International Conference Papers

Permanent URI for this collectionhttp://dl.cerist.dz/handle/CERIST/4

Browse

Author: search.filters.author.Meziane, AbdelkrimStart date: 2018

Search Results

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Towards Big Data Analytics over Mobile User Data using Machine Learning
    (IEEE, 2023-01) Ichou, Sabrina; Hammoudi, Slimane; Cuzzocrea, Alfredo; Meziane, Abdelkrim; Benna, Amel
    Machine Learning (ML) is a science that forces computers to learn and behave like humans. As these systems interact with data, networks, and people, they automatically become smarter so that they can eventually solve or predict a practical issue in the world for us. The use of ML can be a giant leap for cannot simply be integrated as the top layer. This requires redefining workflow, architecture, data collection and storage, analytics, and other modules. This paper aims to discuss the issue of machine learning technique for analysis data of mobile user. First, we identified the machine learning benefits and drawbacks, challenges, advantages of using Machine Learning. Then, we propose a generic model of analytic mobile user data using ML, the model is centered on the machine learning component, which interacts with two other components, including mobile user data, and system. The interactions go in both directions. For instance, mobile user data serves as inputs to the learning component and the latter generates outputs; system architecture has impact on how learning algorithms should run and how efficient it is to run them, and simultaneously meeting. Mobile user data goes through several stages: prepossessing which includes the steps we need to follow to transform or encode the data so that it can be easily analyzed by the machine. Then, modelling in this step we will be clustering and classification the data obtained. Finally, evaluation, various measures of performance, accuracy, recall, precision, and F-measure were used to analyze the results of the naive Bayes, SVM, and K-nearest neighbor classification algorithms.
  • Thumbnail Image
    Item
    On the challenges of mobility prediction in smart cities
    (Copernicus Publications, 2020) Boukhedouma, H.; Meziane, Abdelkrim; Hammoudi, S.; Benna, Amel
    The mass of data generated from people’s mobility in smart cities is constantly increasing, thus making a new business for large companies. These data are often used for mobility prediction in order to improve services or even systems such as the development of location-based services, personalized recommendation systems, and mobile communication systems. In this paper, we identify the mobility prediction issues and challenges serving as guideline for researchers and developers in mobility prediction. To this end, we first identify the key concepts and classifications related to mobility prediction. We then, focus on challenges in mobility prediction from a deep literature study. These classifications and challenges are for serving further understanding, development and enhancement of the mobility prediction vision.
  • Thumbnail Image
    Item
    Leap motion controller for upper limbs physical rehabilitation in post-stroke patients: a usability evaluation
    (2022-05) Hadjadj, Zineb; Masmoudi, Mostefa; Meziane, Abdelkrim; Zenati, Nadia
    Stroke in Algeria is one of the most important causes of severe physical disability. Since the disease strongly influences the quality of life of patients, optimal solutions for the treatment of post-stroke patients are needed. The use of new technologies in the field of rehabilitation aims to reduce the impact of functional problems. Recent studies have shown that technologies such as virtual reality and video games can provide a way that can motivate and help patients recover their motor skills. In this paper, our objective is to evaluate the usability of the Leap Motion Controller virtual reality system (LMC), which is a sensor that captures the movement of the patient's hands and fingers without the need to place sensors or devices on the body, with serious games specifically designed for upper limbs rehabilitation in post-stroke patients. We measured the usability of the LMC system used with serious games as well as the level of satisfaction among healthy participants and post-stroke patients from Bounaama Djilali Hospital (CHU Douera) in Algeria. The results show favorable data, for the first time, the LMC is a usable tool, measured by the System Usability Scale (SUS). In addition, participants demonstrated good motivation, enjoyment and the majority of them said that they would like to use the proposed system in future treatment. Nevertheless, further studies are needed to confirm these preliminary findings.
  • Thumbnail Image
    Item
    Object Detection in Images Based on Homogeneous Region Segmentation
    (Springer, 2018) Amrane, Abdesalam; Meziane, Abdelkrim; Boulkrinat, Nour El Houda
    Image segmentation for object detection is one of the most fundamental problems in computer vision, especially in object-region extraction task. Most popular approaches in the segmentation/object detection tasks use sliding-window or super-pixel labeling methods. The first method suffers from the number of window proposals, whereas the second suffers from the over-segmentation problem. To overcome these limitations, we present two strategies: the first one is a fast algorithm based on the region growing method for segmenting images into homogeneous regions. In the second one, we present a new technique for similar region merging, based on a three similarity measures, and computed using the region adjacency matrix. All of these methods are evaluated and compared to other state-of-the-art approaches that were applied on the Berkeley image database. The experimentations yielded promising results and would be used for future directions in our work.
  • Thumbnail Image
    Item
    Classification automatique des images histologiques du cancer du sein par réseaux de neurones convolutifs (RNC)
    (Publication en ligne, 2018-08-01) Setitra, Insaf; Meziane, Abdelkrim; Mayouf, Mouna Sabrine; Hamrioui, Amel
    Après le cancer de la peau, le cancer du sein est le deuxième type de cancer le plus commun chez la femme à l’échelle mondiale. Ce dernier enregistre un taux de mortalité assez élevé comparé aux autres types de cancer. (Spanhol, Oliveira et al. 2016). Le diagnostic des tumeurs du sein pour différencier les cellules bénignes des malignes établi par le pathologiste est le fruit d’un processus minutieux, fastidieux, long et sujet à plusieurs erreurs et divergence d’avis. Afin d’essayer de palier à ces inconvénients, un vif intérêt s’est porté sur l’automatisation du processus du diagnostic. Dans ce travail, nous reportons les différentes méthodes utilisées jusque-là par la communauté scientifique et nous exposons notre méthode basée sur la classification par réseaux de neurones convolutifs (RNC) qui sont un récent type de réseaux de neurones qui relie le traitement d’images à l’apprentissage automatique, afin de déterminer de la manière la plus précise le type tumoral.