@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.}
}
@article {ElHafidy2021,
	title = {Gamified mobile applications for improving driving behavior: A systematic mapping study},
	journal = {Mobile Information Systems},
	volume = {2021},
	year = {2021},
	note = {cited By 4},
	abstract = {Many research works and official reports approve that irresponsible driving behavior on the road is the main cause of accidents. Consequently, responsible driving behavior can significantly reduce accidents{\textquoteright} number and severity. Therefore, in the research area as well as in the industrial area, mobile technologies are widely exploited in assisting drivers in reducing accident rates and preventing accidents. For instance, several mobile apps are provided to assist drivers in improving their driving behavior. Recently and thanks to mobile cloud computing, smartphones can benefit from the computing power of servers in the cloud for executing machine learning algorithms. Therefore, many mobile applications of driving assistance and control are based on machine learning techniques to adjust their functioning automatically to driver history, context, and profile. Additionally, gamification is a key element in the design of these mobile applications that allow drivers to develop their engagement and motivation to improve their driving behavior. To have an overview concerning existing mobile apps that improve driving behavior, we have chosen to conduct a systematic mapping study about driving behavior mobile apps that exist in the most common mobile apps repositories or that were published as research works in digital libraries. In particular, we should explore their functionalities, the kinds of collected data, the used gamification elements, and the used machine learning techniques and algorithms. We have successfully identified 220 mobile apps that help to improve driving behavior. In this work, we will extract all the data that seem to be useful for the classification and analysis of the functionalities offered by these applications. {\textcopyright} 2021 Abderrahim El hafidy et al.},
	keywords = {Accidents, Computing power, Digital libraries, Driving assistance, Driving behavior, Gamification, Industrial area, Industrial research, Learning algorithms, Machine learning, Machine learning techniques, Mapping, Mobile applications, Mobile cloud computing, Mobile computing, Mobile Technology, Systematic mapping studies},
	doi = {10.1155/2021/6677075},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114117887\&doi=10.1155\%2f2021\%2f6677075\&partnerID=40\&md5=6ad1b624deabb49917039716eb133f13},
	author = {El Hafidy, A. and Rachad, T. and Idri, A. and Zellou, A.}
}
@article {Karbal2020157,
	title = {A Comparison of Different Machine Learning Algorithms for Intrusion Detection},
	journal = {Communications in Computer and Information Science},
	volume = {1264},
	year = {2020},
	note = {cited By 0},
	pages = {157-169},
	abstract = {With the rapid development of the internet, intrusion detection became one of the major research problems in computer security. Many Intrusion Detection Systems (IDS) use data mining algorithms for classifying network traffic data and detecting different security violations. In this paper, we present some of the datasets and methods employed with the focus on network anomaly detection. We compare different machine learning techniques used in the latest research carried out for developing network intrusion detection systems. We also present an overview of some deep learning methodologies and their application for IDS purposes. The primary objective of this survey is to provide with a researcher, the state of the artwork already performed in this field of research. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Anomaly detection, Classification (of information), Computer crime, Data communication systems, Data mining, Data mining algorithm, Deep learning, Intrusion detection, Intrusion Detection Systems, Learning algorithms, Learning systems, Machine learning techniques, Network anomaly detection, Network intrusion detection systems, Network security, Primary objective, Research problems, Security violations},
	doi = {10.1007/978-3-030-61143-9_13},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097408678\&doi=10.1007\%2f978-3-030-61143-9_13\&partnerID=40\&md5=3725b891261e386c6df84eebcbc0e514},
	author = {Karbal, B. and Romadi, R.}
}
@article {Kamri2020298,
	title = {Machine Learning Approach for Smart Self-diagnosing Autonomic Computing Systems},
	journal = {Advances in Intelligent Systems and Computing},
	volume = {1105 AISC},
	year = {2020},
	note = {cited By 0},
	pages = {298-307},
	abstract = {While modern systems and networks are continuously growing in size, complexity and diversity, the monitoring and diagnosing of such systems is becoming a real challenge. Technically and economically, more automation of the classical diagnosing tasks is needed. This has triggered a considerable research initiative, grouped under the terms self-management and Autonomic Computing. In this paper we propose a new model for smart self-diagnosing systems based on Autonomic Computing principles and Machine Learning techniques. {\textcopyright} 2020, Springer Nature Switzerland AG.},
	keywords = {Autonomic Computing, Autonomic computing system, Diagnosing system, Intelligent systems, Learning systems, Machine learning, Machine learning approaches, Machine learning techniques, planning, Research initiatives, Self management, Self-diagnosing, Sustainable development, wireless networks},
	doi = {10.1007/978-3-030-36674-2_31},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080896883\&doi=10.1007\%2f978-3-030-36674-2_31\&partnerID=40\&md5=3dce8bc657338047166fb3ded16c10c8},
	author = {Kamri, H. and Bounabat, B.}
}
@article {Sebbar20205875,
	title = {MitM detection and defense mechanism CBNA-RF based on machine learning for large-scale SDN context},
	journal = {Journal of Ambient Intelligence and Humanized Computing},
	volume = {11},
	number = {12},
	year = {2020},
	note = {cited By 15},
	pages = {5875-5894},
	abstract = {Software defined network (SDN) is a promising new network abstraction that aims to improve and facilitate network management. Due to its centralized architecture and the lack of intelligence on the data plane, SDN suffers from many security issues that slows down its deployment. Man in the Middle (MitM) attack is considered as one of the most devastating attacks in an SDN context. In fact, MitM attack allows the attackers to capture, duplicate and spoof flows by targeting southbound interfaces and SDN nodes. Furthermore, it{\textquoteright}s very difficult to detect MitM attacks since it is performed passively at the SDN level. To reduce the impact of this attack, we generally set up security policies and authentication mechanisms. However, these techniques are not applicable for a large scale SDN architecture as they require complexes and static configurations and as they negatively influence on network performance. In this paper, we propose an intrusion detection and prevention framework by using machine learning techniques to detect and stop MitM attempts. To do so, we build a context-based node acceptance based on the random forest model (CBNA-RF), which helps to setting-up appropriate security policies and to automating defense operations on a large-scale SDN context. This mechanism can realize a quick and early detection of MitM attacks by automatically detecting malicious nodes without affecting performances. The evaluation of the proposed framework demonstrates that our model can correctly classify and detect malicious connections and nodes while keeping high accuracy and precision scores. {\textcopyright} 2020, Springer-Verlag GmbH Germany, part of Springer Nature.},
	keywords = {Authentication mechanisms, Centralized architecture, Decision trees, Defense operations, Intrusion detection, Intrusion detection and prevention, Machine learning, Machine learning techniques, Man-In-The-Middle (MITM) Attack, Network abstractions, Network architecture, Network security, Random forest modeling, Security systems},
	doi = {10.1007/s12652-020-02099-4},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085987877\&doi=10.1007\%2fs12652-020-02099-4\&partnerID=40\&md5=cf120767ba875958e97b87ce22203057},
	author = {Sebbar, A. and Zkik, K. and Baddi, Y. and Boulmalf, M. and Kettani, M.D.E.-C.E.}
}
@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.}
}
@article {Elkhoukhi2020,
	title = {A platform architecture for occupancy detection using stream processing and machine learning approaches},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {32},
	number = {17},
	year = {2020},
	note = {cited By 15},
	abstract = {Context-awareness in energy-efficient buildings has been considered as a crucial fact for developing context-driven control approaches in which sensing and actuation tasks are performed according to the contextual changes. This could be done by including the presence of occupants, number, actions, and behaviors in up-to-date context, taking into account the complex interlinked elements, situations, processes, and their dynamics. However, many studies have shown that occupancy information is a major leading source of uncertainty when developing control approaches. Comprehensive and real-time fine-grained occupancy information has to be, therefore, integrated in order to improve the performance of occupancy-driven control approaches. The work presented in this paper is toward the development of a holistic platform that combines recent IoT and Big Data technologies for real-time occupancy detection in smart building. The purpose of this work focuses mainly on the presence of occupants by comparing both static and dynamic machine learning techniques. An open-access occupancy detection dataset was first used to assess the usefulness of the platform and the effectiveness of static machine learning strategies for data processing. This dataset is used for applications that follow the strategy aiming at storing data first and processing it later. However, many smart buildings{\textquoteright} applications, such as HVAC and ventilation control, require online data streams processing. Therefore, a distributed real-time machine learning framework was integrated into the platform and tested to show its effectiveness for this kind of applications. Experiments have been conducted for ventilation systems in energy-efficient building laboratory (EEBLab) and preliminary results show the effectiveness of this platform in detecting on-the-fly presence of occupants, which is required to either make ON or OFF the system and then activate the corresponding embedded control technique (eg, ON/OFF, PID, state-feedback). {\textcopyright} 2019 John Wiley \& Sons, Ltd.},
	keywords = {Context- awareness, Data streams, Embedded systems, energy efficiency, Energy efficient building, Intelligent buildings, Internet of things, Learning systems, Machine learning, Machine learning approaches, Machine learning techniques, Occupancy detections, Platform architecture, Real-time data processing, State feedback, Ventilation, Ventilation control},
	doi = {10.1002/cpe.5651},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076920865\&doi=10.1002\%2fcpe.5651\&partnerID=40\&md5=f34bbe0cd8dadd04282227e1a5698c57},
	author = {Elkhoukhi, H. and NaitMalek, Y. and Bakhouya, M. and Berouine, A. and Kharbouch, A. and Lachhab, F. and Hanifi, M. and El Ouadghiri, D. and Essaaidi, M.}
}
@conference {Echihabi20201,
	title = {Scalable Machine Learning on High-Dimensional Vectors: From Data Series to Deep Network Embeddings},
	booktitle = {ACM International Conference Proceeding Series},
	volume = {Part F162565},
	year = {2020},
	note = {cited By 0},
	pages = {1-6},
	abstract = {There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to analyze very large collections of static and streaming sequences (a.k.a. data series), predominantly in real-time. Examples of such applications come from Internet of Things installations, neuroscience, astrophysics, and a multitude of other scientific and application domains that need to apply machine learning techniques for knowledge extraction. It is not unusual for these applications, for which similarity search is a core operation, to involve numbers of data series in the order of hundreds of millions to billions, which are seldom analyzed in their full detail due to their sheer size. Such application requirements have driven the development of novel similarity search methods that can facilitate scalable analytics in this context. At the same time, a host of other methods have been developed for similarity search of high-dimensional vectors in general. All these methods are now becoming increasingly important, because of the growing popularity and size of sequence collections, as well as the growing use of high-dimensional vector representations of a large variety of objects (such as text, multimedia, images, audio and video recordings, graphs, database tables, and others) thanks to deep network embeddings. In this work, we review recent efforts in designing techniques for indexing and analyzing massive collections of data series, and argue that they are the methods of choice even for general high-dimensional vectors. Finally, we discuss the challenges and open research problems in this area. {\textcopyright} 2020 Owner/Author.},
	keywords = {Application requirements, Astrophysics, Deep learning, Designing techniques, Embeddings, High-dimensional, Intelligent systems, Knowledge extraction, Learning systems, Machine learning techniques, Real time systems, Research problems, Scalable machine learning, Semantics, Similarity search, Vectors, Video recording},
	doi = {10.1145/3405962.3405989},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091520891\&doi=10.1145\%2f3405962.3405989\&partnerID=40\&md5=b3b9f8c365e940d7a488d9a9a20df419},
	author = {Echihabi, K. and Zoumpatianos, K. and Palpanas, T.}
}
