Multiple Sclerosis (MS) is a chronic neuroinflammatory disease of the Central Nervous System (CNS) in which the body's immune system attacks and destroys the myelin sheath that protects nerve fibers, leading to a wide range of debilitating symptoms and causing disruption of axonal signal transmission. Accurate prediction, diagnosis, monitoring and treatment (PDMT) of MS are essential to improve patient outcomes. Recent advances in neuroimaging technologies, particularly electroencephalography (EEG), combined with machine learning (ML) techniques - including Deep Learning (DL) models - offer promising avenues for enhancing MS management. This systematic review synthesizes existing research on the application of ML and DL models to EEG data for MS. It explores the methodologies used, with a focus on DL architectures such as Convolutional Neural Networks (CNNs) and hybrid models, and highlights recent advancements in ML techniques and EEG technologies that have significantly improved MS diagnosis and monitoring. The review addresses the challenges and potential biases in using ML-based EEG analysis for MS. Strategies to mitigate these challenges, including advanced preprocessing techniques, diverse training datasets, cross-validation methods, and explainable Artificial Intelligence (AI), are discussed. Finally, the paper outlines potential future applications and trends in ML for MS management. This review underscores the transformative potential of ML-enhanced EEG analysis in improving MS management, providing insights into future research directions to overcome existing limitations and further improve clinical practice.

Given the large and growing volume of big data and frequent use of complex analytical queries, understanding energy efficiency of query processing has become a critical research issue, as highlighted by database systems papers in the last few years. Common software solutions mainly consider IO cost models to estimate energy consumption when executing queries. On the other hand, current hardware solutions benefit from advances in the development of green components and their associated tuning techniques, especially dynamic voltage and frequency scaling (DVFS), which can balance the performance and power consumption of multicore CPUs. Unfortunately, to the best of our knowledge, there is an absence of solutions mixing both (hardware and software). Heeding this gap, we propose a novel predictive model to measure and predict energy consumption of analytical queries when using multi-core processors and different frequency configurations. We first experimentally illustrate the surprising impact of CPU frequency and the number of processor cores on execution time, power and energy consumption. Second, we introduce an extended predictive model that enriches a well-known machine learning cost model with our new angle, the frequency scaling in multi-core environment. Specifically, by using Support Vector Regression and Random Forest Regression, we compute the energy coefficients of an accurate regression model for energy prediction. Experiments with benchmark data sets TPC-H and TPC-DS evaluate our proposed framework in terms of energy consumption reduction, showing promising results.

The Personalized systems are generally based on collecting and exploiting users’ preferences by exploring their traces’ data. Actually, they find users who have similar attributes, cluster them, and then applying algorithms using the subnets. The similarity between users compares their profiles including their attributes. The adding of tags to enrich the user profile must take into consideration the long and short term criteria of the user’s attributes that change over time. In this paper, we present a tag-based profile enrichment approach by adding a time score describing the short and long term criteria of the attribute. Then we use graph analytics to draw clusters of users by inspecting similar tags. Our approach helps companies to make their predictions and conclusions. The datasets of patients’ images ChestX-Ray14 have been conducted to evaluate the effectiveness of our approach.

Summary To support health professionals in making decisions, CDSS are developed to manage the patients' EHR, improve the way of diagnosis, and treatment of diseases. The process of analyzing EHRs is based on reading free‐text notes. However, it spends time and physicians' efforts. In this case, the most used solution is describing the EHRs with shortcut tags, representing pathologies or diseases, which are well‐defined and meaningful information. Still, this solution remains insufficient. The exploration of the relationship between those tags, the EHRs and their belonging patients will improve the analysis and then the CDSS. In this paper, we present a graph‐based tag recommendation approach that suggests relevant tags (diseases and pathologies) by analyzing the tagged medical images. We use graph analytics to generate graphs of tags, patients, and images by inspecting similar medical images descriptive. We have also created sub‐communities of patients with the same diseases by applying the Louvain clustering method. The tag recommendation aims to enhance the computer‐aided diagnosis in medical imaging. The tag recommendation approach will allow radiologists to detect and interpret invisible diseases of the underlying anatomical structure. It will also help in early revealing and diagnosis. The dataset ChestX‐Ray14 has been conducted to evaluate and test the accuracy and effectiveness of the proposed approach. Future perspective will focus on the deployment of our proposal within a Moroccan e‐health project.

Pervasive computing, facilitates robustly and practically the access to any information wished, in an amount of time. Its ultimate goal is to integrate seamless technologies into our daily in order to make our life much easier, then we benefit all services anywhere, at any time. Learning has always been paramount, not limited to a certain age, in any form whatsoever (informal as well as the formal learning). It keep making great progress, thanks to pervasive computing technologies. Our article aims to show a ludic learning context, where learners being knowledgeable in various specified fields while having fun. Combining reality and virtuality, pervasive or mixed reality games marks relevant results. In this paper, we present the wider context, we recall game concepts, gamification as well as edutainment and we reveal the notion of serious games, multitude projects in this context. We conclude in highlight our vision, to conceive an Educational Pervasive Serious Adaptive Game EPSAG, its pedagogical contribution on the higher education system in Morocco generally and University Cadi Ayaad (UCA) particularly.

The end user service development known as the user-centric SOA emerged as a new approach that allows giving the end user the ability to create on the fly his own applications that meet a situational need. In fact, the classical SOA was designed for developers and is characterized by a heavy technical stack which is out of reach of end users. Lightweight Web 2.0 technologies such as Mashup appeared to bridge this gap and provide a new agile and quick way to compose and integrate different resources in a dynamic and on the fly manner. However, Mashups are emerging applications, and thus consist of immature, non intuitive and non formalized area. In this paper, we formalize the user-centric SOA development by introducing a new rich integration language based on the advanced Enterprise Integration Patterns (EIPS). We also propose a new intuitive and self-explanatory semantic methodology and interaction model for end users services integration.

The elearning is a distance learning system which offers training courses and custom tailored to the needs of learners. It is a measure allowing users to progress at their own pace.