ABDELBASSET Barkat

بوهادف محمد , مناد عومار

Mobile Application for parental control of pupils

Mechri Hichem , Rahmouni Samir

Conception et réalisation d’une application de gestion d'une bibliothèque

Salah benchibout , ilyas hamoudi

Mobile Application for Online Doctor’s Appointment Booking

Laaraf Ahmed , Lateli thamir

Developing ab online strategy game powred by an intelligent algorithm

Hamza Sara

Job scheduling in cloud computing environment

3. Zoulikha TITRAOUI

L'optimisation multi objectifs pour l’ordonnancement de tâches dans le cloud computing

BENLATRECHE Abdelouahab

Modélisation de feux de forêts par automates cellulaires

Zaiter Hicham

Detection de visage dans une séquence vidéo par la méthode AdaBoost

baya baali

Un système intelligent pour la traduction automatique du texte

In recent years, cloud computing has been widely adopted over the Internet due to the numerous advantages and services it offers to users. The resources in cloud computing are based on virtualization, which makes them dynamic and prone to frequent changes. These resources are utilized by cloud services or user applications in order to respond to user requests. The smallest unit of a user application, known as a job, requires exclusive access to certain resources which would enable its execution. Assigning jobs to cloud nodes (a set of computational resources) is a NP-complete problem, commonly referred to as job scheduling in the cloud. The aim of this paper is to propose an integer programming model for cloud job scheduling and to find an optimal or near-optimal solution by using two algorithms, namely a genetic algorithm called GAJSC, and a particle swarm optimization (PSO) algorithm. This study concludes with a comparison of the performance of these two approaches against certain traditional baseline algorithms, including the First-Come-First-Serve (FCFS) and Shortest Job First (SJF) algorithms in terms of the obtained makespan and their scalability.

Citation

Abdelbasset Barkat , , (2025-09-30), A Comparative Study of Job Scheduling in Cloud Computing, STUDIES IN INFORMATICS AND CONTROL, Vol:34, Issue:3, pages:81-90, The National Institute for R&D in Informatics – ICI Bucharest

This study introduces a novel hybrid predictive model that integrates eXtreme Gradient Boosting (XGBoost) with Long Short-Term Memory (LSTM) networks, specifically designed for real-time urban traffic congestion prediction. The proposed model innovatively incorporates external data, such as weather conditions, traffic incidents, and road classifications, and effectively addresses the common issue of class imbalance in the traffic dataset and captures dynamic spatiotemporal traffic relationships. This comprehensive approach enables the capture of complex, dynamic spatiotemporal traffic relationships more accurately. XGBoost performs robust feature selection and preliminary classification, generating probabilistic traffic jam level estimates. These outputs are subsequently enhanced through bidirectional LSTM layers that leverage temporal dependencies within traffic data, thus significantly improving predictive accuracy. The hybrid XGBoost–LSTM model was evaluated using approximately three million real-time traffic records from central London, providing a substantial and realistic testing environment. The results demonstrated its superior performance, achieving an accuracy of 93%, with precision values between 86% and 96%, and recall between 84% and 97% across varying congestion scenarios, from free flow to heavy congestion. Notably, the inclusion of probabilistic feature augmentation successfully mitigated the impact of class imbalance, further enhancing reliability. Comparative analyses against traditional and standalone methods highlighted the proposed hybrid model’s substantial improvement in accurately differentiating traffic jam levels, making it a valuable tool for intelligent transportation systems (ITS). This research contributes significantly to urban traffic management strategies, supporting smoother traffic flow and congestion reduction.

Citation

Messaoud BECHERE , Abdelbasset Barkat , ghenabzia ahmed , Derya Yiltas-Kaplan, , (2025-05-22), Urban Traffic Prediction Using Hybrid XGBoost–LSTM Model, International Journal of Computing and Digital Systems, Vol:18, Issue:1, pages:1-15, scopus

As data becomes a crucial resource for powering various real-world applications, the field of data mining encounters numerous challenges, particularly regarding storage and real-time processing. Mining association rules to uncover relationships and patterns in large datasets is a crucial technique. However, the inherent uncertainty and incompleteness of data pose significant difficulties for traditional mining algorithms. To tackle these challenges, a novel method is proposed for mining sequential rules from uncertain sequence databases (SDs). This method involves two primary steps: first, extracting a set of probabilistic rules, and second, filtering these rules based on the sequential information within the data. This approach effectively addresses data uncertainty and incompleteness, enabling the extraction of meaningful sequential rules that are otherwise difficult to identify using conventional methods. This innovative method enhances the capability of mining algorithms to handle uncertain data, offering a robust solution for real-time data processing as well as storage issues in various applications. Experimental results demonstrate the algorithm's efficiency and scalability on both synthetic and real-world datasets. The proposed method achieved superior runtime and memory efficiency as dataset sizes increase.

Citation

Abdelbasset Barkat , , (2025-02-20), Efficient sequential rule mining in uncertain sequence databases, International Journal of Advanced Technology and Engineering Exploration, Vol:12, Issue:123, pages:237-253, IJATEE

Congestion in smart cities is a challenging issue due to its impact on people’s lives. Therefore, many researchers are developing solutions to this problem by taking advantage of new technologies, especially sensors and wireless network infrastructures. These technologies enable the collection of real-time information about many features directly or indirectly linked to traffic congestion. In this paper, we propose a model for traffic congestion prediction based on real-time data, considering a set of relevant features such as traffic flow, incidents, holidays, and weather data. Our prediction classifies data into four categories: free flow, mild congestion, moderate congestion, and heavy congestion. After analyzing the results, the algorithm with the highest performance is XGBoost, achieving a prediction accuracy of 90%, followed by random forest and decision tree, both with a prediction accuracy of 89%

Citation

Abdelbasset Barkat , ,(2024-12-11), Real-Time Data Integration for Effective Congestion Forecasting,The Sixth International Symposium on Informatics and Its Applications (ISIA),M'sila

omputers now handle large amounts of data, leading to the emergence of data mining as a science to extract useful information from this data. Frequent itemset mining is a popular technique used to discover relationships between items in big data. However, in real-life scenarios, data may be incomplete or uncertain, posing a challenge for frequent itemset mining. This paper proposes a novel approach for mining sequential frequent itemsets from uncertain sequence databases. The approach comprises two main parts: extracting a set of probabilistic frequent itemsets, and filtering this set using the sequential information in the data. At the end of the paper, we provide a comparison with an existing method to further demonstrate the value of our approach.

Citation

Abdelbasset Barkat , , (2023-05-23), Extracting sequential frequent itemsets from probabilistic sequences database, International Journal of Information Technology, Vol:15, Issue:5, pages:2329 - 2332, springer

Recently, the amount of data used by computers has become larger, and this is what led to the emergence of data mining as a science to make these data useful. Mining frequent itemsets is one of the strategies used to discover how the items are related to each other in big data, but unfortunately, these data are not always certain; in fact, and many cases in real life the data are incomplete or uncertain. In this paper, we propose a new approach for mining sequential frequent itemsets from uncertain sequence databases. Where we can divide this approach into two main parts, the first one is extracting the set of probabilistic frequent itemsets, and the second one is filtering this set using the sequential information in the data

Citation

Abdelbasset Barkat , ,(2022), Extracting sequential frequent itemsets from probabilistic sequences database,International Symposium on Informatics and its Applications 2022 (ISIA'22),M'sila

Recently, the term cloud computing is widely used in the searching community, which shows the importance given by the scientists to this research area; cloud computing is a new computing model provides shared resources and data based on service delivery model, where everything from infrastructures, platforms, and software are given to the user like a set of services. The users of cloud platforms deal with these services to satisfy their requests, however these requests become more complex, and they need more than one service to accomplish one request; the process of gathering a set of services to satisfy a user request is called the service composition. In addition to the fact that one request needs a set of services to be executed, and die to the quick development of cloud technology, there are many of similar services which offer the same functionality, for each service in this set, which make the composition process needs a mechanism to choose between these infinity choices to give the user an optimal satisfaction. In this paper, We propose a framework for service composition in the multi cloud environment where we compose these services based on two factors: the first is a set of QoS (quality of service) criteria for each service, and the second is the number of cloud bases involved in the composition process, to build a composed service that satisfy the user request.

Citation

Abdelbasset Barkat , Kazar Okba, Imane SEDDIKI, , (2021), Framework for web service composition based on QoS in the multi cloud environment, International Journal of Information Technology, Vol:13, Issue:2, pages:459–467, springer

In this study, we proposed a resource allocation approach that aims at fulfilling two main objectives. First, it equilibrates between the different cloud infrastructure particularities including load balancing, so it enhances the performance of infrastructure. Second, our approach provides a solution for the customer needs through shortening the execution time and reducing payments of the requested resources that have a dynamic nature. This paper suggests a new hybrid resources allocation approach based on three methods: multiagent system (MAS), distributed constraints satisfaction problems (DCSP), and the fuzzy logic (FL). The MAS represents the physical infrastructure. It provides an efficient management of the resources in the distribution and the heterogeneity of this infrastructure. DCSP, on the other hand, works side by side with MAS to maintain resources allocation policies in datacenters, while the FL is used to facilitate the representation of the dynamic resource values into linguistic terms (low, medium, high  …) and helps the system to determine the best solution according to the criteria of customer requests. The experimental results show the efficiency of our approach in terms of load balancing, energy consumption cost, execution time, and the rate payment gain of customers.

Citation

Abdelbasset Barkat , , (2019), Smart and fuzzy approach based on CSP for cloud resources allocation, International Journal of Computers and Applications, Vol:0, Issue:0, pages:1-13, Taylor & Francis

In this paper, we propose a security model based mobile agent for MANETs, the objective here is to improve the security level of the network communication without affecting its performance. The model includes a hierarchical clustering and mobile agents. We apply the concept of dominating set based clustering for partitioning the network into clusters. The cluster head election is based on both the trust and resources ability of the node. We define four agent types. The node agent manages the use of node resources. The ambassador agent is created by the control agent to monitor all the actions of the node agent. The control agent is created in the most trusted with best resources node to control the communication into the cluster and participates with its counterparts in the security network completely, and the transporter agent carries the encrypted information in the network.

Citation

Abdelbasset Barkat , , (2019), Security model based mobile agent for mobile ad hoc networks, International Journal of Communication Networks and Distributed Systems, Vol:22, Issue:1, pages:36-54, Inderscience Publishers (IEL)

In this paper, we propose a security model based mobile agent for MANETs, the objective here is to improve the security level of the network communication without affecting its performance. The model includes a hierarchical clustering and mobile agents. We apply the concept of dominating set based clustering for partitioning the network into clusters. The cluster head election is based on both the trust and resources ability of the node. We define four agent types. The node agent manages the use of node resources. The ambassador agent is created by the control agent to monitor all the actions of the node agent. The control agent is created in the most trusted with best resources node to control the communication into the cluster and participates with its counterparts in the security network completely, and the transporter agent carries the encrypted information in the network.

Citation

Abdelbasset Barkat , , (2019), Security model based mobile agent for mobile ad hoc networks, International Journal of Communication Networks and Distributed Systems, Vol:22, Issue:1, pages:36-54, Inderscience Publishers (IEL)

Purpose User requests over the cloud are not achievable with one single service, multiple services need to be executed to fulfill what a user asks for. Typically, such services are composed and presented as one global service. Moreover, the same operation can be achieved by multiple services available at different clouds, which can result in different possibilities in composing them. This paper aims to decrease the number of clouds involved in the composition process, so that user requests are satisfied with minimal cost (communication costs, execution time and financial charges). Design/methodology/approach This paper investigates the use of an intelligent water drops (IWDs) optimization-based algorithm, and an integer linear programming model to optimize the number of cloud bases involved in the composition process. A comparison of the solutions found by these two techniques is presented in the paper. Findings The obtained results show that the number of cloud bases can be decreased without affecting user satisfaction. Originality/value The paper is a first attempt to use the IWDs algorithm for service composition, tested with big-size data

Citation

Abdelbasset Barkat , , (2018), Service composition in the multi cloud environment, International Journal of Web Information Systems, Vol:13, Issue:4, pages:471-484., Emerald Publishing Limited

An ontology is an explicit specification of a conceptualization, the term is often linked with the Semantic Web, ontologies are used like representations of knowledge, to annotate web resources and also to communication between systems. This make them very important, but unfortunately their construction is expensive, and because they are representations of knowledge, we thought of using the enormous amount of information available under textual format to automate the process of ontology building, and since we deal with texts, the NLP (Natural Language Processing) is considered as the base for the ontology construction from text. In this paper our goal is to propose an agent-based approach to build ontology from text, and implement a multi-agent system guided by this approach, which start from a set of textual resources to give us an ontology in OWL (Ontology Web Language), using the Formal Concept Analysis FCA and Relational Concept Analysis RCA to move from the syntactic level to the semantic level.

Citation

Abdelbasset Barkat , ,(2013), Agent-based approach for building ontology from text,International Conference on Computer Applications Technology (ICCAT),Sousse