KADRI Ouahab
@univ-batna2.dz
Department of computer science
University of Batna 2
Ouahab Kadri received, his Habilitation from the Department of Industrial Engineering, University of Batna, Algeria, in 2018. He received, his PhD from the Department of Industrial Engineering, University of Batna, in 2013. He is currently an Assistant Professor in the Department of Computer Science at the University of Batna2, Algeria. He was an Assistant Professor in the Department of Mathematics and Computer Science at the University of Khenchela, Algeria. He received his Magister degree from Department of Computer Science, University of Batna, Algeria. He has published five books and over 20 papers. His current research interests include evolutionary computation and artificial intelligence.
EDUCATION
Habilitation en génie industriel au Laboratoire d’Automatique et Productique à l’université de BATNA
RESEARCH INTERESTS
Support Vector Machine
Ant Colony Optimization
Evolutionary Computation
Optimization
Computational Intelligence
Applied Artificial Intelligence
Neural Networks and Artificial Intelligence
Classification
Feature Extraction
Feature Selection
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Mohamed Elamine Benrabah, Ouahab Kadri, Kinza Nadia Mouss, and Abdelghani Lakhdari
International Information and Engineering Technology Association
Starting from a worrying observation, that companies have difficulties controlling the anomalies of their manufacturing processes, in order to have a better control over them, we have realized a case study on the practical data of the Fertial Complex to analyze the main parameters of the ammonia neutralization by nitric acid process. This article proposes a precise diagnostic of this process to detect dysfunction problems affecting the final product. We start with a general diagnosis of the process using the SPC method, this approach is considered an excellent way to monitor and improve the product quality and provides very useful observations that allowed us to detect the parameters that suffer from problems affecting the quality. After the discovery of the parameters incapable to produce the quality required by the standards, we applies two machine learning technologies dedicated to the type of data of these parameters for detected the anomaly, the first technique called The kernel connectivity-based outlier factor (COF) algorithm consists in recording for each object the degree of being an outlier, the second technique called the Isolation Forest, its principle is to establish a forest to facilitate the calculation and description. The results obtained were compared in order to choose which is the best algorithm to monitor and detect the problems of these parameters, we find that the COF method is more efficient than the isolation forest which leads us to rely on this technology in this kind of process in order to avoid passing a bad quality to the customer in future.
Ouahab Kadri, Abderrezak Benyahia, and Adel Abdelhadi
IGI Global
Many cloud providers offer very high precision services to exploit Optical Character Recognition (OCR). However, there is no provider offers Tifinagh Optical Character Recognition (OCR) as Web Services. Several works have been proposed to build powerful Tifinagh OCR. Unfortunately, there is no one developed as a Web Service. In this paper, we present a new architecture of Tifinagh Handwriting Recognition as a web service based on a deep learning model via Google Colab. For the implementation of our proposal, we used the new version of the TensorFlow library and a very large database of Tifinagh characters composed of 60,000 images from the Mohammed Vth University in Rabat. Experimental results show that the TensorFlow library based on a Tensor processing unit constitutes a very promising framework for developing fast and very precise Tifinagh OCR web services. The results show that our method based on convolutional neural network outperforms existing methods based on support vector machines and extreme learning machine.
Oussama Hadji, Ouahab Kadri, Moufida Maimour, Eric Rondeau, and Abderrezak Benyahia
IEEE
Wetlands or humid zones are one of the most vital areas where many species of birds that maintain the balance of ecological systems. Due to global warming and change climate, rare species are threatened with extinction. It is important to preserve track and monitor these species. Most wild life monitoring systems are expensive or sub-optimal in terms of performance, deployment and network overloading. In this paper, we explore the possibility of using image processing techniques to reduce the large amount of data transmitted in traditional audio/video streaming monitoring systems. We used the region of interest technique to convey only the occurrence of a moving object. Feature extraction and matching techniques are used to deal with the redundancy and counting problem. We believe that our results show the viability of employing and using these techniques to reduce the amount of data transmitted in wild life monitoring systems.
Mohamed Takieddine Seddik, Ouahab Kadri, Chakir Bouarouguene, and Houssem Brahimi
Instituto Politecnico Nacional/Centro de Investigacion en Computacion
Optical burst switching (OBS) has become one of the best and widely used optical networking techniques. It offers more efficient bandwidth usage than optical packet switching (OPS) and optical circuit switching (OCS). However, it undergoes more attacks than other techniques and the Classical security approach cannot solve its security problem. Therefore, a new security approach based on machine learning and cloud computing is proposed in this article. We used the Google Colab platform to apply Support Vector Machine (SVM) and Extreme Learning Machine (ELM)to Burst Header Packet (BHP) flooding attack on Optical Burst Switching (OBS) Network Data Set.
Tarek Berghout, Leïla-Hayet Mouss, Ouahab Kadri, Lotfi Saïdi, and Mohamed Benbouzid
Elsevier BV
Tarek BERGHOUT, Leila Hayet MOUSS, Ouahab KADRI, and Nadjiha HADJIDJ
IEEE
the main objective of this works is to study and improve the performances of the Single hidden Layer Feedforward Neural network (SLFN) for the application of Remaining Useful Life (RUL) prediction of aircraft engines. The most common problems in SLFNs based old training algorithms such as backpropagation are time consuming, over-fitting and the appropriate network architecture identification. In this paper a new incremental constructive learning algorithm based on Extreme Learning Machine algorithm is proposed for founding the appropriate architecture of a neural network under less computational costs. The aim of the proposed training approach is to study its maximum capabilities during RUL prediction by reducing over-fitting and human intervention. The performances of the proposed approach which are evaluated on C-MAPPS dataset and compared with its original variant from the literature. Experimental results proved that the new algorithm outperforms the old one in many metrics evaluations.
Tarek Berghout, Leïla-Hayet Mouss, Ouahab Kadri, Lotfi Saïdi, and Mohamed Benbouzid
MDPI AG
The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.
Ouahab Kadri, L.H. Mouss, and Adel Abdelhadi
Inderscience Publishers
Ouahab Kadri, Leila Hayet Mouss, and Mohamed Djamel Mouss
Springer Science and Business Media LLC