Department of Computer Science

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Application et modélisation d'un protocole de communication pour la sécurité routière
(University Badi Mokhtar Annaba, 2014-10-25) BEKTACHE DJAMEL Chérif Tolba , Ghoualmi Nacera
Forecasting approach in VANET based on vehicle kinematics for road safety
(Inderscience, 2014-10-30) BEKTACHE Djamel; TOLBA Cherif; GHOUALMI Nacera Zine
This 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.
An Adaptive E-Learning System based on Student’s Learning Styles: An Empirical Stud
(IGI GLOBAL, 2016) Samia Drissi; Abdelkrim Amirat
Personalized 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;
Adaptation with Four Dimensional Personalization Criteria Based on Felder Silverman Model
(IGI GLOBAL, 2017) Samia Drissi; Abdelkrim Amirat
In 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 decreased
AMF-IDBSCAN: Incremental Density Based Clustering Algorithm using Adaptive Median Filtering Technique
(Slovene Society Informatika, 2019) Aida Chefrour; Labiba Souici-Meslati
Density-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.
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 Bakkouche
Incremental 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.
Incremental supervised learning: algorithms and applications in pattern recognition
(SPRINGER, 2019) Aida Chefrour
The 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.
3D Mobility, Resizing and Mobile Sink Nodes in Reconfigurable Wireless Sensor Networks based on Multi-agent Architecture under Energy Harvesting Constraints
(SCITEPRESS, 2020-01) Hanene Rouainia; Hanen Grichi; Laid Kahloul; Mohamed Khalgui
This paper deals with reconfigurable wireless sensor networks (RWSNs) to be composed of a set of sensor nodes, which monitor the physical and chemical conditions of the environment. RWSNs adapt dynamically their behaviors to their environment. The main challenge in RWSN is to keep the network alive as long as possible. We apply a set of solutions for energy problems by using 3D mobility, resizing and mobile sink nodes. These solutions are based on a multi-agent architecture employing a wireless communication protocol. Moreover, we develop an application named RWSNSim that allows us to simulate an RWSN and apply the proposed solutions. The performance of the proposed approach is demonstrated through a case study. The case study consists of surveying of fire in a forest which is simulated with RWSNSim application.
Dynamic array PIN: A novel approach to secure NFC electronic payment between ATM and smartphone
(Taylor & Francis, 2020-06-04) Samir Chabbi; Rachid Boudour; Fouzi Semchedine; Djalel Chefrour
Near Field Communication (NFC) technology has been used recently for electronic payment between an Automated Teller Machine (ATM) and a Smartphone. It is threatened by several attacks that can steal the user personal data like the password or the Personal Identification Number (PIN). In this paper, we present Dynamic Array PIN (DAP), a novel approach for user authentication on a Smartphone that uses NFC electronic payment with an ATM. Our analysis and experimentation prove that this technique protects against thirteen different attacks and is cost-effective in terms of required hardware, authentication time, computing power and storage space.
Points Fixes et Géométrie des Espaces de Banach
(2021-03-20) Sami Atailia
Forecasting approach for Blind Spot Collision Alert
(ACM Digital Library, 2021-03-22) Djamel BEKTACHE, Yakoubi Mohamed Amine and Ghoualmi Nacera
Every year many accidents involving all categories of road users. However, some accidents are related to the blind spot, due to the deficiency of visibility in this zone, also lack of information or negligence. In this work, we focused on the road safety applications. The main of our researches is to alert the risk of blind spot collision. For this we designed a new approach, namely FBSCA, Forecasting Blind Spot Collision Alert, the driver alerts his neighbors for a possible accident related to the blind spot. The most interesting scenario testing includes the case where the vehicles need to exchange information in real-time in order to avoid traffic collisions when their visibility is hindered by road obstacles. For the implementation and testing of our approach we used popular network simulation tools such MOVE, SUMO and Ns2, we provided an assessment of proposed solution in order to alert the blind spot collisions for all neighboring vehicles.
One-Way Delay Measurement From Traditional Networks to SDN: A Survey
(2021-07) Djalel Chefrour
We expose the state of the art in the topic of one-way delay measurement in both traditional and software-defined networks. A representative range of standard mechanisms and recent research works, including Open-Flow and Programming Protocol-independent Packet Processors (P4)-based schemes, are covered. We classify them, discuss their advantages and drawbacks, and compare them according to their application environment, accuracy, cost, and robustness. The discussion extends to the reuse of traditional schemes in software-defined networks and the benefits and limitations of the latter with respect to reducing the overhead of network wide measurements. We conclude with a summary of learned lessons and open challenges for future work.
Existence and Stability of Solitions for certain Fractional differential equations by the Fixed point technique
(2021-07-17) Moussa Haoues
ملخص في هذه الأطروحة ، نحن مهتمون بمناقشة التحليل النوعي لبعض أنواع المعادلات التفاضلية الكسرية غير الخطية مع أو بدون تأخير. بعد مقدمة قصيرة وبعض التمهيدات عن التكاملات الكسرية، و المشتقات الكسرية، و نظريات النقطة الثابتة والمعادلات التفاضلية الكسرية ذات تأخير، الخ. نستخدم تقنية النقطة الثابتة لإثبات وجود و وحدانية الحلول لفئة من المعادلات التفاضلية الكسرية غير الخطية مع أو بدون تأخير. كما نستخدم طريقة التقريبات المتتالية لإظهار استقرار أولام. أخيرًا ، نقم بدراسة وجود و وحدانية و رتابة الحلول الموجبة لفئة من المعادلات التكاملية التفاضلية الكسرية الهجينة. تم توضيح جميع النتائج التي تم الحصول عليها في هذه الأطروحة من خلال بعض الأمثلة. ------------------------------ Abstract In this thesis, we are interested in the discussion of the qualitative analysis of some kinds of nonlinear fractional differential equations with or without delay. After a short introduction and some preliminaries on fractional integrals, fractional derivatives, fixed point theorems and fractional delay differential equations, etc. We use the fixed point technique to prove the existence and uniqueness of solutions for a class of nonlinear fractional differential equations with or without delay. We also use the method of successive approximations to show the Ulam stability. Finally, we study the existence, uniqueness and monotonicity of positive solutions for a class of hybrid fractional integro-differential equations. All results obtained in this thesis are illustrated by some examples. ------------------------------------------- Résumé Dans cette thèse, nous sommes intéressons a la discussion de l’analyse qualitative de quelques types d’équations différentielles fractionnaires non linéaires avec ou sans retard. Après une brève introduction et quelques préliminaires sur les intégrales fractionnaires, les dérivées fractionnaires, les théorèmes de point fixe et les équations différentielles fractionnaires à retard, etc. Nous utilisons la technique du point fixe pour prouver l’existence et l’unicité des solutions pour une classe d'équations différentielles fractionnaires non linéaires avec ou sans retard. Nous utilisons également la méthode des approximations successives pour montrer la stabilité d’Ulam. En fin, nous étudions l’existence, l’unicité et la monotonie des solutions positives pour une classe d’équations intégro-différentielles fractionnaires hybrides. Tous les résultats obtenus dans cette thèse sont illustres par quelques exemples.
An Optimized Path Planning for Wheeled Robot in Obstacle Environments
(IEEE explore, 2021-11-03) Mohamed Amine Yakoubi; Djamel Bektache; Abderahmane Gaham; Raouf Toumi
In this paper, we have proposed an obstacle avoidance algorithm for a path planning in unknown environment for a mobile robot based on the fuzzy logic control. Therefore, the wheeled mobile robot is equipped with 3 wheels, one steering wheel and two fixed wheels and mounted on the same axis. Its task is to move from a starting position to a target position. For this, our proposed algorithm creates one or more imaginary target and applies a fuzzy logic control system, which is adopted by a rule table that is induced from two inputs data (the distance and the angle between the robot and the target) and two outputs data (the angle orientation and velocity of the steer wheel). Experimental results show the effectiveness of the proposed algorithm.
A modified incremental density based clustering algorithm
(IEEE, 2022) Aida Chefrour
Cluster analysis, generally known as clustering, is a technique for separating data into groups (clusters) of similar objects. Except if the system is completely retrained, traditional clustering classifiers will be unable to learn new information and knowledge (attributes, examples, or classes). Only incremental learning, which outperforms when new data objects are introduced into an existing database, can solve this problem. These evolutionary strategies are applied to dynamic databases by updating the data. We’ll choose to study the Incremental Density- Based Spatial Clustering of Applications with Noise algorithm because of its capacity to discover arbitrary clusters and identify noise. In this study, a modified version of the Incremental Density Based Clustering Algorithm using an Adaptive Median Filtering Technique was used. The difference between our previous proposed AMF-IDBSCAN and the proposed algorithm developed in this work is in the evaluation performance stage. The key idea consists of a database change in the case of introducing new data items to an existing database in order to improve performance. We conducted several experiments on benchmark and synthetic data collected from the University of California Irvine repository in terms of the Generalized Dunn Index, Davies Bouldin Index, and change of time (milliseconds) with the increment of data in the original database. Experiments with datasets of various sizes and dimensions show that the proposed algorithm enhances clustering when compared to several current incremental wellknown techniques.
CAE-CNN: Image Classification Using Convolutional AutoEncoder Pre-Training
(2022) Aida Chefrour; Samia Drissi
The work presented in this paper is in the general framework of classification using deep learning and, more precisely, that of convolutional neural networks (CNN). In particular, the convolutional autoencoder proposes an alternative for the processing of high-dimensional data, to facilitate their classification. In this paper, we propose the incorporation of convolutional autoencoders as a general unsupervised learning data dimension reduction method for creating robust and compressed feature representations in order to improve CNN performance on image classification tasks. For prediction reasons, we applied the two methods to the MNIST image databases. The use of CNN with the convolutional autoencoder gives better results compared to the individual use of each of them in terms of accuracy, to obtain a good classification of the data high-dimensional entrance.
Unsupervised Deep Learning: Taxonomy and Algorithms
(Slovene Society Informatika, 2022) Aida Chefrour; Labiba Souici-Meslati
Clustering is a fundamental challenge in many data-driven application fields and machine learning techniques. The data distribution determines the quality of the outcomes, which has a significant impact on clustering performance. As a result, deep neural networks can be used to learn more accurate data representations for clustering. Many recent studies have focused on employing deep neural networks to develop a clustering-friendly representation, which has resulted in a significant improvement in clustering performance. We present a systematic survey of clustering with deep learning in this study. Then, a taxonomy of deep clustering is proposed, as well as some sample algorithms for our overview. Finally, we discuss some exciting future possibilities for clustering using deep learning and offer some remarks
New Energy Efficient and Fault Tolerant Methodology based on a Multi-agent Architecture in Reconfigurable Wireless Sensor Networks
(SCITEPRESS, 2022-01) Hanene Rouainia; Hanen Grichi; Laid Kahloul; Mohamed Khalgui
Reconfigurable wireless sensor networks became more complex and dynamic systems. Their importance increases with time and more challenges appear. The most important challenges in RWSNs are the energy and software/hardware failure problems. In this paper, we propose a new methodology composed of a set of solutions summarized in the application of the mobility, resizing, and mobile sink nodes using a multi-agent architecture and an energy efficient routing protocol. It contains also a test packet technique to detect the malfunctioning entities and isolate them. Moreover, we develop a simulator named RWSNSim which allows simulating WSNs and RWSNs with and without application of the proposed methodology. It permits also to compare the different results using line charts. Finally, we simulate a case study with RWSNSim in a 3D environment to evaluate the performance of the proposed methodology.
Science Education in Algeria
(2022-01-19) Hafed ZARZOUR
Throughout history, science education has played a vital role in developing and modernizing the countries. The education in Algeria has been developing for the last years as a result of several reforms undertaken for enhancing the quality of learning and teaching in the whole education system, ranging from the primary school to higher education. Hence, this book chapter attempts to present the science education in Algeria. It starts by providing some information about the geographical location, population, and political system, as well as outlining the economic, technologies, and cultural development in the country. It then presents an overview of the education development and the current situation of science education in Algeria. The present chapter further explores the requirements for future development of science education. Finally, challenges and strategies, reflections and issues, and future pathways are discussed in the hope of improving the leaning and teaching for omorrow’s world.
Science Education in Algeria
(2022-01-19) Hafed ZARZOUR