Journal Articles

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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.
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.
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;
An Intersection Attack on the CirclePIN Smartwatch Authentication Mechanism
(IEEE Internet of Things Journal, 2024-04-01) Djalel Chefrour; Yasser Sedira; Samir Chabbi
We present a thorough security analysis of a recent smartwatch authentication mechanism called CirclePIN, which was considered resilient to several attacks, including shoulder surfing and video recording. This mechanism avoids the direct entry of the personal identification number (PIN) by using consecutive screens of random colors that fool the attacker. We disclose a vulnerability in CirclePIN inherent to the way in which the users match the random colors to their PINs’ digits and we illustrate how to exploit it with an intersection attack. This attack uses the information extracted from multiple video recordings of legitimate authentication sessions. We prove that it has a high probability of revealing the user PIN with only three video recordings and always succeeds with five. Our proof is twofold. We formulate the theoretical probability of success for the attack as a function of the number of available video recordings. Then, we validate this formula with a simulation of a large number of attacks to compute their experimental probability of success. In our estimation, manual information extraction takes around 1 min per exploitable video recording. So, a complete intersection attack is cost effective in terms of time, as it lasts 5 min or less.
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.
Evolution of network time synchronization towards nanoseconds accuracy: A survey
(Elsevier, 2022-07) Djalel Chefrour
We expose the state of the art in the topic of network time synchronization. Many distributed applications require a common notion of time to function properly. Without time synchronization, the nodes clocks will drift and report different values for the same instant. This problem is exacerbated by varying network delays between the cooperating nodes. Our survey covers how this issue is tackled by standard time synchronization mechanisms and a representative range of recent research works. We expose how some of them achieve micro and nanoseconds accuracy in wired networks. The reviewed techniques are classified in two categories based on whether they change the hosts clocks or not. The latter category includes schemes that detect and remove clock skew from network traffic trace. We discuss the advantages and drawbacks of the techniques in each category; compare them according to their application environment, accuracy and cost; and conclude this survey with a summary of learned lessons and insights into future work.
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.
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.
K-CAE: Image Classification Using Convolutional AutoEncoder Pre Training and K-means Clustering
(Slovene Society Informatika, 2023) 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 Autoencoder. In particular, this last 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 for better storage and transmission to the classification process to improve K-means performance on image classification tasks. The experimental results on three image databases, MNIST, Fashion-MNIST, and CIFAR-10, show that the proposed method significantly outperforms deep clustering models in terms of clustering quality.
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.
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.
Unsupervised Deep Learning: Taxonomy and Algorithms
(2022-12-14) Aida CHEFROUR and SOUICI-MESLATI Labiba
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
Using Deep Learning for Positive Reviews Prediction in Explainable Recommendation Systems
(2022-10-26) Hafed ZARZOUR, Mohammad Alsmirat and Yaser Jararweh
Using K-means Clustering Ensemble to Improve the Performance in Recommender Systems
(2022-10-26) Hafed ZARZOUR; Faiz Maazouzi; Mohammad Al-Zinati; Amjad Nusayr; Mohammad Alsmirat; Mahmoud Al-Ayyoub; Yaser Jararweh
Vulnerability of the Dynamic Array PIN Protocol
(Ingénierie des Systèmes d’Information, 2022-02-28) Samir Chabbi; Djalel Chefrour
We recently proposed the Dynamic Array PIN protocol (DAP), which is a novel approach for user authentication on Automated Teller Machines. DAP replaces bank cards with smartphones that support Near Field Communication (NFC) and allows a user to enter his PIN code in a secure way. We showed that DAP is resistant to 13 different attacks and is therefore better and more cost effective than several other solutions from the literature. However, after carrying a deeper analysis we found that DAP is vulnerable to a complex attack that might lead to unauthorized transactions on ATMs if the user smartphone and his PIN code are both stolen. In this paper we expose how the user PIN code can be discretely discovered using multiple eavesdropping videos or camera records. We also propose several fixes for this vulnerability.