Publications Scientifiques
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Item A medical comparative study evaluating electrocardiogram signal-based blood pressure estimation(IGI Global, 2024) Moussaoui, Siham; Fellag, Sid Ali; Chebi, HocineIn general, blood pressure (BP) is measured using standard methods (medical monitors), which are widely used, or from physiological sensor data, which is a difficult task usually solved by combining several signals. In recent research, electrocardiogram (ECG) signals alone have been used to estimate blood pressure. The authors present a comparative study that evaluates ECG signal-based blood pressure estimation using complexity analysis to extract features, comparing the results obtained with a random forest regression model as well as with the combination of a stacking-based classification module and a regression module. It was determined that the best result obtained is a mean absolute error range of 3.73 mmHg with a standard deviation of 5.19 mmHg for diastolic blood pressure (DBP) and 5.92 mmHg with a standard deviation of 7.23 mmHg for systolic blood pressure (PAS).Item Novel Approach by Fuzzy Logic to Deal with Dynamic Analysis of Shadow Elimination and Occlusion Detection in Video Sequences of High-Density Scenes(Taylor and Francis Online, 2021) Chebi, Hocine; Benaissa, AbdelkaderMonitoring of high-density images from video sequences provides an important potential for crowd detection and classification. In fact, shadow presence from video sequences causes detection failures of results or mistakes in interpretation. In this contribution, we present an automatic system to deal with shadow elimination based on extracting the vector size of the movement and detection of occlusion management with the Fourier series approach because of the position and orientation of the camera, speed magnitude and visual tracking of crowd scenes, and mathematical morphology of discrete data in a non-linear approach. The model consists of distinctive real objects from fused data and crowded scene caused by shades, which often has consequence such as the failure counting and grading of vehicle and people, while dealing with traditional methods. As we reveal, our technique is principally appropriate for UMN and PETS data to eliminate shadow nuisance and detection occlusion with quite good performance. For classification, we use two classes in each image, for each category of events detected by fuzzy logic. Although this comes down to easy system modeling, as it relates to the use of fuzzy rules. The provided results advocate in favor of our method in terms of effectiveness and precision. Indeed, the proposed approach provides the ability to segment more specific objects, such as people and vehicles in real-time space. The results were compared to other methods, namely Covariance Matrices for Crowd Behavior, Social Force Method, and Ground Truth Technique.Item Strategy of detecting abnormal behaviors by fuzzy logic(IEEE, 2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, MohamedThis work falls within the framework of the video surveillance research axis. This work falls within the scope of video surveillance. It involves a link between automatic processing and problems related to video surveillance. The job is to analyze video streams coming from a network of surveillance cameras, deployed in an area of interest in order to detect abnormal behavior. Our approach in this article relies on the new application and the use of fuzzy logic in the case of division and fusion of the crowd. The detection of these behaviors will increase the speed of response of the security services in order to perform accurate analysis and detection of events in real timeItem Intelligent detection without modeling of behavior unusual by fuzzy logic(Springer, 2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, MohamedItem Strategy of detections abnormal behavior by fuzzy logic(IEEE, 2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, MohamedItem Visual analysis and reconition of crowd behavior by principal componenet analysis(laboratoire of signals and systems, 2016) Chebi, Hocine; Acheli, Dalila; Kesraoui, MohamedItem Detection method without crowd behavior modeling by fuzzy logic(2017) Chebi, Hocine; Acheli, Dalila; Kesraoui, MohamedItem Behavior recognition by principal component analysis (PCA)(International Conference on Pattern Analysis and Intelligent Systemsnd Intelligent Systems, 2016) Chebi, HocineItem Analyse visuelle d’événements anormaux de foules(2015) Chebi, Hocine; Morsly, Y.; Djouadi, MS.L’analyse du comportement humain depuis la vidéo est un domaine vaste de la vision par ordinateur. Dans ce travail, nous nous intéressons à l’analyse du comportement de la foule et de ses entités dans une scène dense. Ces scènes se caractérisent par la présence d’un grand nombre de personnes dans le champ de vision des caméras. Le problème majeur est l’élaboration d’une approche de gestion autonome d’un grand nombre d’anomalies qui est quasiment impossible à effectuer par des opérateurs. Nous présentons dans cet article une nouvelle approche pour la détection d’anomalies dans des scènes très denses en se basant sur la vitesse des individus et celle du groupe. Les différentes anomalies sont détectées d’une manier automatique sans apprentissage en utilisant la densité à base du partitionnement spatial avec application du bruit (Density Based Spatial Clustering of Application with Noise « DBSCAN »). Pour plus de robustesse et ’efficacité, nous avons introduit deux routines permettant l’élimination des ombres et la gestion des d’occlusions