Prof. Dr. Yassine Maleh
Sultan moulay slimane university, Béni Mellal, Béni Mellal-Khénifra, Morocco
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Academic Career Length
13Years (2014-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications8 February 2022 in Journal of Reliable Intelligent Environments
Journal Article
Yassine MalehYQYoussef Qasmaoui
Youssef QasmaouiKGKhalid El Gholami
Khalid El GholamiYSYassine Sadqi
Yassine SadqiSMSoufyane Mounir
Soufyane MounirJournal Article
SESiham ESSAHRAUI
Siham ESSAHRAUI
ISMAIL LAMAAKALIHIkhlas El Hamly
Ikhlas El HamlyYassine MalehIOIbrahim Ouahbi
Ibrahim OuahbiAEAhmed Abd El-Latif
Ahmed Abd El-Latif +4
Drowsy driving poses a significant challenge to road safety worldwide, contributing to thousands of accidents and fatalities annually. Despite advancements in driver drowsiness detection (DDD) systems, many existing methods face limitations such as intrusiveness and delayed reaction times. This research addresses these gaps by leveraging facial analysis and state-of-the-art machine learning techniques to develop a real-time, non-intrusive DDD system. A distinctive aspect of this research is its systematic assessment of various machine and deep learning algorithms across three pivotal public datasets, the NTHUDDD, YawDD, and UTA-RLDD, known for their widespread use in drowsiness detection studies. Our evaluation covered techniques including the K-Nearest Neighbors (KNNs), support vector machines (SVMs), convolutional neural networks (CNNs), and advanced computer vision (CV) models such as YOLOv5, YOLOv8, and Faster R-CNN. Notably, the KNNs classifier reported the highest accuracy of 98.89%, a precision of 99.27%, and an F1 score of 98.86% on the UTA-RLDD. Among the CV methods, YOLOv5 and YOLOv8 demonstrated exceptional performance, achieving 100% precision and recall with mAP@0.5 values of 99.5% on the UTA-RLDD. In contrast, Faster R-CNN showed an accuracy of 81.0% and a precision of 63.4% on the same dataset. These results demonstrate the potential of our system to significantly enhance road safety by providing proactive alerts in real time.
1 January 2015 in Procedia Computer Science
Journal Article
Yassine MalehAEAbdellah Ezzati
Abdellah Ezzati
YQYoussef Qasmaoui
Youssef Qasmaoui
MMMohamed Mbida
Mohamed Mbida
Many researchers are currently focusing on the security of wireless sensor networks (WSNs). This type of network is associated with vulnerable characteristics such as open-air transmission and self-organizing withoutafixed infrastructure. Intrusion Detection Systems (IDSs) can play an important role in detecting and preventing security attacks. In this paper, we propose a hybrid, lightweight intrusion detection system for sensor networks. Our intrusion detection model takes advantage of cluster-based architecture to reduce energy consumption. This model uses anomaly detection based on support vector machine (SVM) algorithm and aset of signature rules to detect malicious behaviors and provide global lightweight IDS. Simulation results show that the proposed model can detect abnormal events efficiently andhas a high detection rate with lower false alarm
Journal Article
ISMAIL LAMAAKALYassine MalehKMKhalid El Makkaoui
Khalid El MakkaouiIOIbrahim Ouahbi
Ibrahim Ouahbi
Pawel PlawiakAEAhmed Abd El-Latif
Ahmed Abd El-Latif +2
Large Language Models (LLMs), like GPT and BERT, have significantly advanced Natural Language Processing (NLP), enabling high performance on complex tasks. However, their size and computational needs make LLMs unsuitable for deployment on resource-constrained devices, where efficiency, speed, and low power consumption are critical. Tiny Language Models (TLMs), also known as BabyLMs, offer compact alternatives by using advanced compression and optimization techniques to function effectively on devices such as smartphones, Internet of Things (IoT) systems, and embedded platforms. This paper provides a comprehensive survey of TLM architectures and methodologies, including key techniques such as knowledge distillation, quantization, and pruning. Additionally, it explores potential and emerging applications of TLMs in automation and control, covering areas such as edge computing, IoT, industrial automation, and healthcare. The survey discusses challenges unique to TLMs, such as trade-offs between model size and accuracy, limited generalization, and ethical considerations in deployment. Future research directions are also proposed, focusing on hybrid compression techniques, application-specific adaptations, and context-aware TLMs optimized for hardware-specific constraints. This paper aims to serve as a foundational resource for advancing TLMs capabilities across diverse real-world applications.
29 April 2025 in IEEE Internet of Things Journal
Journal Article
ISMAIL LAMAAKALSESiham Essahraui
Siham Essahraui
Yassine MalehKMKhalid El Makkaoui
Khalid El MakkaouiIOIbrahim Ouahbi
Ibrahim OuahbiDNDusit Niyato
Dusit Niyato +4
The integration of Tiny Machine Learning (TinyML) with Human Behavior Analysis (HBA) represents a significant advancement in the field of Artificial Intelligence (AI), enabling real-time, efficient, and privacy-preserving analysis on resource-constrained devices. This paper provides the first comprehensive survey exploring this integration, presenting a detailed overview of TinyML, including its definitions, key concepts and advantages. The survey proposes a systematic taxonomy of TinyML applications in HBA, categorizing state-of-the-art implementations based on their use cases and specific methodologies. Furthermore, the challenges and limitations of integrating TinyML in HBA are thoroughly discussed, including technical constraints, data quality issues, and ethical considerations. Finally, future research directions and open issues are outlined, emphasizing the potential advancements and emerging trends in this field. This survey serves as a foundational resource, guiding researchers and practitioners in harnessing the capabilities of TinyML to advance HBA.
24 November 2025 in IEEE Access
Journal Article
CYChaymae Yahyati
Chaymae YahyatiISMAIL LAMAAKALYassine MalehKMKhalid El Makkaoui
Khalid El Makkaoui
IOIbrahim Ouahbi
Ibrahim OuahbiAEAhmed Abd El-Latif
Ahmed Abd El-Latif +1
Tiny Machine Learning (TinyML) has emerged as a transformative paradigm enabling machine learning inference directly on ultra-low-power microcontrollers and edge devices. As AI expands beyond cloud computing to resource-constrained environments, TinyML offers promising solutions for latency-sensitive, bandwidth-efficient, and privacy-preserving applications. This paper presents a Systematic review of state-of-the-art TinyML applications across three critical domains: healthcare, education, and transportation. By analyzing 136 peer-reviewed publications from 2020 to 2025, we identify key trends, representative use cases, and the enabling technologies that support domain-specific deployments. Our review evaluates software frameworks, hardware platforms, model optimization techniques (e.g., quantization, pruning, and neural architecture search), and real-world deployment challenges such as energy consumption, memory limitations, and explainability. We further synthesize the metrics used to assess TinyML systems and highlight open research questions. Unlike previous surveys, our domain-centric approach offers a deeper contextual analysis of how TinyML is being adapted to solve real-world problems across diverse sectors. We conclude by outlining future directions and practical insights to guide researchers and practitioners in designing scalable, resilient, and ethically grounded TinyML systems.
About Me
Yassine Maleh is an associate professor of cybersecurity and IT governance at Sultan Moulay Slimane University, Morocco, since 2019. He is the founding chair of the IEEE Consultant Network, Morocco, and the founding president of the African Research Center of Information Technology and Cybersecurity. He is a former CISO at the National Port Agency between 2012 and 2019. He is a senior member of IEEE. Dr Maleh has made contributions in the fields of information security and privacy, Internet of Things security, and wireless and constrained networks security. His research interests include information security and privacy, Internet of Things, networks security, information system, and IT governance. He has published more than 200 papers (book chapters, international journals, and conferences/workshops), 40 edited books, and 8 authored books. He is the editor-in-chief of the International Journal of Information Security and Privacy (IJISP, IF: 0.8), and the International Journal of Smart Security Technologies (IJSST). He is a series editor of Advances in Cybersecurity Management, by CRC Taylor & Francis. He was also a guest editor for many special issues with prestigious journals. He has served and continues to serve on executive and technical program committees and as a reviewer of numerous international conferences and journals such as Elsevier Ad Hoc Networks, IEEE Network Magazine, IEEE Sensor Journal, ICT Express, and Springer Cluster Computing.