2017 International Conference on New Trends in Computing Sciences, ICTCS 2017},
source = {Scopus},
}

Hijawi, W., Faris, H., Alqatawna, J., Al-Zoubi, A. M., & Aljarah, I.. (2018). Improving email spam detection using content based feature engineering approach. . doi:10.1109/AEECT.2017.8257764
[BibTeX] [Abstract]

Recently, a wide range of Machine Learning (ML) algorithms have been proposed for building email spam detection models. However, the performance of ML methods highly depends on the extracted features. In this paper, we discuss the most influencing spam features reported in the literature. We also describe the development and implementation of an open source tool that provides a flexible way to extract a large number of features from any email corpus to produce cleansed dataset which can be used to train and test various classification algorithms. A total of 140 features are extracted from SpamAssassin email corpus using the developed tool. Extracted features are used to evaluate four popular ML classifiers and a better results are achieved in comparison with the results of a similar previous study. © 2017 IEEE.

@Conference{Hijawi2018,
author = {Hijawi, W. and Faris, H. and Alqatawna, J. and Al-Zoubi, A.M. and Aljarah, I.},
title = {Improving email spam detection using content based feature engineering approach},
year = {2018},
volume = {2018-January},
pages = {1-6},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Recently, a wide range of Machine Learning (ML) algorithms have been proposed for building email spam detection models. However, the performance of ML methods highly depends on the extracted features. In this paper, we discuss the most influencing spam features reported in the literature. We also describe the development and implementation of an open source tool that provides a flexible way to extract a large number of features from any email corpus to produce cleansed dataset which can be used to train and test various classification algorithms. A total of 140 features are extracted from SpamAssassin email corpus using the developed tool. Extracted features are used to evaluate four popular ML classifiers and a better results are achieved in comparison with the results of a similar previous study. © 2017 IEEE.},
document_type = {Conference Paper},
doi = {10.1109/AEECT.2017.8257764},
isbn = {9781509059690},
journal = {2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, AEECT 2017},
source = {Scopus},
}

Recently, a wide range of Machine Learning (ML) algorithms have been proposed for building email spam detection models. However, the performance of ML methods highly depends on the extracted features. In this paper, we discuss the most influencing spam features reported in the literature. We also describe the development and implementation of an open source tool that provides a flexible way to extract a large number of features from any email corpus to produce cleansed dataset which can be used to train and test various classification algorithms. A total of 140 features are extracted from SpamAssassin email corpus using the developed tool. Extracted features are used to evaluate four popular ML classifiers and a better results are achieved in comparison with the results of a similar previous study. © 2017 IEEE.

@Conference{Hijawi20181,
author = {Hijawi, W. and Faris, H. and Alqatawna, J. and Al-Zoubi, A.M. and Aljarah, I.},
title = {Improving email spam detection using content based feature engineering approach},
year = {2018},
volume = {2018-January},
pages = {1-6},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Recently, a wide range of Machine Learning (ML) algorithms have been proposed for building email spam detection models. However, the performance of ML methods highly depends on the extracted features. In this paper, we discuss the most influencing spam features reported in the literature. We also describe the development and implementation of an open source tool that provides a flexible way to extract a large number of features from any email corpus to produce cleansed dataset which can be used to train and test various classification algorithms. A total of 140 features are extracted from SpamAssassin email corpus using the developed tool. Extracted features are used to evaluate four popular ML classifiers and a better results are achieved in comparison with the results of a similar previous study. © 2017 IEEE.},
document_type = {Conference Paper},
doi = {10.1109/AEECT.2017.8257764},
isbn = {9781509059690},
journal = {2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, AEECT 2017},
source = {Scopus},
}

Alsawalqah, H., Faris, H., Aljarah, I., Alnemer, L., & Alhindawi, N.. (2017). Hybrid smote-ensemble approach for software defect prediction. Advances in intelligent systems and computing, 575, 355-366. doi:10.1007/978-3-319-57141-6_39
[BibTeX] [Abstract]

Software defect prediction is the process of identifying new defects/bugs in software modules. Software defect presents an error in a computer program, which is caused by incorrect code or incorrect programming logic. As a result, undiscovered defects lead to a poor quality software products. In recent years, software defect prediction has received a considerable amount of attention from researchers. Most of the previous defect detection algorithms are marred by low defect detection ratios. Furthermore, software defect prediction is very challenging problem due to the high imbalanced distribution, where the bug-free codes are much higher than defective ones. In this paper, the software defect prediction problem is formulated as a classification task, and then it examines the impact of several ensembles methods on the classification effectiveness. In addition, the best ensemble classifier will be selected to be trained again on an over-sampled datasets using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm to tackle imbalanced distribution problem. The proposed hybrid method is evaluated using four software defects datasets. Experimental results demonstrate that the proposed method can effectively enhance the defect prediction accuracy. © Springer International Publishing AG 2017.

@Article{Alsawalqah2017355,
author = {Alsawalqah, H. and Faris, H. and Aljarah, I. and Alnemer, L. and Alhindawi, N.},
title = {Hybrid SMOTE-ensemble approach for software defect prediction},
journal = {Advances in Intelligent Systems and Computing},
year = {2017},
volume = {575},
pages = {355-366},
issn = {21945357},
abstract = {Software defect prediction is the process of identifying new defects/bugs in software modules. Software defect presents an error in a computer program, which is caused by incorrect code or incorrect programming logic. As a result, undiscovered defects lead to a poor quality software products. In recent years, software defect prediction has received a considerable amount of attention from researchers. Most of the previous defect detection algorithms are marred by low defect detection ratios. Furthermore, software defect prediction is very challenging problem due to the high imbalanced distribution, where the bug-free codes are much higher than defective ones. In this paper, the software defect prediction problem is formulated as a classification task, and then it examines the impact of several ensembles methods on the classification effectiveness. In addition, the best ensemble classifier will be selected to be trained again on an over-sampled datasets using the Synthetic Minority Over-sampling Technique (SMOTE) algorithm to tackle imbalanced distribution problem. The proposed hybrid method is evaluated using four software defects datasets. Experimental results demonstrate that the proposed method can effectively enhance the defect prediction accuracy. © Springer International Publishing AG 2017.},
document_type = {Conference Paper},
doi = {10.1007/978-3-319-57141-6_39},
isbn = {9783319571409},
publisher = {Springer Verlag},
source = {Scopus},
}

Al-Zoubi, A. M., Alqatawna, J., & Faris, H.. (2017). Spam profile detection in social networks based on public features. . doi:10.1109/IACS.2017.7921959
[BibTeX] [Abstract]

In the context of Online Social Networks, Spam profiles are not just a source of unwanted ads, but a serious security threat used by online criminals and terrorists for various malicious purposes. Recently, such criminals were able to steal a number of accounts that belong to NatWest bank’s customers. Their attack vector was based on spam tweets posted by a Twitter account which looked very close to NatWest customer support account and leaded users to a link of a phishing site. In this study, we investigate the nature of spam profiles in Twitter with a goal to improve social spam detection. Based on a set of publicly available features, we develop spam profiles detection models. At this stage, a dataset of 82 Twitter’s profiles are collected and analyzed. With feature engineering, we investigate ten binary and simple features that can be used to classify spam profiles. Moreover, a feature selection process is utilized to identify the most influencing features in the process of detecting spam profiles. For feature selection, two methods are used ReliefF and Information Gain. While for classification, four classification algorithms are applied and compared: Decision Trees, Multilayer Perceptron, k-Nearest neighbors and Naive Bayes. Preliminary experiments in this work show that the promising detection rates can be obtained using such features regardless of the language of the tweets. © 2017 IEEE.

@Conference{Al-Zoubi2017130,
author = {Al-Zoubi, A.M. and Alqatawna, J. and Faris, H.},
title = {Spam profile detection in social networks based on public features},
year = {2017},
pages = {130-135},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {In the context of Online Social Networks, Spam profiles are not just a source of unwanted ads, but a serious security threat used by online criminals and terrorists for various malicious purposes. Recently, such criminals were able to steal a number of accounts that belong to NatWest bank's customers. Their attack vector was based on spam tweets posted by a Twitter account which looked very close to NatWest customer support account and leaded users to a link of a phishing site. In this study, we investigate the nature of spam profiles in Twitter with a goal to improve social spam detection. Based on a set of publicly available features, we develop spam profiles detection models. At this stage, a dataset of 82 Twitter's profiles are collected and analyzed. With feature engineering, we investigate ten binary and simple features that can be used to classify spam profiles. Moreover, a feature selection process is utilized to identify the most influencing features in the process of detecting spam profiles. For feature selection, two methods are used ReliefF and Information Gain. While for classification, four classification algorithms are applied and compared: Decision Trees, Multilayer Perceptron, k-Nearest neighbors and Naive Bayes. Preliminary experiments in this work show that the promising detection rates can be obtained using such features regardless of the language of the tweets. © 2017 IEEE.},
art_number = {7921959},
document_type = {Conference Paper},
doi = {10.1109/IACS.2017.7921959},
isbn = {9781509042432},
journal = {2017 8th International Conference on Information and Communication Systems, ICICS 2017},
source = {Scopus},
}

Faris, H., Aljarah, I., & Al-Shboul, B.. (2016). A hybrid approach based on particle swarm optimization and random forests for e-mail spam filtering. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), 9875 LNCS, 498-508. doi:10.1007/978-3-319-45243-2_46
[BibTeX] [Abstract]

Internet is flooded every day with a huge number of spam emails. This will lead the internet users to spend a lot of time and effort to manage their mailboxes to distinguish between legitimate and spam emails, which can considerably reduce their productivity. Therefore, in the last decade, many researchers and practitioners proposed different approaches in order to increase the effectiveness and safety of spam filtering models. In this paper, we propose a spam filtering approach consisted of two main stages; feature selection and emails classification. In the first step a Particle Swarm Optimization (PSO) basedWrapper Feature Selection is used to select the best representative set of features to reduce the large number of measured features. In the second stage, a Random Forest spam filtering model is developed using the selected features in the first stage. Experimental results on real-world spam data set show the better performance of the proposed method over other five traditional machine learning approaches from the literature. Furthermore, four cost functions are used to evaluate the proposed spam filtering method. The results reveal that the PSO based Wrapper with Random Forest can effectively be used for spam detection. © Springer International Publishing Switzerland 2016.

@Article{Faris2016498,
author = {Faris, H. and Aljarah, I. and Al-Shboul, B.},
title = {A hybrid approach based on particle swarm optimization and random forests for e-mail spam filtering},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year = {2016},
volume = {9875 LNCS},
pages = {498-508},
issn = {03029743},
abstract = {Internet is flooded every day with a huge number of spam emails. This will lead the internet users to spend a lot of time and effort to manage their mailboxes to distinguish between legitimate and spam emails, which can considerably reduce their productivity. Therefore, in the last decade, many researchers and practitioners proposed different approaches in order to increase the effectiveness and safety of spam filtering models. In this paper, we propose a spam filtering approach consisted of two main stages; feature selection and emails classification. In the first step a Particle Swarm Optimization (PSO) basedWrapper Feature Selection is used to select the best representative set of features to reduce the large number of measured features. In the second stage, a Random Forest spam filtering model is developed using the selected features in the first stage. Experimental results on real-world spam data set show the better performance of the proposed method over other five traditional machine learning approaches from the literature. Furthermore, four cost functions are used to evaluate the proposed spam filtering method. The results reveal that the PSO based Wrapper with Random Forest can effectively be used for spam detection. © Springer International Publishing Switzerland 2016.},
document_type = {Conference Paper},
doi = {10.1007/978-3-319-45243-2_46},
publisher = {Springer Verlag},
source = {Scopus},
}

Faris, H., Aljarah, I., Mirjalili, S., Castillo, P. A., & Merelo, J. J.. (2016). Evolopy: an open-source nature-inspired optimization framework in python. .
[BibTeX] [Abstract]

EvoloPy is an open source and cross-platform Python framework that implements a wide range of classical and recent nature-inspired metaheuristic algorithms. The goal of this framework is to facilitate the use of metaheuristic algorithms by non-specialists coming from different domains. With a simple interface and minimal dependencies, it is easier for researchers and practitioners to utilize EvoloPy for optimizing and benchmarking their own defined problems using the most powerful metaheuristic optimizers in the literature. This framework facilitates designing new algorithms or improving, hybridizing and analyzing the current ones. The source code of EvoloPy is publicly available at GitHub (https://github.com/7ossam81/EvoloPy). © 2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

@Conference{Faris2016171,
author = {Faris, H. and Aljarah, I. and Mirjalili, S. and Castillo, P.A. and Merelo, J.J.},
title = {EvoloPy: An open-source nature-inspired optimization framework in python},
year = {2016},
volume = {1},
pages = {171-177},
publisher = {SciTePress},
abstract = {EvoloPy is an open source and cross-platform Python framework that implements a wide range of classical and recent nature-inspired metaheuristic algorithms. The goal of this framework is to facilitate the use of metaheuristic algorithms by non-specialists coming from different domains. With a simple interface and minimal dependencies, it is easier for researchers and practitioners to utilize EvoloPy for optimizing and benchmarking their own defined problems using the most powerful metaheuristic optimizers in the literature. This framework facilitates designing new algorithms or improving, hybridizing and analyzing the current ones. The source code of EvoloPy is publicly available at GitHub (https://github.com/7ossam81/EvoloPy). © 2016 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
document_type = {Conference Paper},
isbn = {9789897582011},
journal = {IJCCI 2016 - Proceedings of the 8th International Joint Conference on Computational Intelligence},
source = {Scopus},
}

Qaisi, L. M., & Aljarah, I.. (2016). A twitter sentiment analysis for cloud providers: a case study of azure vs. aws. . doi:10.1109/CSIT.2016.7549473
[BibTeX] [Abstract]

The rapid revolution of Social Network Sites (SNS) around the globe is presenting wide range of data that can be used in studies of sentiment analysis about certain products, brands, services… etc. In addition, cloud computing fields had been one of the most interesting fields in research studies. In this paper, we used the sentiment analysis of top leading cloud service providers namely; Amazon and Microsoft Azure to analyse their customers’ opinions and reviews. To do that, two datasets are extracted which are consisting of tweets that had either organizations’ names or cloud names. We study, and analyze the way customers think about them. In this regard, many organizations tend to find out what do customers think or tweet about their products in order to effectively plan marketing campaigns and try to gain the positive impact of Word-of-Mouth. Results are analyzed and explained in details in term of polarity and emotions classifications to show the impact of sentiment analysis to support organizations decisions. We can note from the emotions classification results that, ‘joy’ category is better for Microsoft Azure comparing to Amazon, The ‘sadness’ percentage is larger for Amazon comparing to Microsoft Azure. Furthermore, we can note from the polarity classification that Microsoft Azure has 65% positive tweets compared 45% for Amazon. In addition, the results show that Amazon has 50% negative polarity compared 25% for Microsoft Azure. © 2016 IEEE.

@Conference{Qaisi2016,
author = {Qaisi, L.M. and Aljarah, I.},
title = {A twitter sentiment analysis for cloud providers: A case study of Azure vs. AWS},
year = {2016},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {The rapid revolution of Social Network Sites (SNS) around the globe is presenting wide range of data that can be used in studies of sentiment analysis about certain products, brands, services... etc. In addition, cloud computing fields had been one of the most interesting fields in research studies. In this paper, we used the sentiment analysis of top leading cloud service providers namely; Amazon and Microsoft Azure to analyse their customers' opinions and reviews. To do that, two datasets are extracted which are consisting of tweets that had either organizations' names or cloud names. We study, and analyze the way customers think about them. In this regard, many organizations tend to find out what do customers think or tweet about their products in order to effectively plan marketing campaigns and try to gain the positive impact of Word-of-Mouth. Results are analyzed and explained in details in term of polarity and emotions classifications to show the impact of sentiment analysis to support organizations decisions. We can note from the emotions classification results that, 'joy' category is better for Microsoft Azure comparing to Amazon, The 'sadness' percentage is larger for Amazon comparing to Microsoft Azure. Furthermore, we can note from the polarity classification that Microsoft Azure has 65% positive tweets compared 45% for Amazon. In addition, the results show that Amazon has 50% negative polarity compared 25% for Microsoft Azure. © 2016 IEEE.},
art_number = {7549473},
document_type = {Conference Paper},
doi = {10.1109/CSIT.2016.7549473},
isbn = {9781467389136},
journal = {Proceedings - CSIT 2016: 2016 7th International Conference on Computer Science and Information Technology},
source = {Scopus},
}

Faris, H., Aljarah, I., & Alqatawna, J.. (2015). Optimizing feedforward neural networks using krill herd algorithm for e-mail spam detection. . doi:10.1109/AEECT.2015.7360576
[BibTeX] [Abstract]

Krill Herd is a new optimization technique that was inspired by the herding behavior of real small crustaceans called Krills. The method was developed for continuous optimization problems and has recently been successfully applied to different complex problems. Feedforward neural network has a number of characteristics which make it suitable for solving complex classification problems. The training of the this type of neural networks is considered the most challenging operation. Training neural networks aims to find a nearly global optimal set of connection weights in a relatively short time. In this paper we propose an application of Krill Herd algorithm for training the Feedforward neural network and optimizing its connection weights. The developed neural network will be applied for an E-mail spam detection model. The model will be evaluated and compared to other two popular training algorithms; the Back-propagation algorithm and the Genetic Algorithm. Evaluation results show that the developed training approach using Krill Herd algorithm outperforms the other two algorithms. © 2015 IEEE.

@Conference{Faris2015,
author = {Faris, H. and Aljarah, I. and Alqatawna, J.},
title = {Optimizing Feedforward neural networks using Krill Herd algorithm for E-mail spam detection},
year = {2015},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Krill Herd is a new optimization technique that was inspired by the herding behavior of real small crustaceans called Krills. The method was developed for continuous optimization problems and has recently been successfully applied to different complex problems. Feedforward neural network has a number of characteristics which make it suitable for solving complex classification problems. The training of the this type of neural networks is considered the most challenging operation. Training neural networks aims to find a nearly global optimal set of connection weights in a relatively short time. In this paper we propose an application of Krill Herd algorithm for training the Feedforward neural network and optimizing its connection weights. The developed neural network will be applied for an E-mail spam detection model. The model will be evaluated and compared to other two popular training algorithms; the Back-propagation algorithm and the Genetic Algorithm. Evaluation results show that the developed training approach using Krill Herd algorithm outperforms the other two algorithms. © 2015 IEEE.},
art_number = {7360576},
document_type = {Conference Paper},
doi = {10.1109/AEECT.2015.7360576},
isbn = {9781479974313},
journal = {2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, AEECT 2015},
source = {Scopus},
}

Shukri, S. E., Yaghi, R. I., Aljarah, I., & Alsawalqah, H.. (2015). Twitter sentiment analysis: a case study in the automotive industry. . doi:10.1109/AEECT.2015.7360594
[BibTeX] [Abstract]

Sentiment analysis is one of the fastest growing areas which uses the natural language processing, text mining and computational linguistic to extract useful information to help in the decision making process. In the recent years, social media websites have been spreading widely, and their users are increasing rapidly. Automotive industry is one of the largest economic sectors in the world with more than 90 million cars and vehicles. Automotive industry is highly competitive and requires that sellers, automotive companies, carefully analyze and attend to consumers’ opinions in order to achieve a competitive advantage in the market. Analysing consumers’ opinions using social media data can be very great way for the automotive companies to enhance their marketing targets and objectives. In this paper, a sentiment analyses on a case study in the automotive industry is presented. Text mining and sentiment analysis are used to analyze unstructured tweets on Twitter to extract the polarity, and emotions classification towards the automotive classes such as Mercedes, Audi and BMW. We can note from the emotions classification results that, joy category is better for BMW comparing to Mercedes and Audi, The sadness percentage is larger for Audi and Mercedes comparing to BMW. Furthermore, we can note from the polarity classification that BMW has 72% positive tweets compared 79% for Mercedes and 83% for Audi. In addition, the results show that BMW has 8% negative polarity compared 18% for Mercedes and 16% for Audi. © 2015 IEEE.

@Conference{Shukri2015,
author = {Shukri, S.E. and Yaghi, R.I. and Aljarah, I. and Alsawalqah, H.},
title = {Twitter sentiment analysis: A case study in the automotive industry},
year = {2015},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Sentiment analysis is one of the fastest growing areas which uses the natural language processing, text mining and computational linguistic to extract useful information to help in the decision making process. In the recent years, social media websites have been spreading widely, and their users are increasing rapidly. Automotive industry is one of the largest economic sectors in the world with more than 90 million cars and vehicles. Automotive industry is highly competitive and requires that sellers, automotive companies, carefully analyze and attend to consumers' opinions in order to achieve a competitive advantage in the market. Analysing consumers' opinions using social media data can be very great way for the automotive companies to enhance their marketing targets and objectives. In this paper, a sentiment analyses on a case study in the automotive industry is presented. Text mining and sentiment analysis are used to analyze unstructured tweets on Twitter to extract the polarity, and emotions classification towards the automotive classes such as Mercedes, Audi and BMW. We can note from the emotions classification results that, joy category is better for BMW comparing to Mercedes and Audi, The sadness percentage is larger for Audi and Mercedes comparing to BMW. Furthermore, we can note from the polarity classification that BMW has 72% positive tweets compared 79% for Mercedes and 83% for Audi. In addition, the results show that BMW has 8% negative polarity compared 18% for Mercedes and 16% for Audi. © 2015 IEEE.},
art_number = {7360594},
document_type = {Conference Paper},
doi = {10.1109/AEECT.2015.7360594},
isbn = {9781479974313},
journal = {2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, AEECT 2015},
source = {Scopus},
}

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