Publications Scientifiques
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Item Machine learning algorithms prediction of methyl orange removal by Fenton oxidation process(Springer Science and Business Media, 2025) Ouazene, Naima; Harrar, Khaled; Gharbi, Amine; Zahi, Salah Eddine; Mokrane, Said; Mokrane, HindFenton oxidation, an advanced oxidation process (AOP), effectively mineralizes azo dyes, mitigating their environmental impact. The Fenton oxidation process (Fe2⁺/H₂O₂) was employed for the degradation of methyl orange (MO) under varying operational conditions, with its efficiency assessed through chemical oxygen demand (COD) analysis. This study aims to develop predictive models for MO degradation efficiency using four machine learning (ML) algorithms: Gaussian process regression (GPR), multilayer perceptron (MLP), decision tree (DT), and support vector regression (SVR). These models were developed and validated using 42 experimental data points obtained under controlled conditions. Experimental findings revealed a 99% COD removal at an initial MO concentration of 125 mg/L, optimized at pH 3.5, [Fe2⁺] = 25 mg/L, reaction time = 90 min, and a molar ratio of [H₂O₂]/[MO] = 42.5. The predictive accuracy of the ML models was evaluated using the coefficient of determination (R2) and root mean square error (RMSE). The GPR model demonstrated the highest predictive performance (R2 = 0.970), followed by DT (R2 = 0.964). The MLP and SVM models exhibited slightly lower predictive capacities, with R2 values of 0.946 and 0.910, respectively. Feature importance analysis indicated that reaction time was the most significant parameter influencing COD removal, underscoring the necessity of its optimization in practical applications. The integration of ML-based predictive modeling with AOPs provides a robust approach for enhancing wastewater treatment efficiency. The outcomes of this study hold particular relevance for water reuse applications in arid and semiarid regions, where effective pollutant removal is critical for sustainable water resource managementItem Detection of knee osteoarthritis based on wavelet and random forest model(2021) Messaoudene, Khadidja; Harrar, KhaledThe most recurrent kind of osteoarthritis is Knee osteoarthritis (KOA). Doctors encounter difficulties for a precise diagnosis through its features and to the naked eye. In this paper, we propose a new approach for the classification of KOA by combining the discrete wavelet decomposition (DWT) and random forest classifier from knee X-ray images. A total of 50 images from patients suffering or not from osteoarthritis were used in this study. The suggested technique includes image enhancement using the Gaussian filter followed by Haar wavelet transform. Five texture features namely, contrast, entropy, correlation, energy, and homogeneity were extracted from the transformed image, and these attributes were used to differentiate the radiographs into two groups: normal (KL 0) or affected with osteoarthritis (KL2). Four classifiers including random forest, SVM, RNN, and Naïve Bayes were tested and compared. The results obtained reveal that random forest achieved the highest performance in terms of accuracy (ACC = 88%) on X-Ray images of the Osteoarthritis Initiative (OAI) dataset.Item Recent progress on field-effect transistor-based biosensors: device perspective(Beilstein-Institut Zur Forderung der Chemischen Wissenschaften, 2024) Smaani, Billel; Nafa, Fares; Benlatrech, Mohamed Salah; Mahdi, Ismahan; Akroum, Hamza; Azizi, Mohamed walid; Harrar, Khaled; Kanungo, SayanOver the last few decades, field-effect transistor (FET)-based biosensors have demonstrated great potential across various industries, including medical, food, agriculture, environmental, and military sectors. These biosensors leverage the electrical properties of transistors to detect a wide range of biomolecules, such as proteins, DNA, and antibodies. This article presents a comprehensive review of advancements in the architectures of FET-based biosensors aiming to enhance device performance in terms of sensitivity, detection time, and selectivity. The review encompasses an overview of emerging FET-based biosensors and useful guidelines to reach the best device dimensions, favorable design, and realization of FET-based biosensors. Consequently, it furnishes researchers with a detailed perspective on design considerations and applications for future generations of FET-based biosensors. Finally, this article proposes intriguing avenues for further research on the topology of FET-based biosensors.Item Computerized diagnosis of knee osteoarthritis from x-ray images using combined texture features: Data from the osteoarthritis initiative(Wiley-Blackwell, 2024) Messaoudene, Khadidja; Harrar, KhaledThe prevalence of knee osteoarthritis (KOA) cases has witnessed a significant increase on a global scale in recent years, emphasizing the need for automated diagnostic computer systems to aid in early-stage osteoarthritis (OA) diagnosis. The accurate characterization of knee KOA stages through feature extraction poses significant research challenges due to the complexity of identifying relevant attributes. In this study, the development of a KOA diagnostic system is presented, leveraging a combination of Gabor, and Tamura parameters using the Canonical Correlation Analysis algorithm. Two feature selection algorithms, namely Principal Component Analysis and Relief, were employed for KOA classification. Furthermore, various classifiers, including K-Nearest Neighbors, AdaBoost, Bagging, and Random Forest, were used to assess the proposed feature extraction approach. The diagnostic system was assessed using a dataset comprising 688 x-ray images sourced from the OA initiative (OAI) dataset, consisting of 344 images from healthy subjects (Grade 0) and 344 images from pathological patients (Grade 2). To mitigate overfitting, a 10-fold cross-validation method was utilized. The experimental results indicate that the combination of Tamura and Gabor parameters with the Random Forest classifier achieved remarkable performance in KOA diagnosis, yielding an accuracy of 94.59%, and an area under the curve of 98.3%. Notably, the combined Gabor and Tamura models exhibited superior performance compared to individual models, as well as existing techniques reported in the literature.Item A comparative study between convolutional and multilayer perceptron neural networks classification models(2019) Bachiri, Mohamed Elssaleh; Harrar, KhaledImage classification plays an important role in image processing, computer vision, and machine learning. This paper deals with image classification using deep learning. For this, a conventional neural network (CNN) and multilayer perceptron neural network (MLP) models were used for the classification. The two models were implemented on the MNIST dataset which was used at 100% and half of capacity, The models were trained with fixed and flexible number of epochs in two runs. CNN provided an accuracy of 98,43% with a loss of 4,44%, where MLP reached 92,80% of classification with a loss of 25,87%. Indeed, for each model, variables as number of filters, size, and activation functions were discussed. The CNN demonstrated a good performance providing high accuracy for image and also proved to be a better candidate for data applications.Item Subchondral tibial bone texture of conventional X-rays predicts total knee arthroplasty(Nature Research, 2022) Almhdie-Imjabbar, Ahmad; Toumi, Hechmi; Harrar, Khaled; Pinti, Antonio; Lespessailles, EricLacking disease-modifying osteoarthritis drugs (DMOADs) for knee osteoarthritis (KOA), Total Knee Arthroplasty (TKA) is often considered an important clinical outcome. Thus, it is important to determine the most relevant factors that are associated with the risk of TKA. The present study aims to develop a model based on a combination of X-ray trabecular bone texture (TBT) analysis, and clinical and radiological information to predict TKA risk in patients with or at risk of developing KOA. This study involved 4382 radiographs, obtained from the OsteoArthritis Initiative (OAI) cohort. Cases were defined as patients with TKA on at least one knee prior to the 108-month follow-up time point and controls were defined as patients who had never undergone TKA. The proposed TKA-risk prediction model, combining TBT parameters and Kellgren–Lawrence (KL) grades, was performed using logistic regression. The proposed model achieved an AUC of 0.92 (95% Confidence Interval [CI] 0.90, 0.93), while the KL model achieved an AUC of 0.86 (95% CI 0.84, 0.86; p < 0.001). This study presents a new TKA prediction model with a good performance permitting the identification of at risk patient with a good sensitivy and specificity, with a 60% increase in TKA case prediction as reflected by the recall valuesItem A hybrid LBP-HOG model and naive Bayes classifier for knee osteoarthritis detection: data from the osteoarthritis initiative(Springer, 2022) Messaoudene, Khadidja; Harrar, KhaledKnee OsteoArthritis (KOA) is a disease characterized by a degeneration of cartilage and the underlying bone. It does not evolve uniformly; it can stay silent for a long time and can quickly intensify for several months or weeks. For this reason, it is necessary to develop an automatic system for diagnosis and reduce the subjectivity in the detection of the disease. In this paper, we present a method for detecting knee osteoarthritis based on the combination of histograms of oriented gradient (HOG) and local binary pattern (LBP). Four classifiers including KNN, SVM, Adaboost, and Naïve Bayes were tested and compared for the prediction of the illness. A total of 620 X-Ray images were analyzed, composed of 310 images from healthy subjects (Grade 0), and 310 images from pathological patients (Grade 2). The results obtained reveal that Naïve Bayes achieved the highest performance in terms of accuracy (ACC = 91%) on the Osteoarthritis Initiative (OAI) dataset. The fusion of HOG and LBP features in KOA classification outperforms the use of either feature alone and the existing methods in the literatureItem Feature extraction using CNN for peripheral blood cells recognition(European Alliance for Innovation, 2022) Ammar, Mohammed; Daho, Mostafa El Habib; Harrar, Khaled; Laidi, AmelINTRODUCTION: The diagnosis of hematological diseases is based on the morphological differentiation of the peripheral blood cell types. OBJECTIVES: In this work, a hybrid model based on CNN features extraction and machine learning classifiers were proposed to improve peripheral blood cell image classification. METHODS: At first, a CNN model composed of four convolution layers and three fully connected layers was proposed. Second, the features from the deeper layers of the CNN classifier were extracted. Third, several models were trained and tested on the data. Moreover, a combination of CNN with traditional machine learning classifiers was carried out. This includes CNN_KNN, CNN_SVM (Linear), CNN_SVM (RBF), and CNN_AdaboostM1. The proposed methods were validated on two datasets. We have used a public dataset containing 12444 images with four types of leukocytes to find the best optimizer function(eosinophil, lymphocyte, monocyte, and neutrophil images). The second dataset contains 17,092 images divided into eight groups: lymphocytes, neutrophils, monocytes). the second public dataset was used to find the best combination of CNN and the machine learning algorithms. the dataset containing 17,092 images: lymphocytes, neutrophils, monocytes, eosinophils, basophils, immature granulocytes, erythroblasts, and platelets. RESULTS: The results reveal that CNN combined with AdaBoost decision tree classifier provided the best performance in terms of cells recognition with an accuracy of 88.8%, demonstrating the performance of the proposed approach. CONCLUSION: The obtained results show that the proposed system can be used in clinical practiceItem Combining GLCM with LBP features for knee osteoarthritis prediction: data from the osteoarthritis initiative(EAI, 2021) Harrar, Khaled; Messaoudene, Khadidja; Ammar, MohammedKnee osteoarthritis is a chronic disease that can make a person more susceptible to develop health complications. It is a significant cause of disability among adults. In advanced stages, people can die from these complications. OBJECTIVES: This paper introduces a quick and effective approach to classify knee X-ray images using LogitBoost and wavelet-based Gray Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP) to increase image classification accuracy and minimize training and testing time. METHODS: The proposed technique involves image enhancement followed by Haar wavelet transformation. GLCM and LBP were extracted from the transformed image and these attributes were used to differentiate the radiographs into two groups of patients composed of 100 normal subjects (KL 0) and 100 pathological cases with osteoarthritis (KL 2). The validation of the classification was carried out using the K-fold cross-validation technique with k = 10. RESULTS: The results revealed that the GLCM provided an accuracy of 77 % and the LBP approach achieved an accuracy of 82.5 %. Moreover, the combination of the two techniques LBP-GLCM improved the accuracy of the prediction with the LogitBoost model (91.16 %). Compared to other classifiers (SVM, logistic regression, and decision tree), the LogitBoost provided a low root mean square error (RMSE) of 27.5 %. CONCLUSION: In addition, the proposed method was compared to the state-of-the-art and revealed the highest accuracy in the prediction of KOA, outperforming the methods existing in the literatureItem Classification of surface defects on steel strip images using convolution neural network and support vector machine(Springer, 2022) Boudiaf, Adel; Benlahmidi, Said; Harrar, Khaled; Zaghdoudi, RachidQuality control of the surfaces of rolled products has received wide attention due to the crucial role that these products play in the manufacture of various car bodies, planes, ships, and trains. The process of quality control has undergone remarkable development. Previously, it was based on the human eye and characterized by slowness, fatigue, and error. To overcome these problems, nowadays the quality control is based mainly on computer vision. In this context, we propose in this work to develop an intelligent recognition system of surface defects for hot-rolled steel strips images using modified AlexNet convolution neural network and support vector machine model. Furthermore, we conducted a study on the effect of layers selection on classification accuracy. We have trained and tested our classification model using a public database of Northeastern University composed of 1800 images of defects. The results showed that our classifier model can be used easily for effective screening of surface defects for hot-rolled steel strips with very a high classification accuracy up to 99.7%, using only 7% of the total extracted features for each image with activations on the fully connected layer “FC7.” In addition, we addressed through this research a comparative study between the proposed classification model and the well-known modern classification models. This study highlighted the efficiency and effectiveness of our proposed model for the classification of surface defects