M. YOUCEF Brik

MCA

Directory of teachers

Department

Departement of ELECTRONICS

Research Interests

Signal and Image Processing Pattern recognition and Machine Learning Artificial intelligence Real time systems Servo systems and regulation

Contact Info

University of M'Sila, Algeria

Email : youcef.brik@univ-msila.dz

On the Web:

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Recent Publications

2025-11-25

The 2nd International Workshop on Machine Learning and Deep Learning (WMLDL25)

Driver fatigue remains one of the most critical factors in preventable road deaths, yet conventional systems based on Convolutional Neural Networks (CNNs) often struggle to strike a vital balance between accuracy, speed, and practical usability under diverse conditions. This paper introduces WakeUp AI, an intelligent fatigue detection system explicitly designed to bridge that gap. The core framework leverages the advanced feature extraction capabilities of a Vision Transformer (ViT), combined with an optimized Support Vector Machine (SVM) classifier, resulting in an outstanding 99.82% test accuracy on the CEW dataset. This hybrid ViT-SVM approach achieves superior feature discrimination while maintaining computational efficiency suitable for edge deployment. For real-time use, WakeUp AI integrates MediaPipe FaceMesh with a streamlined ViT model, achieving inference latency of < 100ms frame. Crucially, a continuous temporal logic module constantly monitors the driver’s eye state, activating instant audio alerts only when fatigue patterns (such as prolonged eye closure duration) are robustly detected. Unlike conventional systems limited to simple, rigid thresholding, WakeUp AI intelligently adapts to diverse environments, making it exceptionally robust. By combining state-of-the-art deep learning with real-time responsiveness, WakeUp AI offers a scalable, high-performance solution for critical safety applications.
Citation

M. YOUCEF Brik, (2025-11-25), "The 2nd International Workshop on Machine Learning and Deep Learning (WMLDL25)", [international] WakeUp AI – Fatigue Detection System , Msila, Algeria

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2024-12-09

Lung and Colon Cancer Histopathological Image Classification Using Deep Learning Approaches

This paper investigates the application of deep
learning for multi-class classification of lung and colon cancer
histopathological images using the LC25000 dataset. The dataset
contains 25,000 images equally distributed across five classes:
benign and adenocarcinoma for both lung and colon tissues,
and squamous cell carcinoma for lung tissue. Preprocessing
involved converting labels to one-hot encoding and removing
1280 identified duplicate images to prevent data leakage. Three
models were trained and evaluated: a baseline CNN, an enhanced
CNN with architectural modifications and regularization, and
a transfer learning model utilizing ResNet50. The baseline
model, using Adam optimizer and categorical cross-entropy loss,
achieved a test accuracy of 62.6% and validation accuracy of
63.7%. The enhanced model, incorporating increased depth,
adjusted kernel size, L2 regularization, dropout, and the Adamax
optimizer with early stopping, reached a test accuracy of 97.9%
and validation accuracy of 98.1%. Finally, the transfer learning
model with ResNet50, fine-tuned with additional dense layers,
dropout, and early stopping, achieved near-perfect performance
with 98.9% test accuracy and 99.3% validation accuracy. This
study demonstrates the significant performance improvement
achieved through architectural enhancements and transfer learning,
highlighting the potential of deep learning for automated
diagnosis of cancer from histopathological images. The removal
of duplicate images proved essential for accurate performance
evaluation and preventing artificially inflated results due to data
leakage.
Citation

M. YOUCEF Brik, (2024-12-09), "Lung and Colon Cancer Histopathological Image Classification Using Deep Learning Approaches", [national] National Conference of Advanced Systems in Electrical Engineering (NCASEE'24) , Boumerdes

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2024-12-03

Automated Brain Tumor Classification using a Fine-Tuned EfficientNet Model

Early and accurate brain tumor detection is critical for effective
treatment planning and improving patient outcomes. Traditional
diagnostic methods, such as biopsies, are invasive and can delay timely intervention.
This research proposes an automated, non-invasive approach
for brain tumor classification using Magnetic Resonance Imaging (MRI)
and a deep learning model based on transfer learning with Efficient-
NetB0. We curated a comprehensive dataset of 3264 MRI scans, encompassing
four distinct categories: glioma, meningioma, pituitary tumors,
and healthy brain tissues. Images were acquired in various planes
(sagittal, axial, and coronal) and pre-processed to ensure consistency
and enhance model performance. Data augmentation techniques, including
rotation and resizing, were employed to increase the dataset’s diversity
and improve the model’s robustness. The EfficientNetB0 architecture,
renowned for its computational efficiency and scalability achieved
through compound scaling, serves as the foundation of our model. We
leveraged transfer learning by utilizing pre-trained weights from ImageNet
and fine-tuning the model with a custom classification head comprising
a Global Average Pooling layer, a Dropout layer for regularization,
and a Dense layer with SoftMax activation for multi-class classification.
Performance evaluation on a held-out test set demonstrated a
remarkable accuracy of approximately 97% in classifying tumor types.
Furthermore, we analyzed the model’s performance using precision, recall,
F1-score, and a confusion matrix to provide a comprehensive assessment
of its diagnostic capabilities. This research highlights the potential
of transfer learning with EfficientNetB0 for developing accurate
and efficient automated brain tumor classification systems. Our findings
suggest that this approach can contribute significantly to improved diagnostic
support, potentially reducing the need for invasive procedures
and facilitating timely treatment interventions.
Citation

M. YOUCEF Brik, (2024-12-03), "Automated Brain Tumor Classification using a Fine-Tuned EfficientNet Model", [national] The first National Conference on Artificial Intelligence and its Applicat1ons , Constantine 1

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2024-12-01

MRI-based brain tumor ensemble classification using two stage score level fusion and CNN models

This paper proposes a novel two-stage approach to improve brain tumor classification accuracy using the Br35H MRI Scan Dataset. The first stage employs advanced image enhancement algorithms, GFPGAN and Real-ESRGAN, to enhance the image dataset’s quality, sharpness, and resolution. Nine deep learning models are then trained and tested on the enhanced dataset, experimenting with five optimizers. In the second stage, ensemble learning algorithms like weighted sum, fuzzy rank, and majority vote are used to combine the scores from the trained models, enhancing prediction results. The top 2, 3, 4, and 5 classifiers are selected for ensemble learning at each rating level. The system’s performance is evaluated using accuracy, recall, precision, and F1-score. It achieves 100% accuracy when using the GFPGAN-enhanced dataset and combining the top 5 classifiers through ensemble learning, outperforming current methodologies in brain tumor classification. These compelling results underscore the potential of our approach in providing highly accurate and effective brain tumor classification.
Citation

M. YOUCEF Brik, (2024-12-01), "MRI-based brain tumor ensemble classification using two stage score level fusion and CNN models", [national] Egyptian Informatics Journal , ELSEVIER

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2024-11-18

A Robust Convolutional Neural Network for Iris Recognition system

Iris recognition, a biometric modality, has grown significantly in recent years. Traditional techniques relied on feature extraction methods and classical machine learning classifiers. However, deep learning models, particularly Convolutional Neural Networks (CNNs), have exhibited remarkable performance in learning discriminative features from iris images, making them robust to variations in imaging conditions and achieving state-of-the-art recognition accuracy. Nonetheless, injuries or occlusions affecting the iris can lead to increased error rates due to missing information. This study presents and evaluates CNN-based models for iris recognition. Various CNN architectures were employed for feature extraction, and the best-performing model was selected. Features from the left and right iris (one instance) were fused to create a multiple-instance system, enhancing recognition performance. The proposed approach was tested using the SDUMLA-HMT iris dataset. The results demonstrate that our system achieves an accuracy of 100%. Comparative analysis with existing methods indicates that our system outperforms the current state-of-the-art techniques for iris recognition.
Citation

M. YOUCEF Brik, (2024-11-18), "A Robust Convolutional Neural Network for Iris Recognition system", [national] National Conference of Applied Sciences and Engineering NCASE ’24 , AGIERS

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2024-11-17

An innovative CNN-SVM hybrid model for enhanced diabetic retinopathy detection

Diabetic retinopathy (DR) presents a significant threat to global visual health. This study introduces a method leveraging deep learning and machine learning to enhance DR detection accuracy. By combining pre-trained CNNs (MobileNetV2, ResNet50, and Xception) with high performance classifiers like SVM and KNN, our method significantly improves diagnostic performance. The combination of ResNet50 and SVM achieved a 95.90% accuracy in detecting retinal abnormalities for the APTOS 2019 Blindness Detection dataset, demonstrating its superiority over current diagnostic techniques.
Citation

M. YOUCEF Brik, (2024-11-17), "An innovative CNN-SVM hybrid model for enhanced diabetic retinopathy detection", [national] Inductics conference , M'SILA

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2024-09-01

Gabor, LBP, and BSIF features: Which is more appropriate for finger-knuckles-print recognition?

An accurate personal identification system helps control access to secure information and data. Biometric technology mainly focuses on the physiological or behavioural characteristics of the human body. This paper investigates the Finger Knuckle Print (FKP) biometric device based on the feature extraction technique. This FKP authentication method includes all the essential processes, such as preprocessing, feature extraction and classification. The features of the FKP application are investigated. Finally, this paper proposes the selection of the best feature extraction based on FKP recognition efficiency. The primary purpose of this paper is to use the Local Binary Patterns (LBP), Binarized Statistical Image Features (BSIF), and Gabor filters and define which helps to increase the False Acceptability Rate (FAR) and Genuine Acceptability Rate (GAR). This latest FKP selection shows better results as this concept shows promising results in recognizing a person's fingerknuckle print.
Citation

M. YOUCEF Brik, (2024-09-01), "Gabor, LBP, and BSIF features: Which is more appropriate for finger-knuckles-print recognition?", [national] Przegląd Elektrotechniczny , Wydawnictwo SIGMA

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2024-08-14

A sequential combination of convolution neural network and machine learning for finger vein recognition system

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

M. YOUCEF Brik, (2024-08-14), "A sequential combination of convolution neural network and machine learning for finger vein recognition system", [national] Signal, Image and Video Processing , SPRINGER

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2024-06-27

Two-stage deep learning classification for diabetic retinopathy using gradient weighted class activation mapping

The fundus images of patients with Diabetic Retinopathy (DR) often display numerous lesions scattered across the retina. Current methods typically utilize the entire image for network learning, which has limitations since DR abnormalities are usually localized. Training Convolutional Neural Networks (CNNs) on global images can be challenging due to excessive noise. Therefore, it's crucial to enhance the visibility of important regions and focus the recognition system on them to improve accuracy. This study investigates the task of classifying the severity of diabetic retinopathy in eye fundus images by employing appropriate preprocessing techniques to enhance image quality. We propose a novel two-branch attention-guided convolutional neural network (AG-CNN) with initial image preprocessing to address these issues. The AG-CNN initially establishes overall attention to the entire image with the global branch and then incorporates a local branch to compensate for any lost discriminative cues. We conduct extensive experiments using the APTOS 2019 DR dataset. Our baseline model, DenseNet-121, achieves average accuracy/AUC values of 0.9746/0.995, respectively. Upon integrating the local branch, the AG-CNN improves the average accuracy/AUC to 0.9848/0.998, representing a significant advancement in state-of-the-art performance within the field.
Citation

M. YOUCEF Brik, (2024-06-27), "Two-stage deep learning classification for diabetic retinopathy using gradient weighted class activation mapping", [national] Automatika: Journal for Control, Measurement, Electronics, Computing and Communications , Taylor & Francis

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2024-06-16

Enhancing Diabetic Retinopathy Detection Using a Hybrid Framework Integrating Machine Learning Classifiers and Advanced Feature Extraction Techniques

Diabetic retinopathy (DR) is recognized as a significant threat to global visual health among
diabetes patients worldwide, necessitating an urgent need for efficient detection methods. Manual
methods of diagnosis have limitations, resulting in delayed diagnosis, especially in areas with limited
access to healthcare services. Deep learning techniques such as convolutional neural networks (CNNs),
provide promising performance in various medical applications. This study proposes an innovative
framework that leverages deep learning and machine learning techniques to enhance the diagnostic
accuracy and detection efficiency of DR. The framework combines pre-trained CNNs with robust
machine learning classifiers to enhance prediction capabilities, incorporating Support Vector Machines
(SVMs) and K-Nearest Neighbor (KNN), stacking their predictions for improved performance.
Additionally, three CNN models—MobileNetV2, ResNet50, and Xception—are employed to extract
pertinent features from retinal images within the DR 224x224 Gaussian Filtered dataset. The combination
of ResNet50 and SVM achieved the highest accuracy rate of 95.22% in detecting retinal abnormalities
within DR images. Comprehensive validation of our framework highlights its superiority over current
diagnostic techniques. By Integrating advanced deep learning architectures with machine learning
classifiers, this study marks a significant advancement in the automated detection of Diabetic
Retinopathy, potentially enhancing healthcare outcomes for diabetic patients globally.
Citation

M. YOUCEF Brik, (2024-06-16), "Enhancing Diabetic Retinopathy Detection Using a Hybrid Framework Integrating Machine Learning Classifiers and Advanced Feature Extraction Techniques", [international] 3rd International Conference on Frontiers in Academic Research , KONYA

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Detection of Diabetic Retinopathy Using a Pre-trained Inception-v3 Model

Diabetic retinopathy (DR) is a significant eye-related disorder caused by accumulated damage
to small retinal blood vessels, often leading to severe visual impairment. Early detection and accurate
diagnosis of DR are crucial for preventing disease progression and ensuring timely treatment. Automated
diagnostic systems, especially those based on deep learning techniques, have shown great promise in
aiding ophthalmologists in this regard. This work proposes an automated DR detection system using a
pre-trained Inception-v3 deep neural network model. The model is fine-tuned to classify retinal fundus
images from the Aptos 2019 dataset into two categories: "With DR" and "No DR." By leveraging the
powerful feature extraction capabilities of the Inception-v3 model, our system effectively identifies small
lesions in retinal images that are indicative of DR. The model's performance is evaluated based on
Precision, Recall, F1-score, and AUC. The Confusion Matrix and ROC curves are also used to visualize
and further validate the model's performance. Our proposed system achieves an accuracy of 98.36%,
demonstrating its efficacy in DR detection. The system's capability is further validated by comparing it
with other established convolutional neural network models, highlighting its superior performance. These
results underscore the potential of our Inception-v3-based approach to provide efficient and accurate DR
diagnosis, supporting ophthalmologists in the early detection and management of diabetic retinopathy.
Citation

M. YOUCEF Brik, (2024-06-16), "Detection of Diabetic Retinopathy Using a Pre-trained Inception-v3 Model", [international] 3rd International Conference on Frontiers in Academic Research , KONYA

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2024-06-15

Combining CNN with hand-crafted features for Diabetic retinopathy classification

Diabetes or diabetes mellitus (DM) is a worldwide issue of pandemic scale. The effect or
illness of this disease on the human eye is called diabetic retinopathy (DR) which can be diagnosed from
the images of the eye fundus. The fundus images of patients with Diabetic Retinopathy (DR) often
display numerous lesions scattered across the retina. This study investigates the task of classifying the
presence of diabetic retinopathy in eye fundus images by first, employing appropriate preprocessing
techniques to enhance image quality such as CLAHE and LoG techniques. Then, we propose a novel
fusion system that uses both: features extracted using Convolutional neural networks (densenet-121), and
hand-crafted texture features (such as LBP, LPQ and BSIF). These two types of features are then used as
the input of a machine learning system (using SVM, KNN, MLP) that has the task of classifying the
presence of DR in the fundus images (Binary classification).
After much extensive tests, we found that our system has great performance both in calculation time and
accuracy, the best accuracy results was found using SVM as the ML technique (using Poly kernel and the
parameters C=100) and LBP as the hand-crafted feature extracted with the accuracy of 94.32%,
representing a significant advancement in state-of-the-art performance within the field.
Citation

M. YOUCEF Brik, (2024-06-15), "Combining CNN with hand-crafted features for Diabetic retinopathy classification", [international] 3rd International Conference on Frontiers in Academic Research , KONYA

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Deep neural networks for an accurate palmprint recognition system across multiple instances

These days, there is increasing in crime, piracy, and security issues across different sectors,
highlighting the necessity for reliable identity recognition methods. Traditional security systems, which
rely on pre-existing information or token-based access, often struggle to distinguish between authorized
individuals and fraudsters. In this research, we investigate deep-learning palmprint recognition systems as
a promising alternative. Palmprint modality offers several advantages, including uniqueness, security, ease
of use, and non-intrusiveness. Our approach explores fine-tuning for feature extraction and two strategies:
learning from single and multiple instances. The proposed system is divided into two parts. First, we employ
three convolutional neural network models (VGG16, VGG19, and MobileNetV2) for feature extraction and
classification. Based on initial experiment results, we select the best-performing models and apply a feature
fusion technique to concatenate the features extracted from the left and right palmprint instances, thus
creating our multiple-instance system. Experimental results using the PolyU palmprint database
demonstrate the effectiveness of our method. With single instance learning, we achieved accuracies of 95%
and 96% (VGG16), 93% and 94% (VGG19), and 97.50% and 96% (MobileNetV2). Using multiple
instances, we achieved an accuracy of 99,50%. Our proposed work exhibits good performance compared
to existing methods, particularly in multi-instance scenarios. This highlights the potential of deep-learning
palmprint recognition systems to address identification challenges across various sectors, especially in
cases of physical anomalies such as injuries, disfigurements, or scars.
Citation

M. YOUCEF Brik, (2024-06-15), "Deep neural networks for an accurate palmprint recognition system across multiple instances", [international] 3rd International Conference on Frontiers in Academic Research , KONYA

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2024-05-14

Boosting the Performance of Deep Ear Recognition Systems Using Generative Adversarial Networks and Mean Class Activation Maps

Ear recognition is a complex research domain within biometrics, aiming to identify individuals using their ears in uncontrolled conditions. Despite the exceptional performance of convolutional neural networks (CNNs) in various applications, the efficacy of deep ear recognition systems is nascent. This paper proposes a two-step ear recognition approach. The initial step employs deep convolutional generative adversarial networks (DCGANs) to enhance ear images. This involves the colorization of grayscale images and the enhancement of dark shades, addressing visual imperfections. Subsequently, a feature extraction and classification technique, referred to as Mean-CAM-CNN, is introduced. This technique leverages mean-class activation maps in conjunction with CNNs. The Mean-CAM approach directs the CNN to focus specifically on relevant information, extracting and assessing only significant regions within the entire image. The process involves the implementation of a mask to selectively crop the pertinent area of the image. The cropped region is then utilized to train a CNN for discriminative classification. Extensive evaluations were conducted using two ear recognition datasets: mathematical analysis of images (MAI) and annotated web ears (AWEs). The experimental results indicate that the proposed approach shows notable improvements and competitive performance: the Rank-1 recognition rates are 100.00% and 76.25% for MAI and AWE datasets, respectively.
Citation

M. YOUCEF Brik, (2024-05-14), "Boosting the Performance of Deep Ear Recognition Systems Using Generative Adversarial Networks and Mean Class Activation Maps", [national] Applied Sciences , MDPI

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2023-12-05

Ensemble Learning vs. Convolutional Neural Networks for Multiclass Brain Tumor Classification of MRI Images

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

M. YOUCEF Brik, (2023-12-05), "Ensemble Learning vs. Convolutional Neural Networks for Multiclass Brain Tumor Classification of MRI Images", [international] International Conference on Science, Technology, Engineering and Management , Istanbul

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2023-12-03

Helping System for Brain Disease Diagnosis Using Deep Learning

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

M. YOUCEF Brik, (2023-12-03), "Helping System for Brain Disease Diagnosis Using Deep Learning", [international] International Conference on Science, Technology, Engineering and Management , Istanbul

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2023-11-19

Un bracelet électronique de surveillance pour les patients atteints de la maladie d'Alzheimer

Brevet
Citation

M. YOUCEF Brik, (2023-11-19), "Un bracelet électronique de surveillance pour les patients atteints de la maladie d'Alzheimer", [national] University of M'sila

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Dispositif d'amélioration de la force du signal WiFI

Brevet
Citation

M. YOUCEF Brik, (2023-11-19), "Dispositif d'amélioration de la force du signal WiFI", [national] University of M'sila

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2023-10-23

Deep Learning for Biomedical and Biometrics Applications

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

M. YOUCEF Brik, (2023-10-23), "Deep Learning for Biomedical and Biometrics Applications", [international] CAIRO Talk series , Kuala Lampur

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2023-07-25

EYEDRIVE

Label projet innovant pour créer une startup.
Citation

M. YOUCEF Brik, (2023-07-25), "EYEDRIVE", [national] University of M'sila

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2023-06-26

Dispositif de système de sécurité domestique

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Citation

M. YOUCEF Brik, (2023-06-26), "Dispositif de système de sécurité domestique", [national] University of M'sila

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2023-06-18

Artificial Intelligence powered medical image classification device

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Citation

M. YOUCEF Brik, (2023-06-18), "Artificial Intelligence powered medical image classification device", [national] University of M'sila

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Accès sécurisé : système d'authentification avancées bimodale

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Citation

M. YOUCEF Brik, (2023-06-18), "Accès sécurisé : système d'authentification avancées bimodale", [national] University of M'sila

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2023-03-01

Rétroviseur intérieur adapté aux véhicules pour la détection et la protection du conducteur somnolent et distrait.

Brevet
Citation

M. YOUCEF Brik, (2023-03-01), "Rétroviseur intérieur adapté aux véhicules pour la détection et la protection du conducteur somnolent et distrait.", [national] University of M'sila

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2023-02-01

Social media-based COVID-19 sentiment classification model using Bi-LSTM

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

M. YOUCEF Brik, Arbane Mohamed, Rachid Benlamri, Ayman Diyab Alahmar, , (2023-02-01), "Social media-based COVID-19 sentiment classification model using Bi-LSTM", [national] Expert Systems with Applications , Elsevier

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2023-01-01

A decision fusion method based on classification models for water quality monitoring

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

M. YOUCEF Brik, (2023-01-01), "A decision fusion method based on classification models for water quality monitoring", [national] Environmental Science and Pollution Research , Springer

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2022-11-26

Enhancement of diabetic retinopathy classification using attention guided convolution neural network

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

M. YOUCEF Brik, (2022-11-26), "Enhancement of diabetic retinopathy classification using attention guided convolution neural network", [international] ICATEEE2022 , M'sila, Algeria

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2022-07-24

Onduleur Monophasé Cascadé a Sept Niveaux

Réalisation d'un Onduleur Monophasé Cascadé a Sept Niveaux
Citation

M. YOUCEF Brik, (2022-07-24), "Onduleur Monophasé Cascadé a Sept Niveaux", [national] Université de M'sila

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2022

Performance Analysis ofTwin-SupportVector Machine in Breast Cancer Prediction

This paper deals with
Citation

M. YOUCEF Brik, (2022), "Performance Analysis ofTwin-SupportVector Machine in Breast Cancer Prediction", [international] ICATEEE2022 , Algeria

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Ear Recognition using Ensemble of Deep Features and Machine Learning Classifiers

Ear Recognition using Ensemble of Deep Features and Machine Learning Classifiers
Citation

M. YOUCEF Brik, (2022), "Ear Recognition using Ensemble of Deep Features and Machine Learning Classifiers", [international] ICCTA 2022 , Alexandria, Egypt

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

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

M. YOUCEF Brik, rafik.zouaoui@univ-msila.dz, , (2022), "Transfer learning approche for alzhimer's dicease diagnosis using mri image", [international] ICATEEE2022 , M'sila_Algeria

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Brain tumor classification based deep transfer learning

Brain tumor classification based deep transfer learning
Citation

M. YOUCEF Brik, oussama.bougeurra@univ-msail.dz, , (2022), "Brain tumor classification based deep transfer learning", [international] ICATEEE2022 , M'sila_Algeria

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Transfer learning for diabetic retinopathy detection

Transfer learning for diabetic retinopathy detection
Citation

M. YOUCEF Brik, (2022), "Transfer learning for diabetic retinopathy detection", [international] ICATEEE2022 , M'sila-Algeria

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Finger vein based cnn for human recognition

Finger vein based cnn for human recognition
Citation

M. YOUCEF Brik, (2022), "Finger vein based cnn for human recognition", [international] ICATEEE2022 , M'sila-Algeria

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Deep learning based framwork for automatic diabetic retinopathy detection

Deep learning based framwork for automatic diabetic retinopathy detection
Citation

M. YOUCEF Brik, (2022), "Deep learning based framwork for automatic diabetic retinopathy detection", [international] ICCTA 2022 , Alexendrie_Egypt

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2021-12-17

An efficient prediction system for diabetes disease based on deep neural network

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

M. YOUCEF Brik, (2021-12-17), "An efficient prediction system for diabetes disease based on deep neural network", [national] Complexity , Hindawi

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2021

Transfer learning for automatic brain tumor classification using MRI images

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

M. YOUCEF Brik, (2021), "Transfer learning for automatic brain tumor classification using MRI images", [international] 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH) , Boumerdes, Algeria

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Efficient heart disease diagnosis based on twin support vector machine

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

M. YOUCEF Brik, (2021), "Efficient heart disease diagnosis based on twin support vector machine", [national] DIAGNOSTYKA , PTDT

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2019

Heart disease prediction using neighborhood component analysis and support vector machines

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

M. YOUCEF Brik, Youssef Chahir, , (2019), "Heart disease prediction using neighborhood component analysis and support vector machines", [international] The VIIIth International Workshop on Representation, analysis and recognition of shape and motion FroM Imaging data (RFMI 2019) , Hal , Tunisia

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Finger-knuckle-print, Plamprint and Fingerprint for Multimodal Recognition System Based on mRMR features selection

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

M. YOUCEF Brik, Youssef Chahir, , (2019), "Finger-knuckle-print, Plamprint and Fingerprint for Multimodal Recognition System Based on mRMR features selection", [international] IC2MAS19 , Istanbul-Turkey

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Fusing Palmprint, Finger-knuckle-print for Bi-modal Recognition System Based on LBP and BSIF

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

M. YOUCEF Brik, (2019), "Fusing Palmprint, Finger-knuckle-print for Bi-modal Recognition System Based on LBP and BSIF", [international] International Conference on Image and Signal Processing and their Applications , Mostaganem, Algeria, Algeria

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Neighborhood Component Analysis and Support Vector Machines for Heart Disease Prediction

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

M. YOUCEF Brik, (2019), "Neighborhood Component Analysis and Support Vector Machines for Heart Disease Prediction", [international] Ingénierie des Systèmes d’Information (ISI) , International Information and Engineering Technology Association (IIETA)

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2018

Mental model for handwritten keyword spotting

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

M. YOUCEF Brik, Djemel Ziou, , (2018), "Mental model for handwritten keyword spotting", [national] Journal of Electronic Imaging , SPIE Digital Library (USA)

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2012

Réalisation d’un onduleur monophasé cascadé à sept niveaux piloté par le PIC16F876

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

M. YOUCEF Brik, (2012), "Réalisation d’un onduleur monophasé cascadé à sept niveaux piloté par le PIC16F876", [international] Deuxième Conférence Internationale sur la Maintenance, la Gestion, la Logistique et l’Electrotechnique , Oran, Algeria

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