@article {Wadghiri2022,
	title = {Ensemble blood glucose prediction in diabetes mellitus: A review},
	journal = {Computers in Biology and Medicine},
	volume = {147},
	year = {2022},
	note = {cited By 0},
	abstract = {Considering the complexity of blood glucose dynamics, the adoption of a single model to predict blood glucose level does not always capture the inter- and intra-patients{\textquoteright} context changes. Ensembles are a set of machine learning techniques combining multiple single learners to find a better variance/bias trade-off and hence improve the prediction accuracy. The present paper aims to review the state of the art in predicting blood glucose using ensemble methods with regard to 8 criteria: publication year and sources, datasets used to train/evaluate the models, types of ensembles used, single learners involved to construct ensembles, combination schemes used to aggregate the base learners, metrics and validation methods adopted to assess the performance of ensembles, reported overall performance of the predictors and accuracy comparison of ensemble techniques with single models. A systematic literature review has been conducted in order to analyze and synthetize primary studies published between 2000 and 2020 in six digital libraries. A total of 32 primary papers were selected and reviewed with regard to eight review questions. The results show that ensembles have gained wider interest during the last years and improved in general the performance compared with other single models. However, multiple gaps have been identified concerning the ensembles construction process and the performance metrics used. Several recommendations have been made in this regard to design accurate ensembles for blood glucose level prediction. {\textcopyright} 2022 Elsevier Ltd},
	keywords = {algorithm, Algorithms, Blood, Blood glucose, Blood glucose level, Data mining, Diabetes mellitus, Digital libraries, Economic and social effects, Ensemble methods, Forecasting, Glucose, glucose blood level, Glucose dynamics, human, Humans, Machine learning, Machine learning techniques, Machine-learning, Performance, Single models, Trade off},
	doi = {10.1016/j.compbiomed.2022.105674},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132341147\&doi=10.1016\%2fj.compbiomed.2022.105674\&partnerID=40\&md5=05bd59c3726654494a4be346e4e6a682},
	author = {Wadghiri, M.Z. and Idri, A. and El Idrissi, T. and Hakkoum, H.}
}
@conference {Sabiri2022418,
	title = {Mechanism of Overfitting Avoidance Techniques for Training Deep Neural Networks},
	booktitle = {International Conference on Enterprise Information Systems, ICEIS - Proceedings},
	volume = {1},
	year = {2022},
	note = {cited By 0},
	pages = {418-427},
	abstract = {The objective of a deep learning neural network is to have a final model that performs well both on the data used to train it and the new data on which the model will be used to make predictions. Overfitting refers to the fact that the predictive model produced by the machine learning algorithm adapts well to the training set. In this case, the predictive model will capture the generalizable correlations and the noise produced by the data and will be able to give very good predictions on the data of the training set, but it will predict badly on the data that it has not yet seen during his learning phase. This paper proposes two techniques among many others to reduce or prevent overfitting. Furthermore, by analyzing dynamics during training, we propose a consensus classification algorithm that avoids overfitting, we investigate the performance of these two types of techniques in convolutional neural network. Early stopping allowing to save the hyper-parameters of a model at the right time. And the dropout making the learning of the model harder allowing to gain up to more than 50\% by decreasing the loss rate of the model. Copyright {\textcopyright} 2022 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Computer vision, Convolution, Convolutional neural network, Convolutional neural networks, Data overfitting, Deep learning, Deep neural networks, Dropout, Early stopping, Forecasting, Learning algorithms, Learning systems, Machine-learning, Max-pooling, Overfitting, Predictive models, Training sets},
	doi = {10.5220/0011114900003179},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140895255\&doi=10.5220\%2f0011114900003179\&partnerID=40\&md5=286b687a1791aefc7394085755e790c8},
	author = {Sabiri, B. and Asri, B.E. and Rhanoui, M.}
}
@article {Hammouchi2022,
	title = {STRisk: A Socio-Technical Approach to Assess Hacking Breaches Risk},
	journal = {IEEE Transactions on Dependable and Secure Computing},
	year = {2022},
	note = {cited By 0},
	abstract = {Data breaches have begun to take on new dimensions and their prediction is becoming of great importance to organizations. Prior work has addressed this issue mainly from a technical perspective and neglected other interfering aspects such as the social media dimension. To fill this gap, we propose a STRisk predictive system where we expand the scope of the prediction task by bringing into play the social media dimension. We study over 3800 US organizations including both victim and non-victim organizations. For each organization, we design a profile composed of a variety of externally measured technical indicators and social factors. In addition, to account for unreported incidents, we consider the non-victim sample to be noisy and propose a noise correction approach to correct mislabeled organizations. We then build several machine learning models to predict whether an organization is exposed to experience a hacking breach. By exploiting both technical and social features, we achieve an Area Under Curve (AUC) score exceeding 98\%, which is 12\% higher than the AUC achieved using only technical features. Furthermore, our feature importance analysis reveals that open ports and expired certificates are the best technical predictors, while spreadability and agreeability are the best social predictors. IEEE},
	keywords = {Blogs, Cybe risk assessment, Cybe risk prediction, Data breach, Forecasting, Hacking breach, Learning systems, Malware, Noise measurements, Personal computing, Predictive models, Risk assessment, Risk predictions, Risks assessments, Security, Social networking (online)},
	doi = {10.1109/TDSC.2022.3149208},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124712380\&doi=10.1109\%2fTDSC.2022.3149208\&partnerID=40\&md5=f6b798bf0fc97476d3214d3efbc3597a},
	author = {Hammouchi, H. and Nejjari, N. and Mezzour, G. and Ghogho, M. and Benbrahim, H.}
}
@article {Houmz2021,
	title = {Detecting the impact of software vulnerability on attacks: A case study of network telescope scans},
	journal = {Journal of Network and Computer Applications},
	volume = {195},
	year = {2021},
	note = {cited By 1},
	abstract = {Network scanning is one of the first steps in gathering information about a target before launching attacks. It is used to scan for vulnerable devices and exposed services in order to exploit them. Such exploits can result in data breaches or network disruption, which can be very costly for organizations. There are many factors, including technical and non-technical, affecting the volume of scanning activities. In this paper, we study the impact of vulnerability disclosure on the volume of scans over time and propose a machine learning-based approach to predict this impact. We conducted a comprehensive data collection of network scans from two network telescopes hosted in different countries, as well as the disclosed vulnerabilities from 2014 to 2019. We then designed a set of features to characterize the disclosed vulnerabilities and used several classifiers to predict whether a vulnerability will impact the volume of daily scans. The resulting classifier achieves over 85\% accuracy in predicting the impact. In addition, we performed an analysis of the key characteristics of vulnerabilities that directly affect scanning activities. Our findings show that this approach is able to classify vulnerabilities that have an impact on network scans. The implementation of our model and validation tests proved the efficiency of the selected features, as well as the robustness of our model to classify vulnerabilities{\textquoteright} impact on scans. {\textcopyright} 2021 Elsevier Ltd},
	keywords = {Case-studies, Classification algorithm, CVE, Forecasting, Machine learning, Network scan, Network scanning, Network security, Network telescopes, NVD, OR-networks, Scanning, Software vulnerabilities, Telescopes, Times series},
	doi = {10.1016/j.jnca.2021.103230},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116335984\&doi=10.1016\%2fj.jnca.2021.103230\&partnerID=40\&md5=1d7f8d5d4111761b27fc93badc70f925},
	author = {Houmz, A. and Mezzour, G. and Zkik, K. and Ghogho, M. and Benbrahim, H.}
}
@article {Wadghiri2021544,
	title = {Ensemble Regression for Blood Glucose Prediction},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1365 AIST},
	year = {2021},
	note = {cited By 1},
	pages = {544-554},
	abstract = {Background: Predicting blood glucose present commonly many challenges when the designed models are tested under different contexts. Ensemble methods are a set of learning algorithms that have been successfully used in many medical fields to improve the prediction accuracy. This paper aims to review the typology of ensembles used in literature to predict blood glucose. Methods: 32 papers published between 2000 and 2020 in 6 digital libraries were selected and reviewed with regard to: years and publication sources, integrated factors and data sources used to collect the data and types of ensembles. Results: This review results found that this research topic is still recent but is gaining a growing interest in the last years. Ensemble models used often blood glucose, insulin, diet and exercise as input to predict blood glucose. Moreover, both homogeneous and heterogeneous ensembles have been investigated. Conclusions: An increasing interest have been devoted to blood glucose prediction using ensemble methods during the last decade. Several gaps regarding the design of the reviewed ensembles and the data collection process have been identified and recommendations have been formulated in this direction. {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
	keywords = {Blood, Data acquisition, Data collection process, Designed models, Digital libraries, Ensemble methods, Ensemble models, Forecasting, Glucose, Heterogeneous ensembles, Information systems, Information use, Learning algorithms, Medical fields, Prediction accuracy, Research topics},
	doi = {10.1007/978-3-030-72657-7_52},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105963359\&doi=10.1007\%2f978-3-030-72657-7_52\&partnerID=40\&md5=4ba83ca8e7ba5bae5fdcd172b58fb8e9},
	author = {Wadghiri, M.Z. and Idri, A. and Idrissi, T.E.}
}
@article {ElIdrissi2021347,
	title = {Evaluating a Comparing Deep Learning Architectures for Blood Glucose Prediction},
	journal = {Communications in Computer and Information Science},
	volume = {1400 CCIS},
	year = {2021},
	note = {cited By 0},
	pages = {347-365},
	abstract = {To manage their disease, diabetic patients need to control the blood glucose level (BGL) by monitoring it and predicting its future values. This allows to avoid high or low BGL by taking recommended actions in advance. In this paper, we conduct a comparative study of two emerging deep learning techniques: Long-Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for one-step and multi-steps-ahead forecasting of the BGL based on Continuous Glucose Monitoring (CGM) data. The objectives are twofold: 1) Determining the best strategies of multi-steps-ahead forecasting (MSF) to fit the CNN and LSTM models respectively, and 2) Comparing the performances of the CNN and LSTM models for one-step and multi-steps prediction. Toward these objectives, we firstly conducted series of experiments of a CNN model through parameters selection to determine its best configuration. The LSTM model we used in the present study was developed and evaluated in an earlier work. Thereafter, five MSF strategies were developed and evaluated for the CNN and LSTM models using the Root-Mean-Square Error (RMSE) with an horizon of 30~min. To statistically assess the differences between the performances of CNN and LSTM models, we used the Wilcoxon statistical test. The results showed that: 1) no MSF strategy outperformed the others for both CNN and LSTM models, and 2) the proposed CNN model significantly outperformed the LSTM model for both one-step and multi-steps prediction. {\textcopyright} 2021, Springer Nature Switzerland AG.},
	keywords = {Biomedical engineering, Blood, Blood glucose level, Comparative studies, Continuous glucosemonitoring (CGM), Convolutional neural networks, Deep learning, Diabetic patient, Disease control, Forecasting, Glucose, Learning architectures, Learning techniques, Long short-term memory, Mean square error, Parameters selection, Root mean square errors},
	doi = {10.1007/978-3-030-72379-8_17},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107268637\&doi=10.1007\%2f978-3-030-72379-8_17\&partnerID=40\&md5=2b36d3e75ce8d317c8b80f3272f4d956},
	author = {El Idrissi, T. and Idri, A.}
}
@article {Malek202156,
	title = {Multivariate deep learning approach for electric vehicle speed forecasting},
	journal = {Big Data Mining and Analytics},
	volume = {4},
	number = {1},
	year = {2021},
	note = {cited By 46},
	pages = {56-64},
	abstract = {Speed forecasting has numerous applications in intelligent transport systems{\textquoteright} design and control, especially for safety and road efficiency applications. In the field of electromobility, it represents the most dynamic parameter for efficient online in-vehicle energy management. However, vehicles{\textquoteright} speed forecasting is a challenging task, because its estimation is closely related to various features, which can be classified into two categories, endogenous and exogenous features. Endogenous features represent electric vehicles{\textquoteright} characteristics, whereas exogenous ones represent its surrounding context, such as traffic, weather, and road conditions. In this paper, a speed forecasting method based on the Long Short-Term Memory (LSTM) is introduced. The LSTM model training is performed upon a dataset collected from a traffic simulator based on real-world data representing urban itineraries. The proposed models are generated for univariate and multivariate scenarios and are assessed in terms of accuracy for speed forecasting. Simulation results show that the multivariate model outperforms the univariate model for short- and long-term forecasting. {\textcopyright} 2018 Tsinghua University Press.},
	keywords = {Deep learning, Design and control, Dynamic parameters, Electric vehicles, energy efficiency, Forecasting, Forecasting methods, Intelligent systems, Intelligent transport systems, Learning approach, Long short-term memory, Long-term forecasting, Multivariate modeling, Roads and streets, Speed, Traffic control, Traffic simulators, Vehicle actuated signals},
	doi = {10.26599/BDMA.2020.9020027},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85099566517\&doi=10.26599\%2fBDMA.2020.9020027\&partnerID=40\&md5=2faaf6f427bd230fa39a873be4e45566},
	author = {Malek, Y.N. and Najib, M. and Bakhouya, M. and Essaaidi, M.}
}
@conference {Gryech2021,
	title = {On spatial prediction of urban air pollution},
	booktitle = {2021 17th International Conference on Intelligent Environments, IE 2021 - Proceedings},
	year = {2021},
	note = {cited By 0},
	abstract = {Air pollution continues to draw global attention, and still causes adverse environmental issues and health effects. Questions about how air pollution evolves spatially are also still unsolved. Many air quality monitoring stations are deployed in several countries to give insight about air quality. However, it is quite frequent for these stations to go out of order during their long life time. These incidents may lead to a significant loss of pollution data. To mitigate this issue, we propose to leverage spatial correlation between pollution monitoring stations to predict the lost data. To reduce the complexity of the prediction model, for each monitoring station, we identify the best set of other stations to use in the prediction model. Correlation-based station selection is shown to outperform distance-based station selection and provides an R2 above 0.8 when applied to the Airparif datasets. {\textcopyright} 2021 IEEE.},
	keywords = {Air quality, Air quality monitoring stations, Environmental issues, Forecasting, Intelligent agents, Monitoring stations, Pollution monitoring, Prediction model, Predictive analytics, Spatial correlations, Spatial prediction, Urban air pollution},
	doi = {10.1109/IE51775.2021.9486618},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114499832\&doi=10.1109\%2fIE51775.2021.9486618\&partnerID=40\&md5=10a04db251b0bca163abb4d34f0c721a},
	author = {Gryech, I. and Ben-Aboud, Y. and Ghogho, M. and Kobbane, A.}
}
@conference {Lahrichi2021322,
	title = {Toward a multimodal multitask model for neurodegenerative diseases diagnosis and progression prediction},
	booktitle = {Proceedings of the 10th International Conference on Data Science, Technology and Applications, DATA 2021},
	year = {2021},
	note = {cited By 1},
	pages = {322-328},
	abstract = {Recent studies on modelling the progression of Alzheimer{\textquoteright}s disease use a single modality for their predictions while ignoring the time dimension. However, the nature of patient data is heterogeneous and time dependent which requires models that value these factors in order to achieve a reliable diagnosis, as well as making it possible to track and detect changes in the progression of patients{\textquoteright} condition at an early stage. This article overviews various categories of models used for Alzheimer{\textquoteright}s disease prediction with their respective learning methods, by establishing a comparative study of early prediction and detection Alzheimer{\textquoteright}s disease progression. Finally, a robust and precise detection model is proposed. Copyright {\textcopyright} 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved},
	keywords = {Alzheimer{\textquoteright}s disease, Comparative studies, Data Science, Detection models, Diagnosis, Early prediction, Forecasting, Hospital data processing, Learning methods, Learning systems, Multi-task model, Neurodegenerative diseases, Patients{\textquoteright} conditions, Time dependent},
	doi = {10.5220/0010600003220328},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111735908\&doi=10.5220\%2f0010600003220328\&partnerID=40\&md5=1f255250606020cc06dfdc4c73cc29c0},
	author = {Lahrichi, S. and Rhanoui, M. and Mikram, M. and Asri, B.E.}
}
@conference {Berradi2020367,
	title = {A comprehensive review of artificial intelligence techniques in financial market},
	booktitle = {Colloquium in Information Science and Technology, CIST},
	volume = {2020-June},
	year = {2020},
	note = {cited By 0},
	pages = {367-371},
	abstract = {Artificial intelligence is a vast and promising domain that helps improving people{\textquoteright}s lives in different areas such as, medicine, education, telecommunication, finance, and economy. The financial market is an important aspect of the economics of any country, by having a clear idea about how it functions, it helps to improve the economy of the country radically and, therefore, the people{\textquoteright}s lives. In this paper, we suggest giving the latest research of deep learning techniques applied on the financial market field that can help investors to make an accurate decision. This paper gathered all the recent articles related to deep learning techniques applied on forecasting the financial market, which includes stock market, stock index, commodity forecasting and Forex. The main goal is to find the most models used recently to solve the prediction problem using RNN, their characteristic and novelty. We will give all aspects that involve the process of forecasting beginning with preprocessing, the input features, the deep learning techniques, and the evaluation metrics employed. {\textcopyright} 2021 IEEE.},
	keywords = {Artificial intelligence techniques, Commerce, Deep learning, Electronic trading, Evaluation metrics, Financial markets, Fintech, Forecasting, Input features, Investments, It functions, Learning systems, Learning techniques, Prediction problem, Stock indices},
	doi = {10.1109/CiSt49399.2021.9357175},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103852221\&doi=10.1109\%2fCiSt49399.2021.9357175\&partnerID=40\&md5=b8ae02858f3f66114efcfced0ae9f173},
	author = {Berradi, Z. and Lazaar, M. and Mahboub, O. and Omara, H.}
}
@conference {Gad2020,
	title = {Crowd Density Estimation Method Using Reduced Features Set},
	booktitle = {2020 International Conference on Electrical and Information Technologies, ICEIT 2020},
	year = {2020},
	note = {cited By 0},
	abstract = {A new crowd density estimation method with reduced feature set is proposed that enhances both prediction errors and computational time in terms of feature extraction, training, and prediction times. Based on the filter, wrapper, and embedded approaches to feature selection, 9 different sets of selected features were generating. Features categories were ranked based on the number of features selected across all selection methods. Using each set of features, 7 different regression models got trained. Each model performance was assessed based on MSE, MAE, and MRE. Feature selection methods were ranked based on its robustness in reducing the prediction errors. Based on the experimental results, the proposed method outperforms the previous works. {\textcopyright} 2020 IEEE.},
	keywords = {Computational time, Crowd density, Feature extraction, Feature selection methods, Features sets, Forecasting, Logistic regression, Model performance, Prediction errors, Regression model, Selection methods},
	doi = {10.1109/ICEIT48248.2020.9113179},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086903221\&doi=10.1109\%2fICEIT48248.2020.9113179\&partnerID=40\&md5=e227463adf229bd65938fec34f2f0d2b},
	author = {Gad, A.F. and Ezzahra Jarmouni, F.}
}
@article {ElIdrissi2020379,
	title = {Deep Learning for Blood Glucose Prediction: CNN vs LSTM},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {12250 LNCS},
	year = {2020},
	note = {cited By 6},
	pages = {379-393},
	abstract = {To manage their disease, diabetic patients need to control the blood glucose level (BGL) by monitoring it and predicting its future values. This allows to avoid high or low BGL by taking recommended actions in advance. In this study, we propose a Convolutional Neural Network (CNN) for BGL prediction. This CNN is compared with Long-short-term memory (LSTM) model for both one-step and multi-steps prediction. The objectives of this work are: 1) Determining the best configuration of the proposed CNN, 2) Determining the best strategy of multi-steps forecasting (MSF) using the obtained CNN for a prediction horizon of 30~min, and 3) Comparing the CNN and LSTM models for one-step and multi-steps prediction. Toward the first objective, we conducted series of experiments through parameter selection. Then five MSF strategies are developed for the CNN to reach the second objective. Finally, for the third objective, comparisons between CNN and LSTM models are conducted and assessed by the Wilcoxon statistical test. All the experiments were conducted using 10 patients{\textquoteright} datasets and the performance is evaluated through the Root Mean Square Error. The results show that the proposed CNN outperformed significantly the LSTM model for both one-step and multi-steps prediction and no MSF strategy outperforms the others for CNN. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Blood, Blood glucose, Blood glucose level, Convolutional neural networks, Deep learning, Diabetic patient, Disease control, Forecasting, Glucose, Long short-term memory, Mean square error, Multi-step, Parameter selection, Prediction horizon, Root mean square errors},
	doi = {10.1007/978-3-030-58802-1_28},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093089262\&doi=10.1007\%2f978-3-030-58802-1_28\&partnerID=40\&md5=f16d576a801dcdbbac35f8593e4a50ea},
	author = {El Idrissi, T. and Idri, A.}
}
@article {ElFazziki2020136,
	title = {Improving Collaborative Filtering Approach by Leveraging Opposite Users},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1102 AISC},
	year = {2020},
	note = {cited By 0},
	pages = {136-145},
	abstract = {Collaborative filtering is a widely used recommendation approach that aims to predict for a target user the most appropriate items. This approach uses the ratings given by users who share similar tastes and preferences to predict ratings for items that haven{\textquoteright}t been rated yet. Despite its simplicity and justifiability, CF approach stills suffering from several drawbacks and problems, including sparsity, gray sheep and scalability. These problems affect the accuracy of the obtained results. In this work, we present a novel collaborative filtering approach based on the opposite preferences of users. We focus on enhancing the accuracy of predictions and dealing with gray sheep problem by inferring new similar neighbors based on users who have dissimilar tastes and preferences. For instance, if a user X is dissimilar to a user Y then the user +X is similar to the user Y. The Experimental results performed on two datasets including MovieLens and FilmTrust show that our approach outperforms several baseline recommendation techniques. {\textcopyright} Springer Nature Switzerland AG 2020.},
	keywords = {Collaborative filtering, Forecasting, Gray sheep, Intelligent systems, Learning systems, Movielens, Opposite neighbors, planning, Recommendation techniques, Recommender Systems, Similar neighbors, Similarity measure, Sustainable development},
	doi = {10.1007/978-3-030-36653-7_14},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078457465\&doi=10.1007\%2f978-3-030-36653-7_14\&partnerID=40\&md5=fdeb5a86480517f6637d45ebc49b0849},
	author = {El Fazziki, A. and El Madani El Alami, Y. and El Aissaoui, O. and El Allioui, Y. and Benbrahim, M.}
}
@article {Gryech2020379,
	title = {Machine learning for air quality prediction using meteorological and traffic related features},
	journal = {Journal of Ambient Intelligence and Smart Environments},
	volume = {12},
	number = {5},
	year = {2020},
	note = {cited By 4},
	pages = {379-391},
	abstract = {The presence of pollutants in the air has a direct impact on our health and causes detrimental changes to our environment. Air quality monitoring is therefore of paramount importance. The high cost of the acquisition and maintenance of accurate air quality stations implies that only a small number of these stations can be deployed in a country. To improve the spatial resolution of the air monitoring process, an interesting idea is to develop data-driven models to predict air quality based on readily available data. In this paper, we investigate the correlations between air pollutants concentrations and meteorological and road traffic data. Using machine learning, regression models are developed to predict pollutants concentration. Both linear and non-linear models are investigated in this paper. It is shown that non-linear models, namely Random Forest (RF) and Support Vector Regression (SVR), better describe the impact of traffic flows and meteorology on the concentrations of pollutants in the atmosphere. It is also shown that more accurate prediction models can be obtained when including some pollutants{\textquoteright} concentration as predictors. This may be used to infer the concentrations of some pollutants using those of other pollutants, thereby reducing the number of air pollution sensors. {\textcopyright} 2020 - IOS Press and the authors. All rights reserved.},
	keywords = {Accurate prediction, Air quality, Air quality monitoring, Air quality prediction, Data-driven model, Decision trees, Forecasting, Machine learning, Non-linear model, Nonlinear systems, Pollution sensors, Predictive analytics, Spatial resolution, Support vector regression, Support vector regression (SVR)},
	doi = {10.3233/AIS-200572},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093821240\&doi=10.3233\%2fAIS-200572\&partnerID=40\&md5=f8b7d726f054f01a2be432560d359fc5},
	author = {Gryech, I. and Ghogho, M. and Elhammouti, H. and Sbihi, N. and Kobbane, A.}
}
@article {ElAissaoui2020655,
	title = {Mining Learners{\textquoteright} Behaviors: An Approach Based on Educational Data Mining Techniques},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1076},
	year = {2020},
	note = {cited By 2},
	pages = {655-670},
	abstract = {Educational data mining is a research field that aims to apply data mining techniques in educational environments. Many data mining techniques such as clustering, classification, and prediction can be performed on educational data in order to analyze the learner behaviors. In this work, we have used the clustering and classification techniques to predict the learners{\textquoteright} learning styles. The students{\textquoteright} behaviors while using the e-learning system have been captured from the log file and given as an input of a clustering algorithm to group them into 16 clusters. The resulted clusters were labeled with learning styles combinations based on the Felder and Silverman learning style model. Then the labeled behaviors were given as input to four classifiers: naive Bayes, Cart, Id3, and C4.5 to compare their performance in predicting students{\textquoteright} learning styles. The four classifiers were performed using Weka data mining tool, and the obtained results showed that Id3 yielded better results than the other classifiers. {\textcopyright} Springer Nature Singapore Pte Ltd. 2020.},
	keywords = {Artificial intelligence, Classification technique, Clustering algorithms, Data mining, Data-mining tools, Educational data mining, Educational environment, Embedded systems, Forecasting, Learning Style, Learning systems, Log file, Naive bayes, Research fields, Students},
	doi = {10.1007/978-981-15-0947-6_62},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085173246\&doi=10.1007\%2f978-981-15-0947-6_62\&partnerID=40\&md5=0cb9665b2720bd9b06e011902e8708b5},
	author = {El Aissaoui, O. and El Alami El Madani, Y. and Oughdir, L. and Dakkak, A. and El Allioui, Y.}
}
@article {ElAissaoui20209,
	title = {A Multiple Linear Regression-Based Approach to Predict Student Performance},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1102 AISC},
	year = {2020},
	note = {cited By 15},
	pages = {9-23},
	abstract = {Predicting students{\textquoteright} academic outcome is useful for any educational institution that aims to ameliorate students{\textquoteright} performance. Based on the resulted predictions, educators can provide support to students at risk of failure. Data mining and machine learning techniques were widely used to predict students{\textquoteright} performance. This process called Educational data mining. In this work, we have proposed a methodology to build a student{\textquoteright} performance prediction model using a supervised machine learning technique which is the multiple linear regression (MLR). Our methodology consists of three major steps, the first step aims to analyze and preprocess the students{\textquoteright} attributes/variables using a set of statistical analysis methods, and then the second step consists in selecting the most important variables using different methods. The third step aims to construct different MLR models based on the selected variables and compare their performance using the k-fold cross-validation technique. The obtained results show that the model built using the variables selected from the Multivariate Adaptive Regression Splines method (MARS), outperforms the other constructed models. {\textcopyright} Springer Nature Switzerland AG 2020.},
	keywords = {Data mining, Education computing, Educational data mining, Forecasting, Intelligent systems, K fold cross validations, Learning systems, Linear regression, Machine learning techniques, Multiple linear regressions, Multivariate adaptive regression splines, Performance prediction models, planning, Predictive analytics, Statistical analysis methods, Students, Supervised learning, Supervised machine learning, Sustainable development},
	doi = {10.1007/978-3-030-36653-7_2},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078413089\&doi=10.1007\%2f978-3-030-36653-7_2\&partnerID=40\&md5=893772562739d5d20e6b6ae21ebae325},
	author = {El Aissaoui, O. and El Alami El Madani, Y. and Oughdir, L. and Dakkak, A. and El Allioui, Y.}
}
@conference {Elmidaoui2020,
	title = {Predicting software maintainability using ensemble techniques and stacked generalization},
	booktitle = {CEUR Workshop Proceedings},
	volume = {2725},
	year = {2020},
	note = {cited By 1},
	abstract = {The prediction of software maintainability has emerged as an important research topic to address industry expectations for reducing costs, in particular maintenance costs. In the last decades, many studies have used single techniques to predict software maintainability but there is no agreement as to which technique can achieve the best prediction. Ensemble techniques, which combine two or more techniques, have been investigated in recent years. This study investigates ensemble techniques (homogeneous as well as heterogeneous) for predicting maintainability in terms of line code changes. To this end, well-known homogeneous ensembles such as Bagging, Boosting, Extra Trees, Gradient Boosting, and Random Forest are investigated first. Then the stacked generalization method is used to construct heterogeneous ensembles by combining the most accurate ones per dataset. The empirical results suggest that Gradient Boosting and Extra Trees are the best ensembles for all datasets, since they ranked first and second, respectively. Moreover, the findings of the evaluation of heterogeneous ensembles constructed using stacked generalization showed that they gave better prediction accuracy compared to all homogeneous ensembles. Copyright {\textcopyright} 2020 for this paper by its authors.},
	keywords = {Decision trees, Ensemble techniques, Forecasting, Forestry, Gradient boosting, Heterogeneous ensembles, Maintainability, Maintenance cost, Prediction accuracy, Research topics, Software engineering, Software maintainability, Stacked generalization},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098182236\&partnerID=40\&md5=32bab56e3a64ff6efa7e8717d9ee67c4},
	author = {Elmidaoui, S. and Cheikhi, L. and Idri, A. and Abran, A.}
}
@article {Filali202015947,
	title = {Preemptive SDN Load Balancing with Machine Learning for Delay Sensitive Applications},
	journal = {IEEE Transactions on Vehicular Technology},
	volume = {69},
	number = {12},
	year = {2020},
	note = {cited By 12},
	pages = {15947-15963},
	abstract = {SDN is a key-enabler to achieve scalability in 5G and Multi-access Edge Computing networks. To balance the load between distributed SDN controllers, the migration of the data plane components has been proposed. Different from most previous works which use reactive mechanisms, we propose to preemptively balance the load in the SDN control plane to support network flows that require low latency communications. First, we forecast the load of SDN controllers to prevent load imbalances and schedule data plane migrations in advance. Second, we optimize the migration operations to achieve better load balancing under delay constraints. Specifically, in the first step, we construct two prediction models based on Auto Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) approaches to forecast SDN controllers load. Then, we conduct a comparative study between these two models and calculate their accuracies and forecast errors. The results show that, in long-term predictions, the accuracy of LSTM model outperforms that of ARIMA by 55\% in terms of prediction errors. In the second step, to select which data plane components to migrate and where the migration should happen under delay constraints, we formulate the problem as a non-linear binary program, prove its NP-completeness and propose a reinforcement learning algorithm to solve it. The simulations show that the proposed algorithm performs close to optimal and outperforms recent benchmark algorithms from the literature. {\textcopyright} 1967-2012 IEEE.},
	keywords = {5G mobile communication systems, Auto-regressive integrated moving average, Autoregressive moving average model, Balancing, Comparative studies, Controllers, Delay constraints, Delay-sensitive applications, Forecasting, Learning algorithms, Long short-term memory, Long-term prediction, Low-latency communication, Prediction errors, Predictive analytics, Reactive mechanism, Reinforcement learning},
	doi = {10.1109/TVT.2020.3038918},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097173809\&doi=10.1109\%2fTVT.2020.3038918\&partnerID=40\&md5=4dc77968a74ba95fb61deec16c598fbb},
	author = {Filali, A. and Mlika, Z. and Cherkaoui, S. and Kobbane, A.}
}
@conference {ElIdrissi2020337,
	title = {Strategies of multi-step-ahead forecasting for blood glucose level using LSTM neural networks: A comparative study},
	booktitle = {HEALTHINF 2020 - 13th International Conference on Health Informatics, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020},
	year = {2020},
	note = {cited By 3},
	pages = {337-344},
	abstract = {Predicting the blood glucose level (BGL) is crucial for self-management of Diabetes. In general, a BGL prediction is done based on the previous measurements of BGL, which can be taken either (manually) by using sticks or (automatically) by using continuous glucose monitoring (CGM) devices. To allow the diabetic patients to take appropriate actions, the BGL predictions should be done ahead of time; thus a multi-step ahead prediction is suitable. Therefore, many Multi-Step-ahead Forecasting (MSF) strategies have been developed and evaluated, and can be categorized in five types: Recursive, Direct, MIMO (for Multiple Input Multiple Output), DirMO (combining Direct and MIMO) and DirRec (combining Direct and Recursive). However, none of them is known to be the best strategy in all contexts. The present study aims at: 1) reviewing the MSF strategies, and 2) determining the best strategy to fit with a LSTM Neural Network model. Hence, we evaluated and compared in terms of two performance criteria: Root-Mean-Square Error (RMSE) and Mean Absolute Error (MAE), the five MSF strategies using a LSTM Neural Network with an horizon of 30 minutes. The results show that there is no strategy that significantly outperformed others when using the Wilcoxon statistical test. However, when using the Sum Ranking Differences method, MIMO is the best strategy for both RMSE and MAE criteria. {\textcopyright} 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.},
	keywords = {Biomedical engineering, Blood, Blood glucose level, Comparative studies, Continuous glucosemonitoring (CGM), Forecasting, Glucose, Long short-term memory, Mean absolute error, Mean square error, Medical informatics, MIMO systems, Multi-step-ahead predictions, Neural network model, Performance criterion, Root mean square errors},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083722214\&partnerID=40\&md5=bd9e45a8538559492b02432e5567d10e},
	author = {El Idrissi, T. and Idri, A. and Kadi, I. and Bakkoury, Z.}
}
