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

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Enhancing machine failure prediction with a hybrid model approach Khattach, Ouiam; Moussaoui, Omar; Hassine, Mohammed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2946-2955

Abstract

The industrial sector is undergoing a substantial transformation by embracing predictive maintenance approaches, aiming to minimize downtime and reduce operational expenses. This transformative shift involves the incorporation of machine learning techniques to refine the accuracy of predicting machinery failures. In this article, we delve into an in-depth exploration of machine failure prediction, employing a hybrid model amalgamating long short-term memory (LSTM) and support vector machine (SVM). Our comprehensive study meticulously assesses the hybrid model’s performance, comparing it with standalone LSTM and SVM models across three distinct datasets. The results showcase that the hybrid model outperformed, providing the modest dependable, and highest F1-score values in our evaluation.
Semantic segmentation and thermal imaging for forest fires detection and monitoring by drones Yandouzi, Mimoun; Berrahal, Mohammed; Grari, Mounir; Boukabous, Mohammed; Moussaoui, Omar; Azizi, Mostafa; Ghoumid, Kamal; Kerkour Elmiad, Aissa
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7663

Abstract

Forest ecosystems play a crucial role in providing a wide range of ecological, social, and economic benefits. However, the increasing frequency and severity of forest fires pose a significant threat to the sustainability of forests and their functions, highlighting the need for early detection and swift action to mitigate damage. The combination of drones and artificial intelligence, particularly deep learning, proves to be a cost-effective solution for accurately and efficiently detecting forest fires in real-time. Deep learning-based image segmentation models can not only be employed for forest fire detection but also play a vital role in damage assessment and support reforestation efforts. Furthermore, the integration of thermal cameras on drones can significantly enhance the sensitivity in forest fire detection. This study undertakes an in-depth analysis of recent advancements in deep learning-based semantic segmentation, with a particular focus on model’s mask region convolutional neural network (Mask R-CNN) and you only look once (YOLO) v5, v7, and v8 variants. Emphasis is placed on their suitability for forest fire monitoring using drones equipped with RGB and/or thermal cameras. The conducted experiments have yielded encouraging outcomes across various metrics, underscoring its significance as an invaluable asset for both fire detection and continuous monitoring endeavors.
Literature review on forecasting green hydrogen production using machine learning and deep learning Rhafes, Mohamed Yassine; Moussaoui, Omar; Raboaca, Maria Simona
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i2.pp884-893

Abstract

Green hydrogen is a sustainable and clean energy source, for this purpose, it conducts the global energy transition. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) with the process of green hydrogen production is essential in enhancing its production. This literature review studies in detail the intersection between AI and green hydrogen. Firstly, it concentrates on ML and DL algorithms used in forecasting green hydrogen production. Secondly, it presents an analysis of the studies released from 2021 to March 2024. Finally, the focus is on the results realized by the ML and DL algorithms proposed by the studies reviewed. This study provides a summary that explains the trends and methods used, as well as highlights the gaps and the opportunities in the field of AI and green hydrogen production. This liternature review presents a solid foundation for future research initiatives in this field.
Unveiling critical features for failure prediction in green internet of things applications Khattach, Ouiam; Moussaoui, Omar; Hassine, Mohammed
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4308-4318

Abstract

The rapid growth of the green internet of things (GIoT) in recent years signifies a transformative shift in internet of things (IoT) solution development. This evolution is driven by technological advancements, heightened environmental awareness, and a global imperative to combat climate change. Ensuring the reliability of GIoT applications is crucial for their success. This study identifies critical features for predicting IoT device failures, enabling early detection and intervention. Using datasets from industry, energy, and agriculture sectors, we employ a feature selection strategy to analyze extensive data from diverse GIoT deployments. Our analysis identifies significant features and integrates key insights from existing literature. Our findings support enhanced predictive maintenance strategies, reduced downtime, and improved overall performance of sustainable IoT solutions.
Co-Authors Azizi, Mostafa Berrahal, Mohammed Boukabous, Mohammed Ghoumid, Kamal Grari, Mounir Hassine, Mohammed Kerkour Elmiad, Aissa Khattach, Ouiam Raboaca, Maria Simona Rhafes, Mohamed Yassine Yandouzi, Mimoun