International Conference Papers

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Author: search.filters.author.Hadjar, Samir

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    Investigating Security and Privacy Concerns in Deep-Learning-based Pervasive Health Monitoring Architectures
    (IEEE, 2023-11) Boulemtafes, Amine; Amira, Abdelouahab; Derki, Mohamed Saddek; Hadjar, Samir
    Pervasive Health Monitoring (PHM) uses sensors and wearable devices and data analytics for real-time health monitoring. It enables early detection and personalized care interventions. This technology has the potential to revolutionize healthcare by improving proactive and preventive care.Besides, Deep learning (DL) based PHM is even more promising as it improves the discovery of complex patterns and correlations. This leads to precise health monitoring and personalized care, enhances diagnostics, and ultimately improves patient outcomes in the field of healthcare.However, privacy and security considerations must be addressed for successful implementation. This paper investigates the security and privacy concerns in Pervasive Health Monitoring architectures. It discusses through an illustrative DL-based PHM architecture the potential threats and attacks during the inference and training phases, and identifies key security and privacy issues. It also gives insights on countermeasures and technological solutions that can address security and privacy concerns in PHM architectures.
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    Detection and Description the Lesions in Brain Images
    (University Cadi Ayyad (Marroc), 2005-11) Lassouaoui, Nadia; Hamami, Latifa; Nouali-Taboudjemat, Nadia; Hadjar, Samir; Saadi, Hocine
    In this paper, we present the various stages for lesion recognition in brain images. We firstly apply a filtering based on geodesic reconstruction operator for increasing the quality of image. After, we use an unsupervised segmentation genetic algorithm for detecting the abnormal zones with respect of theirs morphological characteristics because they define the nature of illness (cyst, tumour, malignant, benign, …). The obtained segmented images are analyzed for computing the characteristics of illness which are necessary for the recognition stage for deducing a decision about the type of illness. So, we give also the various algorithms used for computing the morphological characteristics of lesions (size, shape, position, texture, …). Since we obtain a decision about the malignity or benignity of the lesion and a quantitative information for helping the doctors to locate the sick part.