ALI Dabba

Gana, khadidja , Djaalab, lakhdar, Mekkaoui, Mohammed

Conception et Réalisation d'un Site Web de Gestion des Sujets de Graduation

عيسات، أيمن , براق، يونس, كروش، إسحاق

تصميم و إنشاء تطبيق لإدارة حجزمو اقف السيارات

كرميش إسلام , بن قويدر محمود, غفص ي حسام

تصميم و إنشاء تطبيق جوال لتوصيل الوجبات

DJELLOUL, Ikrame , AOUFI, Linda, BOUSSEDRAIA, Chaima Zouhra

Conception et Réalisation d'une Application Web de Vente en Ligne

مختاري شيماء , تركي كريمة, زروتي وصال ام الخير

تصميم و إنشاء تطبيق لتسيير فندق

Messaoudi Imane , Midouna Soulef

Conception et réalisation d’une application de gestion des stocks des produits pharmaceutiques.

LAMRI Soufyane , HAMICHE Mohamed taki eddine, MDJAHED Abderraouf

Conception et réalisation d’une application de gestion des stocks pour les pièces détachées

Kadi Badreddine , Ben merzoug Widad

Techniques d'analyse AI pour la preduction de COVID 19

NASRI Nesrine , KHODOUR Aya nor elhoda, KOURIBAA Rawnak

Conception et réalisation d'une application Mobile pour la gestion d'un restaurant moderne

Saadaoui Amdjed , Djaalab Ala eddin, Babi chaima

Conception et réalisation d'une application smartphone pour prendre un Rendez-vous à l'hôpital

DJEMIAT Djalal , OUARADJ Yahia

APPLICATION WEB POUR LA GESTION D'UNE ÉCOLE CORANIQUE

Gahgah Hadjer

Problème d’emploi de temps : proposition un algorithme bio inspiré

Touina Hanane

Classification automatique de textes

BOUKHARI Nawel

Auto-localisation dans les réseaux de capteurs sans fil

The increasing recurrence and complexity of DDoS attacks makes adaptability, scalability, and responsiveness in real time very difficult for conventional detection techniques. This study explores a biology-inspired detection approach that uses concepts rooted in natural systems like swarm intelligence, neural dynamics, and the human immune system. These systems are highly capable of self-organization and dynamic adaptation, allowing for fast anomaly detection, which is fundamental to cybersecurity today. This research attempts to develop robust systems that can effectively respond to sophisticated and evolving cyber threats by emulating adaptive traits. In this study, we introduce an Intrusion Detection System (IDS) using a Multi-objective Slap Swarm Algorithm (SSA) such that the performance of the system is optimized according to various criteria. The system is designed to adapt to the specific requirements of the monitored environment and effectively differentiate between normal and malicious traffic. Therefore, we utilize Multi-objective SSA method to choose the most important features of the original IDS datasets for the purpose of low-rate DDoS attacks detection. The proposed algorithm is implemented as the classification algorithm of the new IDS and learned and verified by the NSL-KDD data set. These selected features are then employed by more than one classifier Support Vector Machine (SVM), Decision Tree (DT), Naïve Bayes (NB), and Multilayer Perceptron (MLP) for efficient classification of type of attack. Finally, the proposed system is designed to efficiently recognize and categorize DDoS attack signatures.

Citation

Ali Dabba , ,(2025-01-06), Intelligent Intrusion Detection with Multi-objective Slap Swarm Optimization: A Bio-Inspired Method for DDoS Classification,The 16th International Conference on Multiple Objective Programming and Goal Programming,Varese, Italy

Wireless Sensor Networks (WSNs) are important technology for monitoring and gathering data in many applications such as military, environmental monitoring and smart cities. An important issue in WSNs is the efficient deployment of sensor nodes for achieving the network coverage and connectivity with minimum energy dissipation. These traditional methods are inadequate for dynamic or complicated terrains. The deployment of sensor nodes in Wireless Sensor Networks (WSNs) is an important factor that determines the coverage for the network, connectivity of sensor nodes, energy conservation, and operational lifecycle of the WSN. Conventional deployment techniques commonly have difficulty with issues of size and scale, flexibility, and being adaptable to optimize dynamic or large-scale deployment environments. Bioinspired algorithms such as genetic evolution, swarm intelligence, and ecological behaviors, offer robust solutions to these challenges. In this paper, we proposed a multi-objective sensor deployment strategy using the Moth-Flame Optimization (MFO) algorithm, a metaheuristic inspired by moth's action and behavior. The algorithms proposed could be defined as Mult-Objective MFO (MO-MFO) that were used to optimize individual metrics of sensor deployment that covers area, connectivity, and energy efficiency all at once. We constructed trade-offs between the multiple objectives and generated a Pareto-optimal deployment set of deployment solutions. According to simulation results, the MO-MFO algorithm is able to achieve more balanced and robust sensors than conventional and single-objective methods. It is clear to see that the MO-MFO algorithm uses advantageous features such as adaptability of the algorithm, convergence performance, and capability for use in an actual WSN environment with a variety of complexities and heterogeneous components.

Citation

Ali Dabba , ,(2025-01-06), Optimizing Wireless Sensor Network Deployment Using Multi-Objective Bioinspired Algorithms,The 16th International Conference on Multiple Objective Programming and Goal Programming,Varese, Italy

Cancer classification based on microarray data plays a very important role in cancer diagnosis and detection. Indeed, since microarray data contains a huge number of genes and a small number of samples, it is also nonlinear and noisy, which has led to the need to find a way to reduce the data dimensionality. In order to solve this problem, we need to find an effective way to help biologists and medical research scientists. This paper proposes a new bio-inspired algorithm for cancer classification in gene selection called Binary Grey Wolf Optimization Algorithm (BGWOA), which is based on hybridization between Minimum Redundancy-Maximum Relevance (MRMR) and a novel Binary Grey Wolf algorithm. The BGWOA is composed of two stages: The first stage consists of the MRMR pre-filter to obtain the set of relevant genes that reduces the dimensionality of the data sets. The second stage consists of a new Binary Grey Wolf algorithm based on direct similarity and centroid known in the geometric field to update the positions of grey wolves in order to exploit and explore the search spaces. As well, we used a fitness function that depends on the SVM with LOOCV classifier and the rate of unselected genes to evaluate the presented solutions. The primary goal of the last stage is to identify the best relevant subset of genes among those obtained in the first stage. This research used eight microarray datasets to evaluate and compare the proposed method with other existing algorithms. The experimental results produced in this research are able to provide a higher classification accuracy with fewer genes compared to many recently published algorithms. Specifically, the proposed method achieves 100% classification accuracy in five reference datasets with a number of genes ranging from 12 to 25. Therefore, this indicates that our research is promising and significant.

Citation

Ali Dabba , , (2023-09-21), A novel grey wolf optimization algorithm based on geometric transformations for gene selection and cancer classification, The Journal of Supercomputing, Vol:40, Issue:4, pages:33, Springer

Abstract. Cancer classification is an important issue addressed in the Bioinformatics field. In this paper, we present a novel extension of the Moth Flame Optimization Algorithm combined with Mutual Information Maximization (MIM) to solve gene selection problem called Mutual Information Maximization-modified Moth Flame Optimization Algorithm (MIM-mMFOA). The MIM-mMFOA has two phases: the first one is used to solve the difficulty of high-dimensional data, which measures redundancy and relevance of the gene, in order to obtain the relevant gene set. The second phase is dedicated to finding a small gene subset that can be used to classify samples with high accuracy, using a Support Vector Machine (SVM) with Leave One Out Cross Validation (LOOCV) classifier. In order to evaluate the performance of the proposed MIM-mMFOA, we test it on seven Microarray datasets. Experimental results show that MIM-mMFOA achieves a high classification accuracy in comparison to some known algorithms.

Citation

Ali Dabba , ,(2022-01-24), A New Gene Selection Method Based On Moth Flame Optimization,Second International Conference on Artificial Intelligence and its Applications (AIAP'2022),Université de EL-Oued - Algérie

Creating an effective method for extracting disease information is one of the major challenges in the classification of gene expression data as long as there is (in the presence) a massive amount of redundant data and noise. Bio-inspired algorithms are among the most effective when used for solving gene selection. Several techniques or methods may help in detecting diseases and cancer. Moth Flame Optimization Algorithm (MFOA) is computationally less expensive and can converge faster than other methods.

Citation

Ali Dabba , Rabah Mokhtari , abdelkamel Tari, Samy Meftali, ,(2022), A New Gene Selection Method Based On Moth Flame Optimization,Second International Conference on artificial intelligence and its applications (AIAP'2022),EL OUED, Algeria

The knowledge of the protein structural class is one of the most important sources of information in many biological fields, such as function analysis, protein structure, drug design, and protein folding. However, the protein structural class prediction is still a challenge when dealing with low similarity sequences. Therefore, the accuracy of the top-performing prediction methods remains unsatisfying, especially for proteins from the + ß class. This paper proposes a novel approach for Protein Structural Class Prediction using a Convolutional Neural Network (PSCP-CNN). Our approach consists of two stages. The first is the preprocessing stage which allows the preparation of the data. The second stage is a CNN classifier that automatically extracts the needed features for the classification. To evaluate the performance of our approach, we performed the jackknife test on four low similarity benchmark datasets: 25PDB, 640, 1189, and FC699. The experimental results show that PSCP-CNN achieved high prediction accuracy, where the overall accuracy on datasets 25PDB, 640, 1189, and FC699 is 93.8%, 94.5%, 94.0%, and 98.0%, respectively. Furthermore, comparing the results obtained with existing methods shows that PSCP-CNN outperforms state-of-the-art techniques and confirms that using a convolutional neural network allows a better prediction of protein structural classes.

Citation

Ali Dabba , ,(2022), PSCP-CNN: Protein Structural Class Prediction using a Convolutional Neural Network,2022 5th International Symposium on Informatics and its Applications (ISIA),M'Sila

Ever-increasing data in various fields like Bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer. In this problem, computational complexity is reduced by selecting a small number of genes, but it is necessary to choose the relevant genes to keep a high level of accuracy. Therefore, in order to find the optimal gene subset, it is essential to devise an effective exploration approach that can investigate a large number of possible gene subsets. In addition, it is required to use a powerful evaluation method to evaluate the relevance of these gene subsets. In this paper, we present a novel swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization (MFO) algorithm. The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is a hybridization among MFOA, quantum computing, and support vector machine with leave-one-out cross-validation, etc., in order to solve the gene selection problem. We use quantum computing to guarantee a good trade-off between the exploration and the exploitation of the search space, while a new update moth operation using Hamming distance and Archimedes spiral allows an efficient exploration of all possible gene-subsets. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test QMFOA on thirteen microarray datasets (six binary-class and seven multi-class) to evaluate and compare the classification accuracy and the number of genes selected by the QMFOA against many recently published algorithms. Experimental results show that QMFOA provides greater classification accuracy and the ability to reduce the number of selected genes compared to the other algorithms.

Citation

Ali Dabba , , (2021), Hybridization of Moth flame optimization algorithm and quantum computing for gene selection in microarray data, Journal of Ambient Intelligence and Humanized Computing, Vol:12, Issue:1, pages:2731–2750, Springer Berlin Heidelberg

Cancer classification is one of the main applications of gene expression data (microarray data) and is essential for a comprehensive diagnosis of cancer treatment. Therefore, bio-inspired algorithms have developed several effective applications in the analysis of gene selection, which are one of the most effective applied in this domain. Harris Hawks optimization is a novel and recent algorithm that has an excellent balance between exploration and exploitation. This paper presents the first study on multi-objective binary Harris Hawks optimization (MOBHHO) for gene selection. We define gene selection as a problem, including two main conflicting objectives: minimizing the number of genes and maximizing the classification accuracy. MOBHHO uses two fitness functions to solve competing objectives. The first function based on SVM with LOOCV classifier and the second function also depends on KNN with K-fold classifier, as well as the percentage of gene selection found in both functions. Furthermore, MOBHHO tries to find the Pareto-optimal solutions, i.e. the best gene subset that contains a minimal number of selected genes and better classification accuracy. We have integrated several filter-based ranking methods with our proposal. In order to test the performance accuracy of the proposed MOBHHO algorithm, we compared our algorithm with other recently published algorithms in the literature. The experiment results which have been conducted on eight benchmarks (binary-class and multi-class), MOBHHO able to provide a minimum number of genes to obtain the highest classification accuracy. The proposed method reaches above 98% classification accuracy in six benchmark datasets and a maximum accuracy of 100% is achieved.

Citation

Ali Dabba , , (2021), A new multi-objective binary Harris Hawks optimization for gene selection in microarray data, Journal of Ambient Intelligence and Humanized Computing, Vol:1, Issue:1, pages:1, Springer

In this paper, we present a new swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization algorithm (MFOA). The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QM- FOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is hybridization among MFOA, quantum computing, and support vector machine (SVM) with leave-one-out cross-validation (LOOCV), in order to solve the gene selection problem. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test it on six Microarray datasets. Experimental results show that QMFOA provides great classification accuracy in comparison to some known algorithms.

Citation

Ali Dabba , , (2020), Gene Selection and Classification Using Quantum Moth Flame Optimization Algorithm, American Journal of Science and Engineering, Vol:1, Issue:2, pages:16-20, 1 April 2020

Several techniques or methods may help in detecting diseases and cancer. Creating an effective method for extracting disease information is one of the major challenges in the classification of gene expression data as long as there is (in the presence) a massive amount of redundant data and noise. Bio-inspired algorithms are among the most effective when used for solving gene selection. Moth Flame Optimization Algorithm (MFOA) is computationally less expensive and can converge faster than other methods. In this paper, we propose a new extension of the MFOA called the modified Moth Flame Algorithm (mMFA), the mMFA is combined with Mutual Information Maximization (MIM) to solve gene selection in microarray data classification. Our approach Called Mutual Information Maximization – modified Moth Flame Algorithm (MIM-mMFA), the MIM based pre-filtering technique is used to measure the relevance and the redundancy of the genes, and the mMFA is used to evolve gene subsets and evaluated by the fitness function, which uses a Support Vector Machine (SVM) with Leave One Out Cross Validation (LOOCV) classifier and the number of selected genes. In order to test the performance of the proposed MIM-mMFA algorithm, we compared the MIM-mMFA algorithm with other recently published algorithms in the literature. The experiment results which have been conducted on sixteen benchmark datasets either binary-class or multi-class, confirm that MIM-mMFA algorithm provides a greater classification accuracy.

Citation

Ali Dabba , , (2020), Gene Selection and Classification of Microarray Data Method Based on Mutual Information and Moth Flame Algorithm, Expert Systems with Applications, Vol:166, Issue:114012, pages:1, Elsevier

Multiple sequence alignment (MSA) represents a basic task for many bioinformatics applications. MSA allows finding common conserved regions among various sequences of proteins or DNA. However, to find the optimal multiple sequence alignment, it is necessary to design an efficient exploration approach that could explore a huge number of possible multiple sequence alignments. As well as, it is required to use a powerful evaluation method to assess the biological relevance of these multiple sequence alignment. To address these main problems, this article presents a multiobjective artificial fish swarm algorithm (MOAFS) to solve multiple sequence alignment. MOAFS uses the behaviors of artificial fish swarm algorithm such as the cooperation, decentralization and parallelism to ensure a good trade-off between the exploration and the exploitation of the search space of MSA problem. To preserve the quality and consistency of alignment, two fitness functions have been simultaneously used by the MOAFS algorithm: (i) Weighted Sum of Pairs to determine similar regions horizontally and (ii) Similarity function to determine vertically similar regions between the sequences of an alignment. Following the exploration of space search, the Pareto-optimal set is obtained by MOAFS which performs the optimal multiple sequence alignments for both fitness functions. The performance of MOAFS algorithm has been proved by comparing our algorithm with different progressive alignment methods, and other alignment methods based on evolutionary algorithms with single-objective and many-objective. The experiment results conducted on BAliBASE 2.0 and BAliBASE 3.0 benchmark confirm that the MOAFS algorithm provides a greater accuracy statistical significance in terms of SP or CS scores.

Citation

Ali Dabba , , (2019), Multiobjective artificial fish swarm algorithm for multiple sequence alignment, INFOR: Information Systems and Operational Research, Vol:1, Issue:4, pages:1-22, INFOR: Information Systems and Operational Research

Border coverage is acknowledged as important problem addressed in literature of wireless sensor networks (WSNs). In this paper, we suggest a solution to solve this problem called Border Coverage Protocol (BCP) that preserves both border and area coverage. This protocol runs in three phases: the first one is used to detect the nodes close to the border and that will be considered as boundaries nodes. The second phase is dedicated to find the transfer and internal nodes. The last phase focused on replacing border nodes by the transfer nodes, in case of any failure. Simulation results show that BCP achieves a high coverage ratio in comparison to some known protocols.

Citation

Ali Dabba , ,(2014), BCP: A Border Coverage Protocol for wireless sensor networks.,Science and Information Conference (SAI),London, UK

Dans les réseaux de capteurs sans fil (RCSF), un problème très important est abordé dans la littérature est celui de la couverture de frontières. Nous avons proposé un nouveau protocole de couverture de frontières pour les RCSFs, nommé BCP (Border Coverage Protocol) basé sur deux concepts, les ensembles dominants de cardinalité minimale et le polygone de voronoï. Ce protocole traite les trois points principaux : la détection des nœuds frontières, la détection et la sélection des nœuds internes et transfert, et la maintenance des nœuds frontières. Nous avons présenté un modèle analytique pour ce protocole, qui repose sur les chaînes de Markov. Le comportement de chaque nœud du réseau est modélisé par une chaîne de Markov.

Citation

Ali Dabba , ,(2014), Étude de la Consommation d’énergie du protocole BCP par les chaînes de Markov,2ème journée d’Informatique de l’université de Bordj Bou Arreridj,Université de Bordj Bou Arreridj

Dans les réseaux de capteurs sans fil (RCSF), un problème très important est abordé dans la littérature est celui de la couverture de frontières. Nous avons proposé un nouveau protocole de couverture de frontières pour les RCSFs, nommé BCP (Border Coverage Protocol) basé sur deux concepts, les ensembles dominants de cardinalité minimale et le polygone de voronoï. Ce protocole traite les trois points principaux : la détection des nœuds frontières, la détection et la sélection des nœuds internes et transfert, et la maintenance des nœuds frontières. Nous avons présenté un modèle analytique pour ce protocole, qui repose sur les chaînes de Markov. Le comportement de chaque nœud du réseau est modélisé par une chaîne de Markov.

Citation

Ali Dabba , ,(2014), Modélisation d’un nouveau protocole de couverture de frontière dans les RCSF par les chaînes de Markov,JEESI 14,l'École nationale supérieure d'informatique d'Alger