Predicting Students’Final Performance Using Artificial Neural Networks

Artificial Intelligence(AI)is based on algorithms that allow machines to make decisions for humans.This technology enhances the users’experience in various ways.Several studies have been conducted in the field of education to solve the problem of student orientation and performance using various Machine Learning(ML)algorithms.The main goal of this article is to predict Moroccan students’performance in the region of Guelmim Oued Noun using an intelligent system based on neural networks,one of the best data mining techniques that provided us with the best results.

Application of Internet of Things in the Health Sector:Toward Minimizing Energy Consumption

The Internet of Things(IoT)is currently reflected in the increase in the number of connected objects,that is,devices with their own identity and computing and communication capacities.IoT is recognized as one of the most critical areas for future technologies,gaining worldwide attention.It applies to many areas,where it has achieved success,such as healthcare,where a patient is monitored using nodes and lightweight sensors.However,the powerful functions of IoT in the medical field are based on communication,analysis,processing,and management of data autonomously without any manual intervention,which presents many difficulties,such as energy consumption.However,these issues significantly slow down the development and rapid deployment of this technology.The main causes of wasted energy from connected objects include collisions that occur when two or more nodes send data simultaneously and the leading cause of data retransmission that occurs when a collision occurs or when data are not received correctly due to channel fading.The distance between nodes is one of the factors influencing energy consumption.In this article,we have proposed direct communication between nodes to avoid collision domains,which will help reduce data retransmission.The results show that the distribution can ensure the performance of the system under general conditions compared to the centralization and to the existing works.

Enhancing resource allocation in edge and fog-cloud computing with genetic algorithm and particle swarm optimization

Evolutionary algorithms have gained significant attention from researchers as effective solutions for various optimization problems.Genetic Algorithm(GA)is widely popular due to its logical approach,broad applicability,and ability to tackle complex issues encountered in engineering systems.However,GA is known for its high implementation cost and typically requires a large number of iterations.On the other hand,Particle Swarm Optimization(PSO)is a relatively new heuristic technique inspired by the collective behaviors of real organisms.Both GA and PSO algorithms are prominent heuristic optimization methods that belong to the population-based approaches family.While they are often seen as competitors,their efficiency heavily relies on the parameter values chosen and the specific optimization problem at hand.In this study,we aim to compare the runtime performance of GA and PSO algorithms within a cutting-edge edge and fog cloud architecture.Through extensive experiments and performance evaluations,the authors demonstrate the effectiveness of GA and PSO algorithms in improving resource allocation in edge and fog cloud computing scenarios using FogWorkflowSim simulator.The comparative analysis sheds light on the strengths and limitations of each algorithm,providing valuable insights for researchers and practitioners in the field.