@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.} } @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 {Ouaddi2020, title = {A comparative study of three methods for solving the Mutli-Trip Dynamic Vehicle Routing Problem with Overtime (MTDVRPOT)}, booktitle = {Proceedings - 2020 5th International Conference on Logistics Operations Management, GOL 2020}, year = {2020}, note = {cited By 0}, abstract = {In recent years, the dynamic vehicle routing problem (DVRP) and its variants have been increasingly studied and several resolution approaches have been proposed. The objective of this study is to compare the results of three different approaches designed for the multi-trip dynamic vehicle routing problem with overtime (MTDVRPOT). The first approach is based on an exact method, the second is a hybrid ant colony system, while the third is a memetic algorithm. The tests are performed on small instances, and the results show the efficiency of the memetic algorithm. {\textcopyright} 2020 IEEE.}, keywords = {Ant colony optimization, Comparative studies, Dynamic vehicle routing problems, Exact methods, Hybrid ant colony systems, Memetic algorithms, Multi trips, Vehicle routing, Vehicles}, doi = {10.1109/GOL49479.2020.9314727}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100211299\&doi=10.1109\%2fGOL49479.2020.9314727\&partnerID=40\&md5=b18f63526f7e1ac4b3feb37e42ce0faa}, author = {Ouaddi, K. and Mhada, F.-Z. and Benadada, Y.} } @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.} } @conference {Reda202091, title = {Towards a data quality assessment in big data}, booktitle = {ACM International Conference Proceeding Series}, year = {2020}, note = {cited By 4}, pages = {91-96}, abstract = {In recent years, as more and more data sources have become available and the volumes of data potentially accessible have increased, the assessment of data quality has taken a central role whether at the academic, professional or any other sector. Given that users are often concerned with the need to filter a large amount of data to better satisfy their requirements and needs, and that data analysis can be based on inaccurate, incomplete, ambiguous, duplicated and of poor quality, it makes everyone wonder what the results of these analyses will really be like. However, there is a very complex process involved in the identification of new, valid, potentially useful and meaningful data from a large data collection and various information systems, and is critically dependent on a number of measures to be developed to ensure data quality. To this end, the main objective of this paper is to introduce a general study on data quality related with big data, by providing what other researchers came up with on that subject. The paper will be finalized by a comparative study between the different existing data quality models. {\textcopyright} 2020 ACM.}, keywords = {Big data, Comparative studies, Complex Processes, data quality, Data quality assessment, Data quality models, Data reduction, Data-sources, Intelligent systems, Large amounts, Large data}, doi = {10.1145/3419604.3419803}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096426287\&doi=10.1145\%2f3419604.3419803\&partnerID=40\&md5=7542ef0e44f4d94b808c64ce466a5eca}, author = {Reda, O. and Sassi, I. and Zellou, A. and Anter, S.} } @conference {Khannat202020, title = {Towards mining semantically enriched configurable process models}, booktitle = {ACM International Conference Proceeding Series}, year = {2020}, note = {cited By 0}, pages = {20-24}, abstract = {Providing configurable process model with high quality is a primary objective to derive process variants with better accuracy and facilitate process model reuse. For this purpose, many research works have been interested in configurable process mining techniques to discover and configure processes from event logs. Moreover, to use the knowledge captured by event logs when mining processes, the concept of semantic process mining is introduced. It allows for combining semantic technologies with process mining. Despite the diversity of works in mining and customizing configurable process models, the application of these techniques is still limited to use semantics in minimizing the complexity of discovered processes. However, it seems to be pertinent to discover semantically enriched configurable process models directly from event logs. Consequently, this can facilitate using semantic in configuring, verifying conformance or enhancing discovered configurable processes. In this paper, we present a comparative study of existing works that focus on mining configurable process models with respect to semantic technologies. Our aim is to propose a new framework to automatically discover semantically enriched configurable processes. {\textcopyright} 2020 ACM.}, keywords = {Comparative studies, Configurable process models, Data mining, Intelligent systems, Mining process, Primary objective, Process mining, Process Modeling, Process variants, Semantic technologies, Semantic Web, Semantics}, doi = {10.1145/3419604.3419797}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096408590\&doi=10.1145\%2f3419604.3419797\&partnerID=40\&md5=a77606eacf64646ccd3459e57f5cabdf}, author = {Khannat, A. and Sba{\"\i}, H. and Kjiri, L.} } @article {9434372520130201, title = {Ant colony algorithm for the multi-depot vehicle routing problem in large quantities by a heterogeneous fleet of vehicles.}, journal = {INFOR}, volume = {51}, number = {1}, year = {2013}, pages = {31 - 40}, abstract = {This article describes a heuristic method based on an ant colony algorithm for the multi-depot vehicle routing problem in large quantities by a heterogeneous fleet of vehicles. Test results on different problem instances are presented and compared with those obtained by CPLEX and by a previous constructive heuristic. [ABSTRACT FROM AUTHOR]}, keywords = {Ant algorithms, ant colony algorithm, Comparative studies, Computer networks, Heuristic algorithms, Information technology, Keywords Distribution, products in large quantities, Vehicle routing problem}, issn = {03155986}, url = {http://search.ebscohost.com/login.aspx?direct=true\&db=bth\&AN=94343725\&site=ehost-live}, author = {Benslimane, Mohammed Taha and Benadada, Youssef} }