@conference {Habibi2022462,
	title = {Effect of Features Extraction and Selection on the Evaluation of Machine Learning Models},
	booktitle = {IFAC-PapersOnLine},
	volume = {55},
	number = {12},
	year = {2022},
	note = {cited By 0},
	pages = {462-467},
	abstract = {The exponential growth of sophisticated malware attacks against computer systems, has alerted IT security experts on the shortcomings of traditional protection tools because, they became unable to detect new families of malware that are more advanced and use advanced tools such as polymorphism, metamorphism, and obfuscation tools. Nowadays, machine learning is widely used in several IT fields; and also in cybersecurity, and can be an essential tool for malware detection, moreover, it can go beyond the limits of classic malware detection methods, such as the signature-based method, the anomaly-based method and the hybrid-based method, etc. The purpose of this study is to analyze the feature selection and extraction effects on the performance of malware classification model using machine learning. The results show that reducing dimensionality of datasets can help to improve the efficiency of security models in restricted time but with high performance, Random Forest using chi-square achieves an accuracy of 99.51\%. {\textcopyright} 2022 Elsevier B.V.. All rights reserved.},
	keywords = {ANN, Cybersecurity, Decision trees, Extraction, Feature extraction, Features extraction, KNN, Logistic regression, Logistics regressions, Machine-learning, Malware, Malware detection, Network security, Performance, random forest, Random forests},
	doi = {10.1016/j.ifacol.2022.07.355},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137165053\&doi=10.1016\%2fj.ifacol.2022.07.355\&partnerID=40\&md5=b3575cc26a69761120b672e3d94e63c0},
	author = {Habibi, O. and Chemmakha, M. and Lazaar, M.}
}
@conference {Chemmakha2022771,
	title = {Improving Machine Learning Models for Malware Detection Using Embedded Feature Selection Method},
	booktitle = {IFAC-PapersOnLine},
	volume = {55},
	number = {12},
	year = {2022},
	note = {cited By 0},
	pages = {771-776},
	abstract = {Machine learning performance always rely on relevant phase of pre-processing, that includes dataset cleaning, cleansing and extraction. Feature selection (FS) is a crucial phase too, because it is intended to increase the efficiency of Machine Learning (ML) models in terms of predictiveness, by assigning a representative value to the most important features in a dataset of malware. In this study, we focus on feature selection using embedded-based methods in order to minimize computational time and complexity of ML models. Embedded-based methods combine advantages of both filter-based and wrapped-based methods, in terms of studying the importance of features while executing the model and their reduced time of execution. Applying ML models shows a high stability of models will selecting 10 most relevant features from the dataset, with an accuracy that achieve 99.47\%, 99.02\% for respectively Random Forest (RF) and XGBoost (XGB). {\textcopyright} 2022 Elsevier B.V.. All rights reserved.},
	keywords = {ANN, Decision trees, feature selection, Features selection, Lightgbm, Machine learning models, Machine-learning, Malware, Malware detection, Random forests, support vector machine, Support vector machines, Support vectors machine, Xgboost},
	doi = {10.1016/j.ifacol.2022.07.406},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137157154\&doi=10.1016\%2fj.ifacol.2022.07.406\&partnerID=40\&md5=4c66048ed61a71abdde00c73a3839bbc},
	author = {Chemmakha, M. and Habibi, O. and Lazaar, M.}
}
@conference {Rachidi202213,
	title = {Network intrusion detection using Machine Learning approach},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2022},
	note = {cited By 0},
	pages = {13-17},
	abstract = {Today, intrusion detection has become an active research area. Due to the rapidly increasing number of intrusion variants, intrusion detection system analyses and notifies the activities of users as normal (or) anomaly. In our paper, we built a model of intrusion detection system applied to the NSL-KDD data set using different supervised classifiers such as KNN and Na{\"\i}ve Bayes. We also proposed two algorithms for multi-classification based on the Random Forest (RF) which is an ensemble classifier and KNN. Then we used the K-folds method to evaluate and validate our model. To evaluate the performances, we realized experiments on NSL-KDD data set. The result shows that the second proposed algorithm is efficient with high accuracy and time optimization. {\textcopyright} 2022 ACM.},
	keywords = {Barium compounds, Classification (of information), Computer crime, data set, Decision trees, Intrusion detection, Intrusion Detection Systems, Intrusion-Detection, Machine learning, Machine learning approaches, Multi-classification, Network intrusion detection, NSL-KDD, Random forests, Research areas, Sodium compounds, Supervised classifiers},
	doi = {10.1145/3551690.3551693},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139141127\&doi=10.1145\%2f3551690.3551693\&partnerID=40\&md5=e47a5f8038487b706673543f7fdf2de4},
	author = {Rachidi, Z. and Chougdali, K. and Kobbane, A. and Ben-Othman, J.}
}
@article {Nakach20223,
	title = {Random Forest Based Deep Hybrid Architecture for Histopathological Breast Cancer Images Classification},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {13376 LNCS},
	year = {2022},
	note = {cited By 0},
	pages = {3-18},
	abstract = {Breast cancer is the most common cancer in women worldwide. While the early diagnosis and treatment can significantly reduce the mortality rate, it is a challenging task for pathologists to accurately estimate the cancerous cells and tissues. Therefore, machine learning techniques are playing a significant role in assisting pathologists and improving the diagnosis results. This paper proposes a hybrid architecture that combines: three of the most recent deep learning techniques for feature extraction (DenseNet_201, Inception_V3, and MobileNet_V2) and random forest to classify breast cancer histological images over the BreakHis dataset with its four magnification factors: 40X, 100X, 200X and 400X. The study evaluated and compared: (1) the developed random forest models with their base learners, (2) the designed random forest models with the same architecture but with a different number of trees, (3) the decision tree classifiers with the best random forest models and (4) the best random forest models of each feature extractor. The empirical evaluations used: four classification performance criteria (accuracy, sensitivity, precision and F1-score), 5-fold cross-validation, Scott Knott statistical test, and Borda Count voting method. The best random forest model achieved an accuracy mean value of 85.88\%, and was constructed using 9 trees, 200X as a magnification factor, and Inception_V3 as a feature extractor. The experimental results demonstrated that combining random forest with deep learning models is effective for the automatic classification of malignant and benign tumors using histopathological images of breast cancer. {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Breast Cancer, Classification (of information), Decision trees, Deep learning, Diagnosis, Diseases, early diagnosis, Feature extractor, hybrid, Hybrid architectures, Image classification, Images classification, Learning systems, Magnification factors, Medical imaging, Random forest modeling, Random forests, Transfer learning},
	doi = {10.1007/978-3-031-10450-3_1},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135002675\&doi=10.1007\%2f978-3-031-10450-3_1\&partnerID=40\&md5=892757314e050f81e9b0dba117f2d64a},
	author = {Nakach, F.-Z. and Zerouaoui, H. and Idri, A.}
}
@conference {Bouijij2021,
	title = {Machine Learning Algorithms Evaluation for Phishing URLs Classification},
	booktitle = {2021 4th International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2021},
	year = {2021},
	note = {cited By 2},
	abstract = {Phishing URL is a type of cyberattack, based on falsified URLs. The number of phishing URL attacks continues to increase despite cybersecurity efforts. According to the Anti-Phishing Working Group (APWG), the number of phishing websites observed in 2020 is 1 520 832, doubling over the course of a year. Various algorithms, techniques and methods can be used to build models for phishing URL detection and classification. From our reading, we observed that Machine Learning (ML) is one of the recent approaches used to detect and classify phishing URL in an efficient and proactive way. In this paper, we evaluate eleven of the most adopted ML algorithms such as Decision Tree (DT), Nearest Neighbours (KNN), Gradient Boosting (GB), Logistic Regression (LR), Na{\"\i}ve Bayes (NB), Random Forest (RF), Support Vector Machines (SVM), Neural Network (NN), Ex-tra_Tree (ET), Ada_Boost (AB) and Bagging (B). To do that, we compute detection accuracy metric for each algorithm and we use lexical analysis to extract the URL features. {\textcopyright} 2021 IEEE.},
	keywords = {Accuracy metric, Adaptive boosting, Algorithm evaluation, Computational linguistics, Computer crime, Cyber security, Cyber-attacks, Cybersecurity, Decision trees, Feature, Lexical analysis, Logistic regression, Machine learning algorithms, Phishing, Phishing-URL, Random forests, Support vector machines},
	doi = {10.1109/ISAECT53699.2021.9668489},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124953329\&doi=10.1109\%2fISAECT53699.2021.9668489\&partnerID=40\&md5=6054cb867f24686cb4af1bf450094608},
	author = {Bouijij, H. and Berqia, A.}
}
@article {Gryech2020,
	title = {Moreair: A low-cost urban air pollution monitoring system},
	journal = {Sensors (Switzerland)},
	volume = {20},
	number = {4},
	year = {2020},
	note = {cited By 25},
	abstract = {MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is based on mobile and nomadic sensors as well as on medical data collected at a children{\textquoteright}s hospital, used to identify urban areas of high prevalence of respiratory diseases. Another key feature is the use of machine learning to perform prediction. In this paper, Moroccan cities are taken as case studies. Using the agile deployment strategy of MoreAir, it is shown that in many Moroccan neighborhoods, road traffic has a smaller impact on the concentrations of particulate matters (PM) than other sources, such as public baths, public ovens, open-air street food vendors and thrift shops. A geographical information system has been developed to provide real-time information to the citizens about the air quality in different neighborhoods and thus raise awareness about urban pollution. {\textcopyright} 2020 by the authors. Licensee MDPI, Basel, Switzerland.},
	keywords = {Agile deployments, Agile manufacturing systems, Air quality, Costs, Decision trees, Geographic information systems, Information systems, Information use, Learning systems, Machine learning, Mobile sensing, Monitoring, Particles (particulate matter), Particulate Matter, Pollution detection, Pollution monitoring, Random forests, Real-time information, Sensor deployment, Urban air pollution, Urban pollutions},
	doi = {10.3390/s20040998},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079609813\&doi=10.3390\%2fs20040998\&partnerID=40\&md5=dc90b4357fcdc64d82d12f57a45f971f},
	author = {Gryech, I. and Ben-Aboud, Y. and Guermah, B. and Sbihi, N. and Ghogho, M. and Kobbane, A.}
}
@conference {Lamrhari2020,
	title = {Random Forest-based Approach for Classifying Customers in Social CRM},
	booktitle = {2020 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2020},
	year = {2020},
	note = {cited By 0},
	abstract = {Social Customer Relationship Management (social CRM) has become one of the central points for many companies seeking to improve their customer experience. It comprises a set of processes that allows decision-makers to analyze customer data in order to launch an efficient customer-centric and cost-effective marketing strategy. However, targeting all potential customers with one general marketing strategy seems to be inefficient. While targeting each potential customer with a specific strategy can be cost demanding. Thus, it is essential to group customers into specific classes and target each class according to its respective customer needs. In this paper, we develop a Random Forest- based approach to classify potential customers into three main categories namely, prospects, satisfied and unsatisfied customers. The proposed model has been trained, tested, and compared to some state-of-the-art classifiers viz., Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) based on several metrics including accuracy, sensitivity, specificity, false positive rate, and false negative rate. The reported results were satisfactory with an accuracy of 98.46\%, a sensitivity of 97.69\%, and a specificity of 98.84\%. {\textcopyright} 2020 IEEE.},
	keywords = {Commerce, Cost effectiveness, Customer experience, Customer relationship management, Decision making, Decision trees, False negative rate, False positive rates, Industrial management, K nearest neighbor (KNN), Marketing strategy, Nearest neighbor search, Neural networks, Potential customers, Public relations, Random forests, Sales, State of the art, Strategic planning, Support vector machines},
	doi = {10.1109/ICTMOD49425.2020.9380602},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103738578\&doi=10.1109\%2fICTMOD49425.2020.9380602\&partnerID=40\&md5=c672980200a36b34b0ae6df0842ef948},
	author = {Lamrhari, S. and Elghazi, H. and El Faker, A.}
}
@article {Zakrani202072,
	title = {Software Effort Estimation Using an Optimal Trees Ensemble: An Empirical Comparative Study},
	journal = {Smart Innovation, Systems and Technologies},
	volume = {146},
	year = {2020},
	note = {cited By 0},
	pages = {72-82},
	abstract = {Since information systems have become the heartbeat of many organizations, the investment in software is growing rapidly and consuming then a significant portion of the company budget. In this context, both the software engineering practitioners and researchers are more interested than ever about accurately estimating the effort and the quality of software product under development. Accurate estimates are desirable but no technique has demonstrated to be successful at effectively and reliably estimating software development effort. In this paper, we propose the use of an optimal trees ensemble (OTE) to predict the software development effort. The ensemble employed is built by combining only the top ranked trees, one by one, from a set of random forests. Each included tree must decrease the unexplained variance of the ensemble for software development effort estimation (SDEE). The effectiveness of the OTE model is compared with other techniques such as regression trees, random forest, RBF neural networks, support vector regression and multiple linear regression in terms of the mean magnitude relative error (MMRE), MdMRE and Pred(l) obtained on five well known datasets namely: ISBSG R8, COCOMO, Tukutuku, Desharnais and Albrecht. According to the results obtained from the experiments, it is shown that the proposed ensemble of optimal trees outperformed almost all the other techniques. Also, OTE model outperformed statistically the other techniques at least in one dataset. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Accuracy evaluation, Budget control, Decision trees, Forestry, Linear regression, Multiple linear regressions, Optimal trees ensemble, Radial basis function networks, Random forests, RBF Neural Network, Regression trees, Software design, Software development effort, Support vector regression (SVR)},
	doi = {10.1007/978-3-030-21005-2_7},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069482179\&doi=10.1007\%2f978-3-030-21005-2_7\&partnerID=40\&md5=8d9d107d9b52c20f6cd93bb86477a6a0},
	author = {Zakrani, A. and Idri, A. and Hain, M.}
}
