Journal Articles

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    Agent-Based Simulation of Crowd Evacuation Through Complex Spaces
    (Lavoisier, 2023) Mohamed Chatra; Mustapha Bourahla
    In this paper, we have developed a description of an agent-based model for simulating the evacuation of crowds from complex physical spaces for escaping dangerous situations. The model describes a physical space containing a set of differently shaped fences, and obstacles, and an exit door. The pedestrians comprising the crowd and moving in this space in order to be evacuated are described as intelligent agents with supervised machine learning using perception-based data to perceive a particular environment differently. The description of this model is developed with the Python language where its execution represents its simulation. Before the simulation, the model can be validated using an animation written with the same language to fix possible problems in the model description. A model performance evaluation is presented using an analysis of simulation results, showing that these results are very encouraging
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    Fast approach for link prediction in complex networks based on graph decomposition
    (2023) Abdelhamid Saif; Farid Nouioua; Samir Akhrouf
    Social networks such as Facebook, Twitter, etc. have dramatically increased in recent years. These databases are huge and their use is time consuming. In this work, we present an optimal calculation in graph mining for link prediction to reduce the runtime. For that purpose, we propose a novel approach that operates on the connected components of a network instead of the whole network. We show that thanks to this decomposition, the results of all link prediction algorithms using local and path-based similarity measure scan be achieved with much less amount of computations and hence within much shorter runtime. We show that this gain depends on the distribution of nodes in components and may be captured by the Gini and the variance measures. We propose a parallel architecture of the link prediction process based on the connected components decomposition. To validate this architecture, we have carried out an experimental study on a wide range of well-known datasets. The obtained results clearly confrm the efciency of exploiting the decomposition of the network into connected components in link prediction
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    A scalable time-division-based emergency messages broadcast scheme for connected and autonomous vehicles in urban environment
    (Elsevier Inc., 2022) Salah Guesmia; Fouzi Semchedine; Soufiene Djahel
    Connected and Autonomous Vehicles (CAVs) technology promises to revolutionize the transportation sector by solving many safety and traffic efficiency challenges while significantly enhancing passengers’ and drivers’ comfort. Most of these applications are built upon multi-hop broadcast of information among vehicles, leading to an increased contention and thus higher collision probability due to the limited bandwidth available for vehicular communications. In this paper, we propose a novel TimeDivision-based Broadcast (TDB) scheme to mitigate interferences originating from hidden nodes (i.e., vehicles) to meet the stringent safety applications requirements. Then, we use this broadcast scheme as a basis for designing a new efficient and scalable protocol for emergency messages dissemination in urban vehicular networks (UV-TDB: an Urban, Vehicular and Time-Division-based Broadcast protocol). The simulation experiments show that UV-TDB significantly improves the performance of multi-hop broadcast and outperforms two state of the art protocols in terms of the achieved reduction in broadcast overhead (up to 77.9%), packet collisions ratio (up to 93.5%), interference rate (up to 43.5%) and increase in message reception rate (up to 337.7%). The results highlight also that UV-TDB adapts better to varying levels of vehicle density, making it more scalable
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    Computers and Electrical Engineering
    (Université de M'sila, 2023) Boubakeur Moussaoui; Noureddine Chikouche; Hacène Fouchal
    In Cooperative Intelligent Transport Systems (C-ITS), vehicles and Road Side Units (RSU) exchange various messages about safety, traffic control, and weather conditions. These messages are broadcast to all neighbors. Each message should be authenticated and should protect users’ privacy, mainly by hiding their locations. The authentication is achieved by signing messages using a private key related to the Pseudonym Certificate (PC), provided by a Trusted Authority (TA). The PC is delivered together with messages so that receivers can authenticate the messages. Privacy is ensured by changing PCs many times during a journey, so that trackers cannot get drivers’ traces. This paper proposes a novel approach to manage PC switching periods between vehicles. The proposed method uses a Common PC (CPC) during a short period before switching to a new PC. Vehicles use the same shared PC during this period to sign their messages. Simulations have been conducted in an OMNET++ environment, showing significant improvement in privacy protection compared to well-known privacy schemes
  • ItemOpen Access
    محاضرات موجهة لطلبة السنة الأولى علوم إقتصادية
    (جامعة محمد بوضياف بالمسيلة, 2019-06-10) ديلمي, مصطفى
    بنية الفضاء الشعاعي، التطبيقات الخطية، المصفوفات والمحددات،جمل المعادلات الخطية، القيم الذاتية والأشعة الذاتية
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    A New Hybrid Image Segmentation Method Based on Fuzzy C-Mean and Modified Bat Algorithm
    (Université de M'sila, 2022) Souhil Larbi BOULANOUAR; Chaabane Lamiche2
    Magnetic resonance imaging (MRI) plays an important role in clinical diagnosis, because of that it has attracted increasing attention in recent years. The symptom of many diseases corresponds to the brain's structural variants. The detection of various diseases has became very useful through the segmentation methods. Fuzzy c-means (FCM) considers among the popular clustering algorithms for medical image segmentation. However, FCM is sensitive to the noise and falls into local optimal solution easily because of the random initialization of the cluster centers. In this research, we propose a hybrid method based on modified fuzzy bat algorithm (MFBA) and the FCM clustering algorithm named MFBAFCM. This developed approach uses the MFBA to get better initial cluster centers for the FCM algorithm by using a new fitness function, which combines intra cluster distance with fuzzy cluster validity indices. Experimental results on several MRI brain images corrupted by different levels of intensity non-uniformity and noise, show that the proposed method produced better results than the standard FCM and some other recent published works
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    ECG based biometric identification using one-dimensional local difference pattern
    (Université de M'sila, 2020) Mohamed Benouis; Lotfi Mostefai; Nicholas Costen; Meryem Regouid
    In this work, an enhanced version of 1D local binary pattern is proposed, for the derivation of the most relevant features for ECG-based human recognition. Generally, ECG signal characteristics by nature impose some notable challenges, mostly related to its sensitivity to noises, artifacts, behavioral and emotional disorders and other variability factors. To deal with this critical issue, we use a One-dimensional Local Difference Pattern (1D-LDP) operator to extract the discriminating statistical features from ECG by using the difference between consecutive neighboring samples to capture both the micro and macro patterns information in the heartbeat activity while reducing the local and global variation occurred in ECG over time. To verify its robustness, K-nearest neighbors (KNN) linear support vector machine (SVM) and neural network were performed as the classifier models in this work. Obtained results show that the 1D-LDP operator clearly outperforms existing 1D-LBP variants on MIT-BIH Normal Sinus Rhythm and ECG-ID database
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    Efficient Arabic Handwritten Character Recognition based on Machine Learning and Deep Learning Approaches
    (Université de M'sila, 2020) Said Gadri
    Arabic Handwritten character recognition is one of the most studied topics since many decades, there exists many difficulties which prevent to have significant advances in this important field such as: the variability of handwriting from a person to another, the large availability of databases, the complicated morphology of Arabic as a very rich Semitic language. In this paper, we proposed a deep learning model based on convolutional neural networks CNN which permits to achieve a high performance in Arabic handwritten characters recognition.
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    SCATTER: Fully Automated Classification System across Multiple Databases
    (Université de M'sila, 2019-07) Tahar, Mehenni
    Data mining approaches performed recently use data coming from a single table and are not adapted to multiple tables. Moreover, computer network expansion and data sources diversity require new data mining systems handling databases heterogeneity in multi-database systems. In this paper, we propose SCATTER: a fully automated classification system from multiple heterogeneous databases. SCATTER is composed of three components. The first component uses schema matching techniques to find foreign-key links across the multi-database system. The second component tries to find the most useful links that are critical for producing accurate classes across multiple databases. The last component is a decision tree classification algorithm which exploits the useful links discovered automatically across the databases. Experiments performed on real databases were very satisfactory with an average accuracy of 86.5% and showed that SCATTER system succeeded in achieving a fully automated classification from multiple heterogeneous databases.
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    (Université de M'sila, 2018-05) Attia, abdelouahab
    In this paper, a new method based on Log Gabor- TPLBP (LGTPLBP) has been proposed. However the Three Patch Local Binary Patterns (TPLBP) technique used in face recognition has been applied in Finger-Knuckle-Print (FKP) recognition. The 1D- Log Gabor filter has been used to extract the real and the imaginary images from each of the Region of Interest (ROI) of FKP images. Then the TPLBP descriptor on both images has been applied to extract the feature vectors of the real image and the imaginary image respectively. These feature vectors have been jointed to form a large feature vector for each image FKP. After that, the obtained feature vectors of all images are processed directly with a dimensionality reduction algorithm, using linear discriminant analysis (LDA). Finally, the cosine Mahalanobis distance (MAH) has been used for matching stage. To evaluate the effectiveness of the proposed system several experiments have been carried out. The Hong Kong Polytechnic University (PolyU) FKP database has been used during all of the tests. Experimental results show that the introduced system achieves better results than other state-of-the-art systems for both verification and identification.
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    Counterexamples in Model Checking – A Survey
    (Université de M'sila, 2018-09) Debbi, Hichem
    Model checking is a formal method used for the verification of finite-state systems. Given a system model and such specification, which is a set of formal properties, the model checker verifies whether or not the model meets the specification. One of the major advantages of model checking over other formal methods its ability to generate a counterexample when the model falsifies the specification. Although the main purpose of the counterexample is to help the designer to find the source of the error in complex systems design, the counterexample has been also used for many other purposes, either in the context of model checking itself or in other domains in which model checking is used. In this paper, we will survey algorithms for counterexample generation, from classical algorithms in graph theory to novel algorithms for producing small and indicative counterexamples. We will also show how counterexamples are useful for debugging, and how we can benefit from delivering counterexamples for other purposes. Povzetek: Pregledni ˇclanek se ukvarja s protiprimeri v formalni metodi za preverjanje konˇcnih avtomatov, tj. sistemov manjše raˇcunske moˇci kot Turingovi stroji. Protiprimeri koristijo snovalcem na veˇc naˇcinom, predvsem kot naˇcin preverjanja pravilnosti delovanja.
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    An Agents’ Model Using Ontologies and Web Services for Creating and Managing Virtual Enterprises
    (Université de M'sila, 2018-12) Mahmoud, Brahimi
    Currently, the economic competition has become very tough where successful companies find themselves obliged to develop their organizations and diversify their work strategies. Thereby, virtual enterprises concept imposes itself and overcomes geographical constraints by allowing companies to cooperate and address their shortcomings through exchanging their services and skills. The present paper proposes a virtual enterprise model based on the use of agents endorsed by the use of ontologies and web services. The coupling of these concepts allows reducing the high costs associated with the use of technology and improves flexibility and intelligence both in the negotiation process through the selection of the best partners, and in the managing process by ensuring the effective cooperation between partners.
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    RFID Authentication Protocols Based on Error- Correcting Codes: A Survey
    (Université de M'sila, 2017-05-16) Chikouche, Noureddine; Cherif, Foudil; Cayrel, Pierre-Louis; Benmohammed, Mohamed
    Code-based cryptography is a very promising research area. It allows the construction of different cryptographic mechanisms (e.g. identification protocol, public-key cryptosystem, etc.). McEliece cryptosystem is the first code-based public-key cryptosystem; several variants of this cryptosystem were proposed to design various security protocols in different systems. In this paper, we present a survey on various and recent authentication protocols in radio frequency identification systems which use diverse variants of the McEliece cryptosystem. Moreover, we discuss the security and the performance of each presented protocol.
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    Combination of Genetic Algorithm with Dynamic Programming for Solving TSP
    (Université de M'sila, 2017-07-02) Hemmak, Allaoua
    This paper presents a combination of Genetic Algorithm (GA) with Dynamic Programming (DP) to solve the well-known Travelling Salesman Problem (TSP). In this work, DP is integrated as a GA operator with a certain probability. In specific, at a given GA generation, the individuals are subdivided into a number of equal segments of genes, and the shortest path on each segment is obtained by applying a DP algorithm. Since the computational complexity of the DP is O (k22k), it becomes of O(1) when k is small. Experimental analyses are conducted to investigate the impact and trade-offs among DP probability, segment size and time processing on the solution quality and computational effort. In addition, we will implement a basic GA approach to compare results and show the contribution of combination of combination approach. Experimental results on benchmark instances showed that the combined GA-DP algorithm reduces significantly the computational effort, produces a clearly improved solution quality and avoids early premature convergence of GA.
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    New Properties for Solving the Single- Machine Scheduling Problem with Early/Tardy Jobs
    (Université de M'sila, 2016-07-12) Hemmak, Allaoua; Bouderah, Brahim
    This paper presents a mathematically enhanced genetic algorithm (MEGA) using the mathematical properties of the single-machine scheduling of multiple jobs with a common due date. The objective of the problem is to minimize the sum of earliness and tardiness penalty costs in order to encourage the completion time of each job as close as possible to the common due date. The importance of the problem is derived from its NP-hardness and its ideal modeling of just-in-time concept. This philosophy becomes very significant in modern manufacturing and service systems, where policy makers emphasize that a job should be completed as close as possible to its due date. That is to avoid inventory costs and loss of customer’s goodwill. Five mathematical properties are identified and integrated into a genetic algorithm search process to avoid premature convergence, reduce computational effort, and produce high-quality solutions. The computational results demonstrate the significant impact of the introduced properties on the efficiency and effectiveness of MEGA and its competitiveness to state-of-the-art approaches.