Publications Scientifiques

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Author: search.filters.author.Harrar, KhaledHas files: No

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    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, Hind
    Fenton 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 management
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    Computerized diagnosis of knee osteoarthritis from x-ray images using combined texture features: Data from the osteoarthritis initiative
    (Wiley-Blackwell, 2024) Messaoudene, Khadidja; Harrar, Khaled
    The 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.
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    A hybrid LBP-HOG model and naive Bayes classifier for knee osteoarthritis detection: data from the osteoarthritis initiative
    (Springer, 2022) Messaoudene, Khadidja; Harrar, Khaled
    Knee 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 literature
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    Cancellous bone structure assessment using a new trabecular connectivity
    (Elsevier, 2021) Harrar, Khaled
    Osteoporosis is a major public health problem; it is characterized by a loss in bone connectivity, which leads to a higher risk of fracture. The objective of this article is to develop a new connectivity parameter for bone microarchitecture characterization and osteoporosis assessment. The purpose is to discriminate 164 subjects composed of 82 healthy patients (HL) and 82 osteoporotic cases (OP). The new connectivity parameter involves several new topological features. The proposed method was compared to a traditional connectivity index, and the results reveal the superiority and the outperformance of the new parameter to discriminate the two groups of subjects with an accuracy (Acc) of 71.95 % and area under curve (AUC) of 80.03 %. Moreover, clinical parameters from patients were involved in this study, and five configurations were constructed, tested, and validated on the data using the k-fold cross-validation (CV) model with several values of k. Furthermore, support vector machine (SVM) was used and various kernels (i.e., linear, quadratic, cubic, and RBF functions) were tested in this study. The objective is to look for the configuration providing the best performance in terms of separation between the two populations. Furthermore, several classifiers (logistic regression, k-nearest neighbors, boosted trees, and naïve Bayes) were tested and a combination of these classifiers was carried out using the stacking ensemble technique to improve the accuracy of the final prediction. Moreover, several studies of state-of-the-art were compared to the proposed method. The results obtained reveal that the 10-fold CV approach combining the new trabecular connectivity index and RBF function of SVM achieved the highest accuracy with Acc = 88.41 %, and AUC = 95.24 %. In addition, the proposed ensemble Meta classifier improved the accuracy of SVM and achieved a high rate with Acc = 95.12 % and AUC = 98.40 % outperforming the existing methods in the literature
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    A Fast and Efficient Approach for Image Compression Using Curvelet Transform
    (Springer, 2018) Inouri, Lynda; Azni, Mohamed; Khireddine, Abdelkrim; Harrar, Khaled; Tighidet, Soraya
    In this paper a novel image compression technique using features of wavelet and curvelet transforms is proposed to improve efficiency and compression performance. Indeed, the curvelet transform is one of the recently developed multiscale transforms which is especially designed to represent efficiently curves and edges in an image. In the proposed method, the compression algorithm involves the Haar wavelet transform to decompose the image into four frequency sub-bands. The lowest frequency sub-band coefficients are processed using Set Partitioning In Hierarchical Trees (SPIHT) encoding. Meanwhile, Fast Discrete Curvelet Transform (FDCT) is applied to the remaining frequency sub-bands. The FDCT output coefficients are then quantized according to the sub-band they belong to. The lowest frequency FDCT output coefficients are quantized using Differential Pulse Code Modulation, the medium frequency coefficients are processed using SPIHT, whereas the high frequency coefficients are removed. Experimental results demonstrate that our method provides high performance for edge detection compared to existing techniques particularly for images with abrupt changes. In addition, this new image coding and decoding approach is powerful in terms of computation time. Moreover, the proposed method reveals significant improvement in compression ratio and decoded peak-signal-to-noise-ratio.