KADRI Ouahab

@univ-batna2.dz

Department of computer science
University of Batna 2

https://researchid.co/ouahabk

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

284

Scholar Citations

Scholar h-index

Scholar i10-index

Scopus Publications

EcoWatch: Region of interest-based multi-quantization resource-efficient framework for migratory bird surveillance using wireless sensor networks and environmental context awareness
Oussama Hadji, Moufida Maimour, Abderezzak Benyahia, Ouahab Kadri, and Eric Rondeau
Elsevier BV

Optimizing Milk Pasteurization Diagnosis Through Deep Q-Networks and Digital Twin Technology
Ouahab Kadri and Adel Abdelhadi
IGI Global
Industrial diagnostic systems play an important role in food manufacturing by ensuring rapid detection of defective components and precise identification of systemic dysfunction. This article proposes a diagnostic model for the pasteurization process to enhance dairy production systems. The authors found that, when a breakdown occurs, the acquisition system stops providing necessary data for diagnostics. To solve this problem, the authors used digital twin (DT) engineering to generate missing values and build a learning model based on reinforcement learning (RL). The effectiveness of this approach was validated through implementation at Aures Batna Dairy, a prominent player in Algeria's dairy industry. Experiments demonstrated the superior efficiency of this method; its precision surpassed that of traditional data imputation techniques by a significant margin.

TRANSFORMATION OF 2D IMAGES INTO 3D BY THE DEEP-LEARNING

Faulty Detection System Based on SPC and Machine Learning Techniques
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.

Tifinagh Handwriting Character Recognition Using a CNN Provided as a Web Service
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.

Region of Interest and Redundancy Problem in Migratory Birds Wild Life Surveillance
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.

Detection of flooding attack on obs network using ant colony optimization and machine learning
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.

Aircraft engines Remaining Useful Life prediction with an adaptive denoising online sequential Extreme Learning Machine
Tarek Berghout, Leïla-Hayet Mouss, Ouahab Kadri, Lotfi Saïdi, and Mohamed Benbouzid
Elsevier BV

Regularized length changeable extreme learning machine with incremental learning enhancements for remaining useful life prediction of aircraft engines
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.

Aircraft engines remaining useful life prediction with an improved online sequential extreme learning machine
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.

Hybrid multi-agent and immune algorithm approach to hybrid flow shops scheduling with SDST

Fault diagnosis for a milk pasteurisation plant with missing data
Ouahab Kadri, L.H. Mouss, and Adel Abdelhadi
Inderscience Publishers

Identification and detection of the process fault in a cement rotary kiln by extreme learning machine and ant colony optimization

Electrical faults detection for the intelligent diagnosis of a photovoltaic generator

Fault diagnosis of rotary kiln using SVM and binary ACO
Ouahab Kadri, Leila Hayet Mouss, and Mohamed Djamel Mouss
Springer Science and Business Media LLC

An efficient hybrid approach based on SVM and binary ACO for feature selection

A hybrid feature subset selection approach based on SVM and binary ACO. Application to industrial diagnosis

RECENT SCHOLAR PUBLICATIONS

EcoWatch: Region of interest-based multi-quantization resource-efficient framework for migratory bird surveillance using wireless sensor networks and environmental context
O Hadji, M Maimour, A Benyahia, O Kadri, E Rondeau
Computers and Electrical Engineering 123, 110076 2025

Enhanced Iot Network Security Through Optimized Feature Subset Selection Using Gto Algorithm
A Benyahia, O Kadri, M Hamouma
http://dx.doi.org/10.2139/ssrn.5100107 2025

Enhanced Energy Efficiency in Visual Sensor Networks through ROI-Based Compression Techniques in Wildlife Surveillance
O Hadji, M Maimour, O Kadri, A Benyahia, E Rondeau
2024 12th International Conference on Systems and Control (ICSC), 395-400 2024

Enhancing epidemic management: agent-based simulation and remote diagnosis
DE Abdelaziz, O Kadri
Brazilian Journal of Technology 7 (2), e70355-e70355 2024

DNN inference splitting and offloading in the Internet of Things: A survey
C Bouarouguene, M Maimour, O Kadri, E Rondeau, A Benyahia
1er Congrs annuel de la Socit d’Automatique de Gnie Industriel et de 2023

Proposed system for efficient wildlife monitoring using WSN and image processing
O Hadji, A Benyahia, M Maimour, E Rondeau, O Kadri
1er Congrs annuel de la Socit d’Automatique de Gnie Industriel et de 2023

Faulty Detection System Based on SPC and Machine Learning Techniques.
ME Benrabah, O Kadri, KN Mouss, A Lakhdari
Revue d'Intelligence Artificielle 36 (6) 2022

Region of interest and redundancy problem in migratory birds wild life surveillance
O Hadji, O Kadri, M Maimour, E Rondeau, A Benyahia
2022 International Conference on Advanced Aspects of Software Engineering 2022

Imputation as service using support vector regression: Application to a photovoltaic system in Algeria
MTE Seddik, O Kadri, MR Abdessemed
1st National Conference of Materials sciences And Engineering,(MSE'22) 2022

Tifinagh Handwriting Character Recognition Using a CNN Provided as a Web Service
K Ouahab, B Abderrezak, A Adel
International Journal of Cloud Applications and Computing (IJCAC) 12 (1), 1-17 2022

Detection of flooding attack on obs network using ant colony optimization and machine learning
M Takieddine Seddik, O Kadri, C Bouarouguene, H Brahimi
Computacin y Sistemas 25 (2), 423-433 2021

Aircraft engines remaining useful life prediction with an adaptive denoising online sequential extreme learning machine
T Berghout, LH Mouss, O Kadri, L Sadi, M Benbouzid
Engineering Applications of Artificial Intelligence 96, 103936 2020

HYBRID MULTI-AGENT AND IMMUNE ALGORITHM APPROACH TO HYBRID FLOW SHOPS SCHEDULING WITH SDST.
A ABDELHADI, LH MOUSS, O KADRI
Academic Journal of Manufacturing Engineering 18 (3) 2020

Regularized length changeable extreme learning machine with incremental learning enhancements for remaining useful life prediction of aircraft engines
T BERGHOUT, LH MOUSS, O KADRI, N HADJIDJ
2020 1st International Conference on Communications, Control Systems and 2020

Aircraft engines remaining useful life prediction with an improved online sequential extreme learning machine
T Berghout, LH Mouss, O Kadri, L Sadi, M Benbouzid
Applied Sciences 10 (3), 1062 2020

RUL prediction (C-MAPSS dataset)
T BERGHOUT, LH Mouss, O Kadri
2019

Application des colonies de fourmis pour le diagnostic industriel
K Ouahab, A Adel, M Hayet
Presses Acadmiques Francophones 1, 180 2018

Relational database courses and exercises
K Ouahab, A Adel
GRIN Verlag 2018

L’application des algorithmes de colonies de fourmis pour le diagnostic des systmes dynamiques et complexes. Doctorat thesis (2013), Universit de Batna 2.
K Ouahab
2017

Identification and detection of the process fault in a cement rotary kiln by extreme learning machine and ant colony optimization
O KADRI, LH MOUSS
Academic Journal of Manufacturing Engineering 15 (2) 2017

MOST CITED SCHOLAR PUBLICATIONS

Fault diagnosis of rotary kiln using SVM and binary ACO
O Kadri, LH Mouss, MD Mouss
Journal of mechanical science and technology 26, 601-608 2012
Citations: 59

Aircraft engines remaining useful life prediction with an improved online sequential extreme learning machine
T Berghout, LH Mouss, O Kadri, L Sadi, M Benbouzid
Applied Sciences 10 (3), 1062 2020
Citations: 44

Tifinagh Handwriting Character Recognition Using a CNN Provided as a Web Service
K Ouahab, B Abderrezak, A Adel
International Journal of Cloud Applications and Computing (IJCAC) 12 (1), 1-17 2022
Citations: 17

Electrical faults detection for the intelligent diagnosis of a photovoltaic generator
W Rezgui, LH Mouss, MD Mouss, O Kadri, A DISSA
Journal of Electrical Engineering 14 (1), 77-84 2014
Citations: 12

Detection of flooding attack on obs network using ant colony optimization and machine learning
M Takieddine Seddik, O Kadri, C Bouarouguene, H Brahimi
Computacin y Sistemas 25 (2), 423-433 2021
Citations: 4

Algorithmes du syst eme immunitaire artificiel pour la surveillance industrielle
A Abdelhadi, H Mouss, O Kadri
International Conference on Industrial Engineering and Manufacturing ICIEM'10 2010
Citations: 4

La Surveillance Industriel Dynamique par les Systmes Neuro-Flous Temporels : Application un systme de Production
M Rafik, M Hayet, C Ouahiba, K Ouahab, H Hichem
International Conference: Sciences of Electronic, Technologies of 2009
Citations: 4

Fault prognosis by temporal neuro-fuzzy systems: application for manufacturing systems
R Mahdaoui, LH Mouss, O Kadri
Proceedings of IEEE SETIT, Sousse, Tunisia, 1-6 2012
Citations: 3

Faulty Detection System Based on SPC and Machine Learning Techniques.
ME Benrabah, O Kadri, KN Mouss, A Lakhdari
Revue d'Intelligence Artificielle 36 (6) 2022
Citations: 2

Fault diagnosis for a milk pasteurisation plant with missing data
O Kadri, LH Mouss, A Abdelhadi
International Journal of Quality Engineering and Technology 6 (3), 123-136 2017
Citations: 2

L’application des algorithmes de colonies de fourmis pour le diagnostic des systmes dynamiques et complexes
O Kadri
Universit de Batna 2 2013
Citations: 2

Reconnaissance des Formes par SVM pour le Diagnostic du Systme de Pasteurisation d’une Usine de Lait
O Kadri, LH Mouss, MD Mouss, A Abdelhadi
Revue des Sciences et de la Technologie –RST 4 (1), 35-52 2013
Citations: 2

kadri Ouahab,(2009)
M Rafik, MH Leyla, C Ouahiba, H Hichem
Industrial dynamics monitoring by Temporals Neuro-Fuzzy systems: Application
Citations: 2