Publications Scientifiques
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Item Fault prediction of pharmaceutical air compressor using the intelligent model based on the bayesian network(University of Zielona Gora, 2025) Amrani, Mohamed; Benazzouz, DjamelThis paper presents a new approach of diagnosis and prognostic in real-time of strategic equipment of pharmaceutical industry. This approach is developed using Bayesian network (BN) which consider industrial data and feedback experience. The objective is to detect, locate and prevent any malfunction of the air compressor (oil-free) without air contamination, dedicated to pharmaceutical industry. The study is based on the functional analysis of the air compressor to obtain the fault tree (FT). This FT is transformed into BN to diagnose automatically the compressor and prevent any malfunctioningItem 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 Formal Methods for Internet of Things: a Concise Classification(Université M’hamed Bougara de Boumerdes : Faculté des Sciences, 2024) Talamali, Ibtissem; Lounas, Razika; Mezghiche, MohamedItem Stability analysis of the pitch angle control of large wind turbines using different controller strategies(SAGE, 2022) Bouregba, Hicham; Hachemi, M.; Hachemi, M.; Hamidat, A .Reducing the environmental impact necessitates a boost in renewable energy conversion systems. Wind energy is regarded as one of the most essential energy sources. For this purpose, the high wind variations in the energy conver- sion chain require robust and reliable control. This research aims to implement a regulation based on artificial intelli- gence toward a blade orientation mechanism to improve the stability of energy conversion. On the other hand, an energy maximization technique called Maximum Power Point Tracking (MPPT) is integrated into the control system. A developed program in MATLAB estimates the turbine performance with two different strategies, namely the MPPT tech- nique and the Pitch control mechanism. For the best control and more stability of energy conversion, three artificial intelligence controllers, which are Neuronal Network (PI-ANN), Fuzzy Logic (PI-FLC), and Neuro-Fuzzy (PI-NFLC), were employed. They are compared with the conventional controller (PI-C). This comparison is made to distinguish the most robust regulator against wind speed variations. The different performance indices showed that the controller PI- NFLC has an excellent response, with an Integral Time Absolute Error (ITAE) of 375.28, whereas the Integral Absolute Error (IAE) and Integral Time Square Error (ITSE) equal 13.87 and 406.59, respectively.Item Progressive Deep Transfer Learning for Accurate Glaucoma Detection in Medical Imaging(Institute of Electrical and Electronics Engineers, 2024) Yakoub, Assia; Gaceb, Djamel; Touazi, Fayçal; Bourahla, NourelhoudaGlaucoma leads to permanent vision disability by damaging the optic nerve, which transmits visual images to the brain. The fact that glaucoma doesn't exhibit any symptoms as it progresses and can't be halted in later stages makes early diagnosis critical. Although various deep learning models have been applied to detect glaucoma from digital fundus images, the scarcity of labeled data has limited their generalization performance, along with their high computational complexity and specialized hardware requirements. In this study, a progressive transfer learning with preprocessing techniques is proposed for the early detection of glaucoma in fundus images. The performance of this approach is compared against transfer learning and convolutional neural networks using three benchmark datasets: Cataract, Glaucoma and Origa. The experimental results demonstrate that reusing pre-trained models from ImageNet and applying them to a database containing the same disease leads to improved performance, compared to using databases with different diseases in progressive transfer learning. Additionally, applying preprocessing techniques to the databases further enhances the results.Item Application of Deep Transfer Learning in Medical Imaging for Thyroid Lesion Diagnostic Assistance(Institute of Electrical and Electronics Engineers, 2024) Chaouchi, Lynda; Gaceb, Djamel; Touazi, Fayçal; Djani, Djouher; Yakoub, AssiaThis academic work evaluates and compares the performance of various deep convolutional neural network (DCNN) architectures in classifying thyroid nodules into two categories, malignant and benign, using ultrasound images. The dataset comprises 269 cases of benign lesions and 526 cases of malignant lesions. Given the limited dataset size, we employ a progressive learning approach with three established CNN models: VGG-16, ResNet-50, and EfficientNet. Initially pretrained on ImageNet, these models undergo further fine-tuning using a radiographic image dataset related to a different medical condition but similar to our domain. Different levels and fine-tuning strategies are applied to these models. A supervised softmax classifier is used for classifying lesions as malignant or benign, with the exception of the VGG-16 model. For the VGG-16 model, two additional classifiers, Support Vector Machine (SVM) and Random Forest (RF), are evaluated. The results obtained demonstrate the possibility of easily transitioning from the classification of one disease to another, even with a limited number of images, by leveraging the knowledge already acquired from another extensive database.Item Real-Time Monitoring and Diagnosis of Environmental Protection Systems by Artificial Neural Networks Case study: Pharmaceutical Isolator(ALJEST, 2023) Amrani, Mohammed: The main objective of this work is the study of risk analysis, in the field of pharmaceutical production. Some dangers can affect pharmaceutical companies’ personnel, as well as their internal and external environment, during the manufacturing process. Furthermore, the current regulations that governs this very sensitive field of manufacturing and the standards which are scrupulously very sharp. Also see the technical complexity of the industrial systems implemented. These three parameters constitute a real problem to be solved. To do this, we have developed an intelligent technique for monitoring these protection systems, in real time, in order to protect the personnel and the environment. This technique is mainly based on the use of an artificial neural network (ANN) which detects and localizes any anomalies that may occur at any time in the protection system. The experiment was carried out on an isolator belonging to BEKER Laboratories (a medecine manufacturing and development company in Dar El Beida-Algers). The test results allowed us to define the good and bad areas of the isolator operation. We concluded that, it is possible to define defaults in real time, using our new techniqueItem Improvement of system reliability in a natural gas processing facility by PSO and DE(Springer Nature, 2024) Saheb, Tafsouthe; Mellal, Mohamed ArezkiThe reliability of the systems as well as its optimization is the first concern of the designers. The elements of a given system can be either in series, parallel, parallel-series, or in a complex configuration. This paper addresses the reliability optimization of a natural gas processing facility. The reliability of this system is calculated and two redundancies strategies, active and standby, are optimized under the resource limits to improve reliability. Two bio-inspired optimization algorithms, namely the particle swarm optimization (PSO) and the differential evolution (DE), are implemented with penalty functions to find the optimal redundancy. The results obtained are compared.Item Legal english and law : a new challenge for the Algerian researcher(M'hamed bougara university of boumerdes: faculty of law and political sciences, 2023) Boulmerka, AminaItem ChatGPT backend: A comprehensive analysis(Institute of Electrical and Electronics, 2023) Belgacem, Ali; Bradai, Abbas; Beghdad-Bey, KaddaArtificial intelligence (AI) has transformed the field of natural language processing, enabling substantial advances in understanding, interpreting, and producing human language. The ability of AI to find new solutions to difficult linguistic expressions has led to the birth of sophisticated language models such as ChatGPT. This model uses cutting-edge deep learning algorithms to produce high-quality, human-like writing in response to natural language inputs. ChatGPT has an amazing capacity to recognize context, evaluate sentiment, and provide coherent and appropriate replies. It has a wide range of applications, from virtual assistants and customer support bots to language translation and content development. Therefore, understanding its backend has become essential. In this paper, we summarize the key principles underlying the operation of ChatGPT's back-end. This study is required reading for ChatGPT researchers because it covers critical aspects of the ChatGPT backend. It includes essential information for researchers looking to improve ChatGPT's performance or create new language models based on its architecture