Titlebook: Research in Computer Science; 6th Conference, CRI Paulin Melatagia Yonta,Kamel Barkaoui,Omer-Blaise Conference proceedings 2024 The Edito

PRISE

,Time Aware Implicit Social Influence Estimation to?Enhance Recommender Systems Performances,icant role in our daily lives. However, with the constantly growing addition of items on these platforms, it becomes challenging for users to select the products that interest them. Hence, the implementation of recommender systems to facilitate this selection process. To enhance these recommender sy

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,Analysis of?COVID-19 Coughs: From the?Mildest to?the?Most Severe Form, a?Realistic Classification Un over 600 million positive cases and over 6 million deaths worldwide. Therefore, an efficient, inexpensive, and ubiquitous diagnostic tool is essential to help fight lung disease and the COVID-19 crisis. Deep learning and machine learning algorithms can be used to analyze the cough sounds of infect

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,SLCDeepETC: An On-Demand Analysis Ready Data Pipeline on?Sentinel-1 Single Look Complex for?Deep Legle Look Complex products from Sentinel-1 to predict some environmental phenomena using Deep Learning. By retaining all original sensor measurements, it has been proven that interferometry data on Single Look Complex products, when analyzed with Deep Learning, can better inform data restoration, coh

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尖酸一點

,Explaining Meta-learner’s Predictions: Case of?Corporate CO2 Emissions,fore necessary for companies to control and reduce their pollution levels, and this requires knowing the amount of CO. emissions that can be produced and identifying the factors responsible for it. Several works have been carried out with the aim of predicting the quantity of CO. emitted at the comp

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ALIAS

,A Hybrid Algorithm Based on?Tabu Search and?K-Means for?Solving the?Traveling Salesman Problem,earch (TS). In this hybrid approach, we first apply the K-means algorithm to group cities into several clusters. Then we use tabu search to explore the solution space to optimise the path within each cluster. This avoids getting stuck in local optima and allows us to explore new, potentially better

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,Hybridization of?a?Recurrent Neural Network by?Quadratic Programming for?Combinatory Optimization: ss, but they are still incomplete because they don’t take into account the changing trend of electricity demand in buildings. In this paper, we propose a novel approach to minimizing joule loss using a hybridization of a recurrent neural network (RNN) and quadratic programming (QP). The RNN is used