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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Youssef Fakhri
Ibn Tofail University
Computer Science & IT Electrical & Electronics Engineering Engineering

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Recognition of Arabic handwritten words using convolutional neural network Asmae Lamsaf; Mounir Ait Kerroum; Siham Boulaknadel; Youssef Fakhri
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 2: May 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i2.pp1148-1155

Abstract

A new method for recognizing automatically Arabic handwritten words was presented using convolutional neural network architecture. The proposed method is based on global approaches, which consists of recognizing all the words without segmenting into the characters in order to recognize them separately. Convolutional neural network (CNN) is a particular supervised type of neural network based on multilayer principle; our method needs a big dataset of word images to obtain the best result. To optimize our system, a new database was collected from the benchmarking Arabic handwriting database using the pre-processing such as rotation transformation, which is applied on the images of the database to create new images with different features. The convolutional neural network applied on our database that contains 40320 of Arabic handwritten words (26880 images for training set and 13440 for test set). Thus, different configurations on a public benchmark database were evaluated and compared with previous methods. Consequently, it is demonstrated a recognition rate with a success of 96.76%.
Predicting user behavior using data profiling and hidden Markov model Bahaa Eddine Elbaghazaoui; Mohamed Amnai; Youssef Fakhri
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5444-5453

Abstract

Mental health disorders affect many aspects of patient’s lives, including emotions, cognition, and especially behaviors. E-health technology helps to collect information wealth in a non-invasive manner, which represents a promising opportunity to construct health behavior markers. Combining such user behavior data can provide a more comprehensive and contextual view than questionnaire data. Due to behavioral data, we can train machine learning models to understand the data pattern and also use prediction algorithms to know the next state of a person’s behavior. The remaining challenges for this issue are how to apply mathematical formulations to textual datasets and find metadata that aids to identify the person’s life pattern and also predict the next state of his comportment. The main idea of this work is to use a hidden Markov model (HMM) to predict user behavior from social media applications by analyzing and detecting states and symbols from the user behavior dataset. To achieve this goal, we need to analyze and detect the states and symbols from the user behavior dataset, then convert the textual data to mathematical and numerical matrices. Finally, apply the HMM model to predict the hidden user behavior states. We tested our program and identified that the log-likelihood was higher and better when the model fits the data. In any case, the results of the study indicated that the program was suitable for the purpose and yielded valuable data.
A transfer hybrid deep learning approach for advanced intrusion detection in IoT-based smart home security Mouad Choukhairi; Ouail Choukhairi; Youssef Fakhri; Ali Choukri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2750-2760

Abstract

As smart home environments increasingly rely on interconnected internet of things (IoT) devices, they face growing cyber threats originating both externally from malicious actors and internally from compromised or malfunctioning IoT devices. These threats, including unauthorized access, distributed denial of service (DDoS) attacks, and data exfiltration, pose significant risks to the security and privacy of smart home inhabitants. This paper introduces an advanced intrusion detection system (IDS) specifically designed to enhance the security of IoT-based smart home networks. Leveraging a hybrid deep learning approach combining convolutional neural networks (CNN) and long short-term memory (LSTM) models, complemented by transfer learning (TL) and hyper-parameter optimization techniques, our proposed IDS efficiently identifies both external and intra-network threats. Using the IoTID20 dataset, which simulates realistic attack scenarios, the IDS was trained and evaluated to detect abnormal behavior effectively within smart home networks. CNN layers extract spatial features from network traffic, while LSTM layers capture temporal dependencies, enabling robust detection against a range of cyber-threats. Evaluation results demonstrate the IDS’s high detection accuracy and exceptional F1-scores, validating its effectiveness in safeguarding IoT-based smart homes from evolving threats.
An efficient ensemble tree-based framework for intrusion detection in industrial internet of things networks Mouad Choukhairi; Oumaima Chentoufi; Ouail Choukhairi; Youssef Fakhri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp481-492

Abstract

The increasing complexity of cyber threats in industrial internet of things (IIoT) environments necessitates robust, scalable, and efficient intrusion detection systems (IDS). This study presents a novel ensemble tree-based framework that integrates gradient boosting-based machine learning models, including XGBoost, LightGBM, AdaBoost, and CatBoost, with mutual information (MI) feature selection and synthetic minority over-sampling technique (SMOTE) to enhance multiclass intrusion detection performance. The framework is designed to handle large-scale, imbalanced datasets efficiently while maintaining high classification accuracy. Performance evaluation using the telemetry of network (ToN)-IoT benchmark dataset demonstrates that the proposed models achieve a high accuracy of 99.43%, with a strong precision-recall balance and an F1-score, ensuring minimal false positive rates of 0.08%. By leveraging MI for optimal feature selection and SMOTE for data balancing, this approach effectively enhances detection capabilities in highly dynamic network environments. The lightweight architecture and reduced execution time make the framework well-suited for deployment in edge or fog nodes within smart industrial environments. The proposed solution provides a scalable and adaptable methodology for securing IIoT networks, making it applicable for real-time intrusion monitoring and further cybersecurity advancements in industrial systems.
Co-Authors Ali Choukri Asmae Lamsaf Bahaa Eddine Elbaghazaoui Mohamed Amnai Mouad Choukhairi Mounir Ait Kerroum Ouail Choukhairi Oumaima Chentoufi Siham Boulaknadel