International Conference Papers
Permanent URI for this collectionhttp://dl.cerist.dz/handle/CERIST/4
Browse
12 results
Search Results
Item Towards Big Data Analytics over Mobile User Data using Machine Learning(IEEE, 2023-01) Ichou, Sabrina; Hammoudi, Slimane; Cuzzocrea, Alfredo; Meziane, Abdelkrim; Benna, AmelMachine 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.Item On the challenges of mobility prediction in smart cities(Copernicus Publications, 2020) Boukhedouma, H.; Meziane, Abdelkrim; Hammoudi, S.; Benna, AmelThe 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.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, NadiaStroke 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.Item Object Detection in Images Based on Homogeneous Region Segmentation(Springer, 2018) Amrane, Abdesalam; Meziane, Abdelkrim; Boulkrinat, Nour El HoudaImage 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.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, AmelAprès 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.Item An Approach to Improve Business Process Models Reuse Using LinkedIn Social Network(Springer International Publishing AG 2017, Editors: Rocha, Á., Correia, A.M., Adeli, H., Reis, L.P., Costanzo, S. (Eds.), 2017-04) Khider, Hadjer; Benna, Amel; Meziane, Abdelkrim; Hammoudi, SlimaneBusiness process (BP) modeling is an important stage in Business Process Management (BPM) lifecycle. However, modeling BP from scratch is fallible task, complex, time-consuming and error prone task. One of the promising solutions to these issues is the reuse of BP models. BP reusability during the BP modeling stage can be very useful since it reduces time and errors modeling, simplify users’ modeling tasks, improve the quality of process models and enhance modeler’s efficiency. The main objective of this paper is to propose a Social BPM approach based on the user social profile to perform the reuse of BP models. We identify the need of exploring user profile to reuse BP models. The LinkedIn social network is used to extract the users’ business interests. These user business interests are then used to recommend the appropriate BP model.Item Binarization of document images with various object sizes(IEEE, 2017-04) Hadjadj, Zineb; Meziane, AbdelkrimThere are a lot of document image binarization techniques that try to differentiate between foreground and background but many of them fail to correctly detect all the text pixels because of degradations. In this paper, a new binarization method for document images is presented. The proposed method is based on the most commonly used binarization method: Sauvola’s, which performs relatively well on classical documents, however, three main defects remain: the window parameter of Sauvola’s formula does not fit automatically to the image content, is not robust to low contrasts, and not invariant with respect to contrast inversion. Thus for some documents, the content may not be retrieved correctly. In this paper we try to overcome one of the limitations of Sauvola’s binarization which is the Handling badly various object sizes. The well-known Chan-Vese active contour model is use in combination with the computed Sauvola’s binarization step to guarantee good quality binarization for both small and large objects inside a single document, without adjusting manually the window size to the document content. The efficiency of the proposed method is shown on several document images with various object sizes.Item Les Manuscrits Arabes Anciens En Ligne : Pratiques et Recommandations(2017-05-11) Habbak, Noureddine; Meziane, AbdelkrimLes technologies de l’information ont révolutionné la bibliothèque classique. Aujourd’hui, de nombreuses bibliothèques passent au monde numérique. L’accès aux documents qui ont tendance à se détériorer rapidement et qui sont très demandés tels que les manuscrits arabes anciens devient de plus en plus simple, ce qui assure la conservation des manuscrits et garantit une large diffusion de ces documents. Ce travail consiste à recenser les différentes bibliothèques numériques qui disposent de ce type de documents selon des critères bien établis et d’effectuer une étude critique et comparative entre ces institutions afin de dégager ce que doit comporter une bonne bibliothèque numérique des manuscrits arabes.Item A tracking approach for text line segmentation in handwritten documents(Springer, 2017-02-24) Setitra, Insaf; Hadjadj, Zineb; Meziane, AbdelkrimTracking of objects in videos consists of giving a label to the same object moving in different frames. This labeling is performed by predicting position of the object given its set of features observed in previous frames. In this work, we apply the same rationale by considering each connected component in the manuscript as a moving object and to track it so that to minimize the distance and angle of the connected component to its nearest neighbor. The approach was applied to images of ICDAR 2013 handwritten segmentation contest and proved to be robust against text orientation, size and writing script.Item Perimeter Histogram based Approach for Calligraphy Classification in Ancient Manuscripts(IEEE Xplore, 2014-12) Setitra, Insaf; Meziane, AbdelkrimManual annotation of images is usually a mandatory task in many applications where no knowledge about the image is available. In presence of huge number of images, this task becomes very tedious and prone to human errors. In this paper, we contribute in automatic annotation of ancient manuscripts by discovering manuscript calligraphy. Ancient manuscripts count a very large number of Persian and Maghrebi writing especially in Noth Africa. Distinguishing between these two calligraphies allows better classifying them and so annotating them. We use background constructing followed by extraction of simple features to classify manuscript calligraphies using an SVM classifier.