BRIK Youcef

Chbabhi Taher , Messad Ahmed

MY DAILY HEALTH: Helping system for medical diagnosis using artificial intelligence

Djerarda Imadeddine

Système de sécurité en temps réel pour les conducteurs: Détection de la somnolence et les distractions en utilisant l'intelligence artificielle

Benaamam Khalil , Sekkiou, Fayçal

Deep transfer learning-based feature extraction for breast cancer detection

Lahmar Hanine , Ziani Zahra

Early detection and classification for diabetic retinopathy by deep learning

Zouaoui Rafik , Nadir Rafik

Deep learning approach for Alzheimer diagnosis using MRI images

Arbane Mohamed

Deep learning for medical data prediction

Guenani Moussa

Embedded deep learning and Raspberry pi system for assisting visually impaired/blind people

Allal Bassem , Bouafia Fatima

Fusion bimodal pour l'identification biométrique

DEGHFEL Ebderezzak , Hrizi Abdelghani

Réalisation d'un régulateur industriel à base d'un Arduino et LabView

Beghriche Tawfiq , Bentayeb Farida

Prédiction de pathologies les plus fréquentes en utilisant deep learning

Bouguerra Oussama , Benslimane Oussama

solar radiation prediction using Machine Learning

Abdelhafid Oualid , Senouci Abdelkarim

Identification Biométrique par les veins des doigts

Mira Ouassila , Saada Sara

Détection des contours dans les images niveaux de gris 'étude comparative'

ABASSI Brahim

APPROCHE DE SÉLECTION DES DONNÉES BIOMÉDICALES DANS UN PROBLÈME DE RECONNAISSANCE DE PATHOLOGIES EN UTILISANT LES SUPPORT VECTOR MACHINES

BENABDI Mouad

IDENTIFICATION DES PERSONNES PAR LES EMPREINTES D’ARTICULATION DES DOIGTS ET LE DEEP LEARNING

MAHDI Fatma Zohra , TABI Fattoum

Caractérisation d’empreinte de l’articulation de doigt pour l’authentification des personnes

BACHIRI Mohamed , DAKHANE Mohammed

ÉVALUATION ET DETERMINATION DES VARIABLES D’ENTREE POUR UN MODELE MULTICLASSE INTELLIGENT DANS LES PROCÉDÉS DE TRAITEMENT DES EAUX PROPRES

AICHE Ishaq , KETFI Meryem

APPRENTISSAGE DES MACHINES PUISSANTES POUR LA RECONNAISSANCE DES FORMES

BENHAMIDA Assia

Repérage de Mots dans un Document Arabe Ancien en Utilisant les Caractéristiques Statistiques

DAHMANE Nassereddine

Étude et Réalisation d’un Suiveur Solaire Pour un Panneau Photovoltaïque

ZEROUGUI Sana

Repérage de Mots dans des Documents Arabes Historiques en Utilisant la Transformée de Radon

Biometric systems play a crucial role in securely recognizing an individual’s identity based on physical and behavioral traits. Among these methods, finger vein recognition stands out due to its unique position beneath the skin, providing heightened security and individual distinctiveness that cannot be easily manipulated. In our study, we propose a robust biometric recognition system that combines a lightweight architecture with depth-wise separable convolutions and residual blocks, along with a machine-learning algorithm. This system employs two distinct learning strategies: single-instance and multi-instance. Using these strategies demonstrates the benefits of combining largely independent information. Initially, we address the problem of shading of finger vein images by applying the histogram equalization technique to enhance their quality. After that, we extract the features using a MobileNetV2 model that has been fine-tuned for this task. Finally, our system utilizes a support vector machines (SVM) to classify the finger vein features into their classes. Our experiments are conducted on two widely recognized datasets: SDUMLA and FV-USM and the results are promising and show excellent rank-one identification rates with 99.57% and 99.90%, respectively.

Citation

Cheyma nadir , BILAL Attallah , BRIK Youcef , , (2024-08-14), A sequential combination of convolution neural network and machine learning for finger vein recognition system, Signal, Image and Video Processing, Vol:18, Issue:, pages:8267–8278, SPRINGER LONDON LTD

Accurate classification of brain tumors from magnetic resonance imaging (MRI) scans is critical for selecting appropriate treatment strategies. In this work, three convolutional neural network (CNN) architectures were developed and trained on a multi-class brain tumor MRI dataset containing four types of images: no tumor, meningioma, glioma, and pituitary tumors. The CNN models achieved test accuracy scores above 99%. Ensemble techniques using average and majority voting were applied to boost performance further and integrate the three models' predictions. The ensemble approach provided a slight but noticeable improvement, with the best model reaching 99.46% accuracy. Overall, the deep CNN models demonstrated excellent capabilities for distinguishing between the multiple tumor classes from MRI scans. The ensemble method offered a way to extract incremental benefits by combining multiple trained models.

Citation

BRIK Youcef , ,(2023-12-05), Ensemble Learning vs. Convolutional Neural Networks for Multiclass Brain Tumor Classification of MRI Images,International Conference on Science, Technology, Engineering and Management,Istanbul

Artificial intelligence (AI) and biological data analysis have advanced significantly in recent years, providing new opportunities for disease diagnosis, treatment planning, and patient monitoring. Biomedical data, which includes a variety of medical imaging and clinical data, is crucial in revealing the complex mechanisms underlying different health disorders. The use of AI methods, especially deep learning and transfer learning, has emerged as a game-changing method for gleaning relevant information from this vast amount of biomedical data. The present work focuses on the application of deep learning to diagnose and analyze many different diseases for helping healthcare professionals to take care of people. Our proposed system explores the application of deep transfer learning models, such as ResNet50, DenseNet-121, and EfficientNet-B3, in analyzing brain tumors, Alzheimer's disease, respiratory diseases, skin cancer, and gastrointestinal diseases. This analysis can give the healthcare professionals a precise information concerning the presence/absence of disease, the severity of tumor, the type of illness and the treatment planning. Furthermore, many preprocessing operations such as data augmentation and image filtering have been involved in order to enhance the decision accuracy. The experiments evaluated and conducted on real databases showed that our system can effectively help medical staff in their daily work for taking care of people.

Citation

BRIK Youcef , ,(2023-12-03), Helping System for Brain Disease Diagnosis Using Deep Learning,International Conference on Science, Technology, Engineering and Management,Istanbul

Medical professionals have become interested in using deep learning techniques in the treatment of many diseases, such as diabetic retinopathy (DR), because they come with great data analysis capabilities. We propose in this paper a detection system for DR (the presence of this disease or not) using deep learning models. We used the APTOS2019 dataset from Kaggle, which includes high-resolution retinal pictures, to evaluate this approach. Then, we use transfer learning to characterize the DR images with discriminating features. Our experiments have been conducted on five models: VGG16, VGG19, InceptionV3, Xception, and MobileNetV2. The obtained results are very satisfactory in terms of accuracy.

Citation

BRIK Youcef , ,(2023-10-23), Deep Learning for Biomedical and Biometrics Applications,CAIRO Talk series,Kuala Lampur

Internet public social media and forums provide a convenient channel for people concerned about public health issues, such as COVID-19, to share and discuss information/misinformation with each other. In this paper, we propose a natural language processing (NLP) method based on Bidirectional Long Short-Term Memory (Bi-LSTM) technique to perform sentiment classification and uncover various issues related to COVID-19 public opinions. Bi-LSTM is an improved version of conventional LSTMs for generating the output from both left and right contexts at each time step. We experimented with real datasets extracted from Twitter and Reddit social media platforms, and our experimental results showed improved metrics compared with the conventional LSTM model as well as recent studies available in the literature. The proposed model can be used by official institutions to mitigate the effects of negative messages and to understand peoples’ concerns during the pandemic. Furthermore, our findings shed light on the importance of using NLP techniques to analyze public opinion and to combat the spreading of misinformation and to guide health decision-making.

Citation

BRIK Youcef , Arbane Mohamed, Rachid Benlamri, Ayman Diyab Alahmar, , (2023-02-01), Social media-based COVID-19 sentiment classification model using Bi-LSTM, Expert Systems with Applications, Vol:212, Issue:2023, pages:118710, Elsevier

Monitoring of water quality is one of the world’s main intentions for countries. Classification techniques based on support vector machines (SVMs) and artificial neural network (ANN) has been widely used in several applications of water research. Water quality assessment with high accuracy and efficiency with innovational approaches permitted us to acquire additional knowledge and information to obtain an intelligent monitoring system. In this paper, we present the use of principal component analysis (PCA) combined with SVM and ANN with decision templates combination data fusion method. PCA was used for features selection from original database. The multi-layer perceptron network (MLP) and the one-against-all strategy for SVM method have been widely used. Decision templates are applied to increase the accuracy of the water quality classification. The specific classification approach was employed to assess the water quality of the Tilesdit dam in Algeria as a study area, defined with a dataset of eight physicochemical parameters collected in the period 2009–2018, such as temperature, pH, electrical conductivity, and turbidity. The selection of the excellent parameters of the used models can be improving the performance of classification process. In order to assess their results, an experiment step using collected dataset corresponding to the accuracy and running time of training and test phases, and robustness to noise, is carried out. Various scenarios are examined in comparative study to obtain the most results of decision step with and without feature selection of the input data. From the results, we found that the integration of SVM and ANN with PCA yields accuracy up than 98%. The combination by decision templates of two classifiers SVM and ANN with PCA yields an accuracy of 99.24% using k-fold cross-validation. The combination data fusion enhanced expressively the results of the proposed monitoring framework that had proven a considerable ability in surface water quality assessment.

Citation

BRIK Youcef , , (2023-01-01), A decision fusion method based on classification models for water quality monitoring, Environmental Science and Pollution Research, Vol:30, Issue:, pages:22532–22549, Springer

Damage to the retina from diabetes can lead to permanent vision loss due to a condition known as diabetic retinopathy. In order to avoid this, it is essential to diagnose this disease early. To address these problems, this paper proposes a two-branch Grad-CAM attention-guided convolution neural network (AG-CNN) with initial CLAHE image preprocessing. The AG-CNN first builds a general attention to the entire image with the global branch, in order to further concentrate the system's attention on the localized areas of the problems, the system isolate the important regions (ROIs) of the global image and then feeds them to a local branch. This extensive experiment is based on the APTOS 2019 DR dataset. In order to start, we offer a solid global baseline that, using DenseNet-121 as a starting point, produced average accuracy/AUC values of 0.9746/0.995, respectively. The average accuracy and AUC of the AG-CNN are increased to 0.9848 and 0.998, respectively, after creating the local branch. which represents a new state-of-the-art in the field.

Citation

Mohamed Abderaouf Moustari , BRIK Youcef , BILAL Attallah , ,(2022-11-26), Enhancement of diabetic retinopathy classification using attention guided convolution neural network,ICATEEE2022,M'sila, Algeria

Deep learning based framwork for automatic diabetic retinopathy detection

Citation

BRIK Youcef , BILAL Attallah , Ishaq AICHE , ,(2022), Deep learning based framwork for automatic diabetic retinopathy detection,ICCTA 2022,Alexendrie_Egypt

Finger vein based cnn for human recognition

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Cheyma nadir , BILAL Attallah , BRIK Youcef , ,(2022), Finger vein based cnn for human recognition,ICATEEE2022,M'sila-Algeria

Transfer learning for diabetic retinopathy detection

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Ishaq AICHE , BRIK Youcef , BILAL Attallah , ,(2022), Transfer learning for diabetic retinopathy detection,ICATEEE2022,M'sila-Algeria

Brain tumor classification based deep transfer learning

Citation

BILAL Attallah , BRIK Youcef , oussama.bougeurra@univ-msail.dz, ,(2022), Brain tumor classification based deep transfer learning,ICATEEE2022,M'sila_Algeria

Transfer learning approche for alzhimer's dicease diagnosis using mri image

Citation

BRIK Youcef , BILAL Attallah , DJERIOUI Mohamed , rafik.zouaoui@univ-msila.dz, ,(2022), Transfer learning approche for alzhimer's dicease diagnosis using mri image,ICATEEE2022,M'sila_Algeria

Ear Recognition using Ensemble of Deep Features and Machine Learning Classifiers

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Cheyma nadir , BILAL Attallah , BRIK Youcef , ,(2022), Ear Recognition using Ensemble of Deep Features and Machine Learning Classifiers,ICCTA 2022,Alexandria, Egypt

This paper deals with

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BRIK Youcef , ,(2022), Performance Analysis ofTwin-SupportVector Machine in Breast Cancer Prediction,ICATEEE2022,Algeria

One of the main reasons for disability and premature mortality in the world is diabetes disease, which can cause different sorts of damage to organs such as kidneys, eyes, and heart arteries. The deaths by diabetes are increasing each year, so the need to develop a system that can effectively diagnose diabetes patients becomes inevitable. In this work, an efficient medical decision system for diabetes prediction based on Deep Neural Network (DNN) is presented. Such algorithms are state-of-the-art in computer vision, language processing, and image analysis, and when applied in healthcare for prediction and diagnosis purposes, these algorithms can produce highly accurate results. Moreover, they can be combined with medical knowledge to improve decision-making effectiveness, adaptability, and transparency. A performance comparison between the DNN algorithm and some well-known machine learning techniques as well as the state-of-the-art methods is presented. The obtained results showed that our proposed method based on the DNN technique provides promising performances with an accuracy of 99.75% and an F1-score of 99.66%. This improvement can reduce time, efforts, and labor in healthcare services as well as increasing the final decision accuracy.

Citation

Tawfiq Beghriche , DJERIOUI Mohamed , BRIK Youcef , BILAL Attallah , , (2021-12-17), An efficient prediction system for diabetes disease based on deep neural network, Complexity, Vol:2021, Issue:1, pages:1-14, Hindawi

Heart disease is the leading cause of death in the world according to the World Health Organization (WHO). Researchers are more interested in using machine learning techniques to help medical staff diagnose or detect heart disease early. In this paper, we propose an efficient medical decision support system based on twin support vector machines (Twin-SVM) for heart disease diagnosing with binary target (i.e. presence or absence of disease). Unlike conventional support vector machines (SVM) that finds only one optimal hyper- plane for separating the data points of first class from those of second class, which causes inaccurate decision, Twin-SVM finds two non-parallel hyper-planes so that each one is closer to the first class and is as far from the second class as possible. Our experiments are conducted on real heart disease dataset and many evaluation metrics have been considered to evaluate the performance of the proposed method. Furthermore, a comparison between the proposed method and several well-known classifiers as well as the state-of-the-art methods has been performed. The obtained results proved that our proposed method based on Twin-SVM technique gives promising performances better than the state-of-the-art. This improvement can seriously reduce time, materials, and labor in healthcare services while increasing the final decision accuracy.

Citation

BRIK Youcef , DJERIOUI Mohamed , BILAL Attallah , , (2021), Efficient heart disease diagnosis based on twin support vector machine, DIAGNOSTYKA, Vol:22, Issue:3, pages:3-11, PTDT

One of the most leading death causes in the world is brain tumor. Solving brain tumor segmentation and classification by relying mainly on classical medical image processing is a complex and challenging task. In fact, medical evidence shows that manual classification with human-assisted support can lead to improper prediction and diagnosis. This is mainly due to the variety and the similarity of tumors and normal tissues. Recently, deep learning techniques showed promising results towards improving accuracy of detection and classification of brain tumor from magnetic resonance imaging (MRI). In this paper, we propose a deep learning model for the classification of brain tumors from MRI images using convolutional neural network (CNN) based on transfer learning. The implemented system explores a number of CNN architectures, namely ResNet, Xception and MobilNet-V2. This latter achieved the best results …

Citation

BRIK Youcef , DJERIOUI Mohamed , ,(2021), Transfer learning for automatic brain tumor classification using MRI images,2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH),Boumerdes, Algeria

Nowadays, one of the main reasons for disability and mortality premature in the world is the heart disease, which make its prediction is a critical challenge in the area of healthcare systems. In this paper, we propose a heart disease prediction system based on Neighborhood Component Analysis (NCA) and Support Vector Machine (SVM). In fact, NCA is used for selecting the most relevant parameters to make a good decision. This can seriously reduce the time, materials, and labor to get the final decision while increasing the prediction performance. Besides, the binary SVM is used for predicting the selected parameters in order to identify the presence/absence of heart disease. The conducted experiments on real heart disease dataset show that the proposed system achieved 85.43% of prediction accuracy. This performance is 1.99% higher than the accuracy obtained with the whole parameters. Also, the proposed system outperforms the state-of-the-art heart disease prediction.

Citation

DJERIOUI Mohamed , BRIK Youcef , Mohamed Ladjal , BILAL Attallah , , (2019), Neighborhood Component Analysis and Support Vector Machines for Heart Disease Prediction, Ingénierie des Systèmes d’Information (ISI), Vol:24, Issue:6, pages:591-595, International Information and Engineering Technology Association (IIETA)

Multimodal biometrics is an evolving technology in the fields of security. Biometrics system reduces the effort of remember a memorable password. Multimodal biometrics system uses two or more traits for efficient recognition. This paper presents a hand biometric system by fusing information of palmprint and finger knuckle. To this end, BSIF ( Binarized Statistical Image Features) filter and LBP (Local binary patterns) coefficients are employed to obtain the Finger-knuckle-print and palm-print traits, and subsequently selection of the features vector is conducted with PCA (Principal Component Analysis) transforms in higher coefficients. To match the finger knuckle or palm-print feature vector, the (ELM) Extreme learning machine is applied. According to the experiment outcomes, the proposed system not only has a significantly high recognition rate but it also affords greater security compared to the single biometric system.

Citation

BILAL Attallah , BRIK Youcef , DJERIOUI Mohamed , ABDELWAHHAB BOUDJELAL , ,(2019), Fusing Palmprint, Finger-knuckle-print for Bi-modal Recognition System Based on LBP and BSIF,International Conference on Image and Signal Processing and their Applications,Mostaganem, Algeria, Algeria

A Biometrics identification system is refers to the automatic recognition of individual person based on their characteristics. Basically biometrics system has two broad areas namely unimodal biometric system and multimodal biometric system. However, a reliable recognition system requires multiple resources [1]. Although multimodality improves the accuracy of the systems, it occupies a large memory space and consumes more execution time considering the collected information from different resources. Therefore we have considered the feature selection[2], that is, the selection of the best attributes that enhances the accuracy and reduce the memory space as a solution. As a result, acceptable recognition performances with less forge and steal can be guaranteed. In this work we propose an identification system using multimodal fusion of finger-knuckle-print, fingerprint and palmprint by adopting several techniques in feature level for multimodal fusion[3]. A feature level fusion and selection is proposed for the fusion of these three biological traits. The proposed system has been tested on the largest publicly available PolyU [4] and Delhi FKP[5] databases. It has shown good performance.

Citation

BILAL Attallah , Hamza BENNACER , Mohamed Ladjal , BRIK Youcef , Youssef Chahir, ,(2019), Finger-knuckle-print, Plamprint and Fingerprint for Multimodal Recognition System Based on mRMR features selection,IC2MAS19,Istanbul-Turkey

In this paper, we propose a heart disease prediction system based on Neighborhood Component Analysis (NCA) and Support Vector Machine (SVM). In fact, NCA is used for selecting the most relevant parameters to make a good decision. This can seriously reduce the time, materials, and labor to get the final decision while increasing the prediction performance. Besides, the binary SVM is used for predicting the selected parameters in order to identify the presence/absence of heart disease. The conducted experiments on real heart disease dataset show that the proposed system achieved 85.43% of prediction accuracy. This performance is 1.99% higher than the accuracy obtained with the whole parameters. Also, the proposed system outperforms the state-of-the-art heart disease prediction.

Citation

DJERIOUI Mohamed , BRIK Youcef , Mohamed Ladjal , BILAL Attallah , Youssef Chahir, ,(2019), Heart disease prediction using neighborhood component analysis and support vector machines,The VIIIth International Workshop on Representation, analysis and recognition of shape and motion FroM Imaging data (RFMI 2019),Tunisia

Most of existing approaches in keyword spotting are system-oriented, which did not take into consideration the user’s needs. However, a user may want to find words, sentences, or texts that match his target image in his mind. The challenge here is how to formulate one’s mental image to reach what he is looking for. The key idea is to design and build a model that properly adapts the human reasoning in information searching through an interactive process. We propose a mental model for handwritten keyword spotting based on relevance feedback, feature weighting, and optimization. This model meets simultaneously the user’s needs, the system behavior, and the user–system relationship. In an appropriate feature space, the query is progressively built from user-supplied keywords, old queries, and spotted images. This dynamic process not only converges toward the desired word images, but also helps the hesitant user to clarify progressively what he is looking for. The proposed model was showcased via a user-friendly interface, which we tested including real users on three well-known handwritten datasets; Institute for Communications, Braunschweig University, Germany/École Nationale d’Ingénieurs de Tunis, Tunisia, Institut für informatik und Angewandte Mathematik, and George Washington. The experimental results show that the proposed method provides promising scores with a reasonable number of refinements.

Citation

BRIK Youcef , Djemel Ziou, , (2018), Mental model for handwritten keyword spotting, Journal of Electronic Imaging, Vol:27, Issue:5, pages:053027, SPIE Digital Library (USA)

This paper relates to the study and the realization of a multilevel inverter associated with the card-based control of microcontroller PIC16F876. This is the series connection of three single phase inverter H-bridge with separate continuous voltages. In the theoretical section we presented a study on the model of the inverter and its control strategies, the full wave and the PWM with its variants. In the practical part, after realizing the control cards and power we implemented for control strategies on the PIC16F876.

Citation

FAYSSAL Ouagueni , BRIK Youcef , ,(2012), Réalisation d’un onduleur monophasé cascadé à sept niveaux piloté par le PIC16F876,Deuxième Conférence Internationale sur la Maintenance, la Gestion, la Logistique et l’Electrotechnique,Oran, Algeria