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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Amnai, Mohamed
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Intelligent task processing using mobile edge computing: processing time optimization Maftah, Sara; El Ghmary, Mohamed; El Bouabidi, Hamid; Amnai, Mohamed; Ouacha, Ali
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp143-152

Abstract

The fast-paced development of the internet of things led to the increase of computing resource services that could provide a fast response time, which is an unsatisfied feature when using cloud infrastructures due to network latency. Therefore, mobile edge computing became an emerging model by extending computation and storage resources to the network edge, to meet the demands of delaysensitive and heavy computing applications. Computation offloading is the main feature that makes Edge computing surpass the existing cloud-based technologies to break limitations such as computing capabilities, battery resources, and storage availability, it enhances the durability and performance of mobile devices by offloading local intensive computation tasks to edge servers. However, the optimal solution is not always guaranteed by offloading computation, therefore, the offloading decision is a crucial step depending on many parameters that should be taken in consideration. In this paper, we use a simulator to compare a two tier edge orchestrator architecture with the results obtained by implementing a system model that aims to minimize a task’s processing time constrained by time delay and the limited device’s computational resource and usage based on a modified version.
A highly scalable CF recommendation system using ontology and SVD-based incremental approach Mhammedi, Sajida; Gherabi, Noreddine; El Massari, Hakim; Sabouri, Zineb; Amnai, Mohamed
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.6261

Abstract

In recent years, the need of recommender systems has increased to enhance user engagement, provide personalized services, and increase revenue, especially in the online shopping industry where vast amounts of customer data are generated. Collaborative filtering (CF) is the most widely used and effective approach for generating appropriate recommendations. However, the current CF approach has limitations in addressing common recommendation problems such as data inaccuracy recommendations, sparsity, scalability, and significant errors in prediction. To overcome these challenges, this study proposes a novel hybrid CF method for movie recommendations that combines the incremental singular value decomposition approach with an item-based ontological semantic filtering approach in two phases, online and offline. The ontology-based technique is leveraged to enhance the accuracy of predictions and recommendations. Evaluating our method on a real-world movie recommendation dataset using precision, F1 scores, and mean absolute error (MAE) demonstrates that our system generates accurate predictions while addressing sparsity and scalability issues in recommendation system. Additionally, our method has the advantage of reduced running time.
Butterfly optimization-based ensemble learning strategy for advanced intrusion detection in internet of things networks Choukhairi, Mouad; Tahiri, Sara; Choukhairi, Ouail; Fakhri, Youssef; Amnai, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3494-3505

Abstract

The massive growth in internet of things (IoT) devices has led to enhanced functionalities through their interconnections with other devices, smart infrastructures, and networks. However, increased connectivity also increases the risk of cyberattacks. To protect IoT systems from these threats, intrusion detection systems (IDS) employing machine learning (ML) techniques have been developed to identify cybersecurity threats. This paper introduces a novel ensemble IDS framework called butterfly optimization-based ensemble learning (BOEL). This framework integrates the butterfly optimization algorithm (BOA) with ensemble learning techniques to improve IDS detection performance in IoT networks. BOEL is designed to accurately detect various types of attacks in IoT networks by dynamically optimizing the weights of base learners, which are the four sophisticated ML gradient-boosting algorithms (GBM, CatBoost, XGBoost, and LightGBM) for each attack category, and identifying the best weight combination for ensemble models. Experiments conducted on two public IoT security datasets, CICIDS2017 and Bot-IoT, demonstrate the robustness of the proposed BOEL in intrusion detection across diverse IoT environments, achieving 99.795% accuracy on CICIDS2017 and 99.966% accuracy on Bot-IoT. These results outline the successful application of diverse learning approaches and highlight the framework’s potential to enhance IDS in addressing IoT cyber threats.
Comparative analysis of machine learning models for fake news detection in social media Eddine Elbaghazaoui, Bahaa; Amnai, Mohamed; Fakhri, Youssef; Choukri, Ali; Gherabi, Noreddine
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1951-1959

Abstract

The rapid rise of information sharing on social media has amplified the spread of fake news, making its detection increasingly critical. As fake news continues to proliferate, the need for efficient detection mechanisms has become more urgent to protect users from misinformation and disinformation. This paper presents a comparative analysis of multiple machine learning models for detecting text based fake news on social media platforms. Using models such as gradient boosting, XGBoost, and linear support vector classifier (SVC) on the Infor mation Security and Object Technology (ISOT) fake news dataset, the study demonstrates that gradient boosting achieves the highest accuracy of 99.61%, while XGBoost provides a strong balance with 99.59% accuracy and a signifi cantly lower execution time, making it more suitable for real-time applications. These results offer valuable insights into the trade-offs between accuracy and computational efficiency, contributing to the development of more practical de tection systems and future research in the field.
Co-Authors Choukhairi, Mouad Choukhairi, Ouail Choukri, Ali Eddine Elbaghazaoui, Bahaa El Bouabidi, Hamid El Ghmary, Mohamed El Massari, Hakim Fakhri, Youssef Gherabi, Noreddine Maftah, Sara Mhammedi, Sajida Ouacha, Ali Sabouri, Zineb Tahiri, Sara