Department of Computer Science
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Item Application et modélisation d'un protocole de communication pour la sécurité routière(University Badi Mokhtar Annaba, 2014-10-25) BEKTACHE DJAMEL Chérif Tolba , Ghoualmi NaceraItem Adaptation with Four Dimensional Personalization Criteria Based on Felder Silverman Model(IGI GLOBAL, 2017) Samia Drissi; Abdelkrim AmiratIn the past decades, various systems have been proposed to provide students with a better learning environment by taking personal factors into account. Learning styles have been one of the widely adopted factors in the previous studies as a reference for adapting learning content or organizing the content. However, very few researchers give an idea of matching e-media with appropriate teaching and learning styles and very few studies give an idea of which appropriate combinations of electronic media and learning styles are more effective than other. In this paper, the authors aim to prototype an AFDPC-FS system (Adaptation with Four Dimensional Personalization Criteria based on Felder Silverman model). Their system presents a general framework for combining and adapting teaching strategies, learning styles and electronic media according to Felder-Silverman’slearning style model. An experiment was designed to explore the effect of adaptation to different learning styles when learning materials were matched with learning styles. In particular it was set up to see whether there are significant differences in learning achievement and cognitive load between two groups, an experimental group who studied with learning style-fit version and a control group who studied with non-fit version of the system without adaptation to learning styles. The experimental results showed that the proposed system could improve the learning achievements of the students. Moreover, it was found that the students’ cognitive load was significantly decreasedItem An Adaptive E-Learning System based on Student’s Learning Styles: An Empirical Stud(IGI GLOBAL, 2016) Samia Drissi; Abdelkrim AmiratPersonalized e-learning implementation is recognized as one of the most interesting research areas in the distance web-based education. Since the learning style of each learner is different one must fit e-learning with the different needs of learners. This paper presents an approach to integrate learning styles into adaptive e-learning hypermedia. The main objective was to develop a new Adaptive Educational Hypermedia System based on Honey and Mumford learning style model (AEHS-H&M) and assess the effect of adapting educational materials individualized to the student’s learning style. To achieve the main objectives, a case study was developed. An experiment between two groups of students was conducted to evaluate the impact on learning achievement. Inferential statistics were applied to make inferences from the sample data to more general conditions was designed to evaluate the new approach of matching learning materials with learning styles and their influence on student’s learning achievement. The findings support the use of learning styles as guideline for adaptation into the adaptive e-learning hypermedia systems;Item A Novel Incremental Learning Algorithm Based on Incremental Vector Support Machina and Incremental Neural Network Learn++(Lavoisier, 2019) Aida Chefrour; Labiba Souici-Meslati; Iness Difi; Nesrine BakkoucheIncremental learning refers to the learning of new information iteratively without having to fully retain the classifier. However, a single classifier cannot realize incremental learning if the classification problem is too complex and scalable. To solve the problem, this paper combines the incremental support vector machine (ISVM) and the incremental neural network Learn++ into a novel incremental learning algorithm called the ISVM-Learn++. The two incremental classifiers were merged by parallel combination and weighted sum combination. The proposed algorithm was tested on three datasets, namely, three databases Ionosphere, Haberman's Survival, and Blood Transfusion Service Center. The results show that the ISVM Learn ++ achieved a learning rate of 98 %, better than that of traditional incremental learning algorithms. The research findings shed new light on incremental supervised machine learning.Item AMF-IDBSCAN: Incremental Density Based Clustering Algorithm using Adaptive Median Filtering Technique(Slovene Society Informatika, 2019) Aida Chefrour; Labiba Souici-MeslatiDensity-based spatial clustering of applications with noise (DBSCAN) is a fundamental algorithm for density-based clustering. It can discover clusters of arbitrary shapes and sizes from a large amount of data, which contains noise and outliers. However, it fails to treat large datasets, outperform when new objects are inserted into the existing database, remove noise points or outliers totally and handle the local density variation that exists within the cluster. So, a good clustering method should allow a significant density modification within the cluster and should learn dynamics and large databases. In this paper, an enhancement of the DBSCAN algorithm is proposed based on incremental clustering called AMF-IDBSCAN which builds incrementally the clusters of different shapes and sizes in large datasets and eliminates the presence of noise and outliers. The proposed AMF-IDBSCAN algorithm uses a canopy clustering algorithm for pre-clustering the data sets to decrease the volume of data, applies an incremental DBSCAN for clustering the data points and Adaptive Median Filtering (AMF) technique for post-clustering to reduce the number of outliers by replacing noises by chosen medians. Experiments with AMF-IDBSCAN are performed on the University of California Irvine (UCI) repository UCI data sets. The results show that our algorithm performs better than DBSCAN, IDBSCAN, and DMDBSCAN.Item Incremental supervised learning: algorithms and applications in pattern recognition(SPRINGER, 2019) Aida ChefrourThe most effective well-known methods in the context of static machine learning offer no alternative to evolution and dynamic adaptation to integrate new data or to restructure problems already partially learned. In this area, the incremental learning represents an interesting alternative and constitutes an open research field, becoming one of the major concerns of the machine learning and classification community. In this paper, we study incremental supervised learning techniques and their applications, especially in the field of pattern recognition. This article presents an overview of the main concepts and supervised algorithms of incremental learning, including a synthesis of research studies done in this field and focusing on neural networks, decision trees and support vector machines.Item Forecasting approach in VANET based on vehicle kinematics for road safety(Inderscience, 2014-10-30) BEKTACHE Djamel; TOLBA Cherif; GHOUALMI Nacera ZineThis paper deals with the forecasting of collision events for road safety. Using significant parameters of each vehicle, such as position, speed and direction, it is possible to contribute to improving the road safety. We present a collaborative forecasting module in intersection scenario for collision avoidance. The proposed module is focused on the estimation of these parameters using a kinematic model of each vehicle to generate the trajectories estimation. The first simulation results show and assess that the vehicle trajectories estimated with the suggested kinematic modelling are realistic in all critical cases. The main goal of the suggested forecasting approach is to detect and avoid collision. On the basis of these trajectories estimation, the future occurrence of the collision event can be calculated, an alert must be generated and this will trigger the forecasting module in order to avoid collision. In addition, the second part of the simulation proves that the proposed forecast scenario is excellent for collision avoidance.