Abdussalam Elhanashi is an an accomplished AI researcher specializing in deep learning for computer vision, imaging, and video applications. He holds a PhD in Information Engineering from Università di Pisa (IsDB scholarship), an MSc from the University of Glasgow, and an MBA from the University of Nicosia. With 16+ years of experience, he has developed AI-driven diagnostic tools (University of Strathclyde) and advanced video analysis techniques (Hiroshima University). His work bridges AI research and real-world applications in healthcare imaging, surveillance, and edge AI. An active SIIM member, he focuses on optimized, deployable AI systems, with expertise in model optimization, medical imaging, video analysis, and lightweight AI for edge devices.
EDUCATION
PhD in Information Engineering at University of Pisa (Italy) :-From June 2019 till June 2022
(Graduated on 03/02/2023)
MBA, University of Nicosia: Nicosia, Cyprus (2015-11-11 to 2018-03-01)
MSc Electronic and Electrical Engineering with Management at University of Glasgow ( The
UK ) Year :-From January 2017 till January 2018 (Graduated on 08/01/2018)
BSc in Electronics and Electrical Engineering at College of Engineering Technology Janzour
(Libya) Year:-From February 2010 till February 2015 (Graduated on 10/02/2015)
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Computer Science, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition
56
Scopus Publications
2854
Scholar Citations
26
Scholar h-index
31
Scholar i10-index
Scopus Publications
Generative AI and the Foundation Model Era: A Comprehensive Review Abdussalam Elhanashi, Siham Essahraui, Pierpaolo Dini, Davide Paolini, Qinghe Zheng, Sergio Saponara Big Data and Cognitive Computing, 2026 Generative artificial intelligence and foundation models have changed machine learning by allowing systems to produce readable text, realistic images, and other multimodal content with little direct input from a user. Foundation models are large neural networks trained on very large and varied datasets, and they form the core of many current generative AI (GenAI) systems. Their rapid development has led to major advances in areas like natural language processing, computer vision, multimodal learning, and robotics. Examples include GPT, LLaMA, and diffusion-based architectures, such as models often used for image generation. Systems such as Stable Diffusion show this shift by illustrating how AI can interpret information, draw basic inferences, and produce new outputs using more than one type of data. This review surveys common foundation model architectures and examines what they can do in generative tasks. It reviews Transformer, diffusion, and multimodal architectures, focusing on methods that support scaling and transfer across domains. The paper also reviews key approaches to pretraining and fine-tuning, including self-supervised learning, instruction tuning, and parameter-efficient adaptation, which support these systems’ ability to generalize across tasks. In addition to the technical details, this review discusses how GenAI is being used for text generation, image synthesis, robotics, and biomedical research. The study also notes continuing challenges, such as the high computing and energy demands of large models, ethical concerns about data bias and misinformation, and worries about privacy, reliability, and responsible use of AI in real settings. This review brings together ideas about model design, training methods, and social implications to point future research toward GenAI systems that are efficient, easy to interpret, and reliable, while supporting scientific progress and ethical responsibility.
Advanced Fault Detection and Diagnosis Exploiting Machine Learning and Artificial Intelligence for Engineering Applications Davide Paolini, Pierpaolo Dini, Abdussalam Elhanashi, Sergio Saponara Electronics Switzerland, 2026 Modern engineering systems require reliable and timely Fault Detection and Diagnosis (FDD) to ensure operational safety and resilience. Traditional model-based and rule-based approaches, although interpretable, exhibit limited scalability and adaptability in complex, data-intensive environments. This survey provides a systematic overview of recent studies exploring Machine Learning (ML) and Artificial Intelligence (AI) techniques for FDD across industrial, energy, Cyber-Physical Systems (CPS)/Internet of Things (IoT), and cybersecurity domains. Deep architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Graph Neural Networks (GNNs) are compared with unsupervised, hybrid, and physics-informed frameworks, emphasizing their respective strengths in adaptability, robustness, and interpretability. Quantitative synthesis and radar-based assessments suggest that AI-driven FDD approaches offer increased adaptability, scalability, and early fault detection capabilities compared to classical methods, while also introducing new challenges related to interpretability, robustness, and deployment. Emerging research directions include the development of foundation and multimodal models, federated learning (FL), and privacy-preserving learning, as well as physics-guided trustworthy AI. These trends indicate a paradigm shift toward self-adaptive, interpretable, and collaborative FDD systems capable of sustaining reliability, transparency, and autonomy across critical infrastructures.
Leveraging IoT and dedicated social networks to enhance mosque role and activities management in Saudi Arabia Nawal Almutairi, Abdussalam Elhanashi Digital Business, 2025 This paper examines the challenges encountered in mosques activities management and explores the potential use of internet of things. To this end, qualitative methods, surveys and semi-structured interviews, were conducted. The results reveal key challenges, which are related to mosque accessibility, communication, and engagement in decision-making. As a part of the proposed solution, the Mehrab system incorporates a dedicated social networking, smart locks, and smart key delegation. Mehrab was developed using an Agile methodology that involves potential users throughout the development process to ensure the inclusion of essential features aligned with the system’s objectives. Mehrab was tested by 400 participants who performed a set of tasks that covered the core functionalities and then answered questionnaires to evaluate application objectives achievement, usability, and technology acceptance. Statistical analysis revealed that participants’ evaluations of system objectives differed significantly from a neutral benchmark, indicating a positive perception. A system usability scale survey was used to assess usability, and we achieved an excellent result of 94.23, exceeding the threshold to pass the test. To assess technology acceptance, the technology acceptance model was integrated with institutional theory, offering a robust framework for understanding acceptance factors in religious settings. Regression analysis showed that perceived usefulness, attitude toward using technology, and behavioral intention were the strongest predictors of actual use which commonly interpreted as indicators of technology acceptance, while institutional factors, including normative and coercive pressures, also had significant influence. Moreover, sentiment analysis was employed to measure users’ opinion, which indicated that 94.5% of participants had positive opinions. • TAM and institutional theory are used to assess community acceptance of technology. • Enhance interfaith dialogue and collaboration using IoT and smart applications. • The Saudi Muslim community has strong acceptance of IoT in their mosque. • Demographic characteristics had an influence in some model constructs. • Females had more acceptance level of technology compared to male participants.
Early Fire and Smoke Detection Using Deep Learning: A Comprehensive Review of Models, Datasets, and Challenges Abdussalam Elhanashi, Siham Essahraui, Pierpaolo Dini, Sergio Saponara Applied Sciences Switzerland, 2025 The early detection of fire and smoke is essential for mitigating human casualties, property damage, and environmental impact. Traditional sensor-based and vision-based detection systems frequently exhibit high false alarm rates, delayed response times, and limited adaptability in complex or dynamic environments. Recent advances in deep learning and computer vision have enabled more accurate, real-time detection through the automated analysis of flame and smoke patterns. This paper presents a comprehensive review of deep learning techniques for fire and smoke detection, with a particular focus on convolutional neural networks (CNNs), object detection frameworks such as YOLO and Faster R-CNN, and spatiotemporal models for video-based analysis. We examine the benefits of these approaches in terms of improved accuracy, robustness, and deployment feasibility on resource-constrained platforms. Furthermore, we discuss current limitations, including the scarcity and diversity of annotated datasets, susceptibility to false alarms, and challenges in generalization across varying scenarios. Finally, we outline promising research directions, including multimodal sensor fusion, lightweight edge AI implementations, and the development of explainable deep learning models. By synthesizing recent advancements and identifying persistent challenges, this review provides a structured foundation for the design of next-generation intelligent fire detection systems.
A Survey of Artificial Intelligence Enabled Channel Estimation Methods: Recent Advance, Performance, and Outlook Binglin Li, Qinghe Zheng, Xinyu Tian, Mingqiang Yang, Guan Gui, Weiwei Jiang, Hongjiang Lei, Jing Jiang, Feng Shu, Abdussalam Elhanashi, Sergio Saponara Artificial Intelligence Review, 2025 With the continuous advancement of wireless communication and the emergence of new communication scenarios, channel estimation, as a core component of wireless system design, has become increasingly significant. This paper reviews important advancements in channel estimation within wireless communication systems, including applications in single-input single-output (SISO), multi-input multi-output (MIMO), orthogonal time frequency space (OTFS), orthogonal frequency division multiplexing (OFDM), and the latest reconfigurable intelligent surface (RIS) systems. We first revisit traditional channel estimation methods, such as least squares (LS), minimum mean square error (MMSE), and compressed sensing (CS), and detail their fundamental principles and scopes of application. Subsequently, we discuss how deep learning techniques offer new perspectives and solutions for channel estimation through models like convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), long short-term memory (LSTM), and graph neural network (GNN), particularly in terms of their potential to handle complicated and dynamic environments. Additionally, we analyze the advantages and disadvantages of these methods in emerging scenarios, including RIS-assisted communications, vehicular networks, indoor positioning, sensing mobile networks, and satellite communications. We also address current methods for evaluating channel estimation performance and highlight the importance of standardization and open data in advancing the field. Finally, we summarize potential future directions for channel estimation and consider its prospects in sixth-generation (6 G) wireless communication systems, aiming to provide a comprehensive technical reference on channel estimation and promote the design of efficient and intelligent wireless communication systems.
AI-Powered Object Detection in Radiology: Current Models, Challenges, and Future Direction Abdussalam Elhanashi, Sergio Saponara, Qinghe Zheng, Nawal Almutairi, Yashbir Singh, Shiba Kuanar, Farzana Ali, Orhan Unal, Shahriar Faghani Journal of Imaging, 2025 Artificial intelligence (AI)-based object detection in radiology can assist in clinical diagnosis and treatment planning. This article examines the AI-based object detection models currently used in many imaging modalities, including X-ray Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound (US). The key models from the convolutional neural network (CNN) as well as the contemporary transformer and hybrid models are analyzed based on their ability to detect pathological features, such as tumors, lesions, and tissue abnormalities. In addition, this review offers a closer look at the strengths and weaknesses of these models in terms of accuracy, robustness, and speed in real clinical settings. The common issues related to these models, including limited data, annotation quality, and interpretability of AI decisions, are discussed in detail. Moreover, the need for strong applicable models across different populations and imaging modalities are addressed. The importance of privacy and ethics in general data use as well as safety and regulations for healthcare data are emphasized. The future potential of these models lies in their accessibility in low resource settings, usability in shared learning spaces while maintaining privacy, and improvement in diagnostic accuracy through multimodal learning. This review also highlights the importance of interdisciplinary collaboration among artificial intelligence researchers, radiologists, and policymakers. Such cooperation is essential to address current challenges and to fully realize the potential of AI-based object detection in radiology.
Generative AI and the Foundation Model Era: A Comprehensive Review A Elhanashi, S Essahraui, P Dini, D Paolini, Q Zheng, S Saponara Big Data and Cognitive Computing 10 (3), 94 , 2026 2026
Object Detection Models A Elhanashi, S Saponara Deep Learning for Object Detection and Localization, 53-82 , 2026 2026
Image Processing A Elhanashi, S Saponara Deep Learning for Object Detection and Localization, 25-51 , 2026 2026
Challenges of Object Detection and Localization A Elhanashi, S Saponara Deep Learning for Object Detection and Localization, 151-169 , 2026 2026
Training and Fine-Tuning Object Detection Models A Elhanashi, S Saponara Deep Learning for Object Detection and Localization, 137-149 , 2026 2026
Deep Learning Frameworks for Object Detection A Elhanashi, S Saponara Deep Learning for Object Detection and Localization, 117-136 , 2026 2026
Applications of Object Detection and Localization A Elhanashi, S Saponara Deep Learning for Object Detection and Localization, 101-116 , 2026 2026
Understanding Localization A Elhanashi, S Saponara Deep Learning for Object Detection and Localization, 83-99 , 2026 2026
Conclusion and Summary A Elhanashi, S Saponara Deep Learning for Object Detection and Localization, 171-177 , 2026 2026
Introduction to Object Detection and Localization A Elhanashi, S Saponara Deep Learning for Object Detection and Localization, 1-24 , 2026 2026
Deep Learning for Object Detection and Localization A Elhanashi, S Saponara Springer Nature , 2026 2026
Tiny deep learning models for real-time and efficient embedded driver state detection S Essahraui, A Elhanashi, Q Zheng, N Almutairi, S Saponara Journal of Real-Time Image Processing 23 (1), 39 , 2026 2026 Citations: 3
Advanced Fault Detection and Diagnosis Exploiting Machine Learning and Artificial Intelligence for Engineering Applications D Paolini, P Dini, A Elhanashi, S Saponara Electronics 15 (2), 476 , 2026 2026 Citations: 3
Large-Scale Foundation Models for Radiological Image Analysis: Clinical Applications, Technical Challenges, and Future Directions Y Singh, O Unal, F Ali, S Salehi, S Kuanar, A Elhanashi, QA Hathaway, ... Journal of Imaging Informatics in Medicine, 1-11 , 2026 2026
Leveraging LLMs for Customized CTI Based on Indicators of Compromise From X: A Comparative Study With Traditional ML N Almutairi, F Coenen, A Elhanashi IEEE Access 13, 206673-206694 , 2025 2025 Citations: 1
Early fire and smoke detection using deep learning: A comprehensive review of models, datasets, and challenges A Elhanashi, S Essahraui, P Dini, S Saponara Applied Sciences 15 (18), 10255 , 2025 2025 Citations: 29
Leveraging IoT and dedicated social networks to enhance mosque role and activities management in Saudi Arabia N Almutairi, A Elhanashi Digital Business, 100151 , 2025 2025 Citations: 9
Early Stroke Detection: A Mobile Application for Real-Time Stroke Diagnosis Using Video and Lightweight Deep Learning A Elhanashi, M Donati, S Saponara, Q Zheng, H Yuankai, Y Singh, F Ali, ... 2025 19th International Symposium on Medical Information and Communication … , 2025 2025
Intelligent video surveillance for early drowning detection using deep learning A Elhanashi, S Saponara, P Dini, Q Zheng, N Almutairi, A Eshtiba, ... Real-time Processing of Image, Depth, and Video Information 2025 13526, 55-64 , 2025 2025 Citations: 1
Rethinking the multi-scale feature hierarchy in object detection transformer (DETR) F Liu, Q Zheng, X Tian, F Shu, W Jiang, M Wang, A Elhanashi, ... Applied Soft Computing 175, 113081 , 2025 2025 Citations: 88
MOST CITED SCHOLAR PUBLICATIONS
Real-time video fire/smoke detection based on CNN in antifire surveillance systems S Saponara, A Elhanashi, A Gagliardi Journal of Real-Time Image Processing 18 (3), 889-900 , 2021 2021 Citations: 305
Overview on intrusion detection systems design exploiting machine learning for networking cybersecurity P Dini, A Elhanashi, A Begni, S Saponara, Q Zheng, K Gasmi Applied Sciences 13 (13), 7507 , 2023 2023 Citations: 213
A real-time constellation image classification method of wireless communication signals based on the lightweight network MobileViT Q Zheng, S Saponara, X Tian, Z Yu, A Elhanashi, R Yu Cognitive Neurodynamics 18 (2), 659-671 , 2024 2024 Citations: 191
MobileRaT: a lightweight radio transformer method for automatic modulation classification in drone communication systems Q Zheng, X Tian, Z Yu, Y Ding, A Elhanashi, S Saponara, K Kpalma Drones 7 (10), 596 , 2023 2023 Citations: 186
Implementing a real-time, AI-based, people detection and social distancing measuring system for Covid-19 S Saponara, A Elhanashi, A Gagliardi Journal of Real-Time Image Processing 18 (6), 1937-1947 , 2021 2021 Citations: 177
Fine-grained modulation classification using multi-scale radio transformer with dual-channel representation Q Zheng, P Zhao, H Wang, A Elhanashi, S Saponara IEEE Communications Letters 26 (6), 1298-1302 , 2022 2022 Citations: 165
Impact of image resizing on deep learning detectors for training time and model performance S Saponara, A Elhanashi International conference on applications in electronics pervading industry … , 2021 2021 Citations: 142
DL-PR: Generalized automatic modulation classification method based on deep learning with priori regularization Q Zheng, X Tian, Z Yu, H Wang, A Elhanashi, S Saponara Engineering Applications of Artificial Intelligence 122, 106082 , 2023 2023 Citations: 129
Advancements in TinyML: Applications, limitations, and impact on IoT devices A Elhanashi, P Dini, S Saponara, Q Zheng Electronics 13 (17), 3562 , 2024 2024 Citations: 127
A real-time transformer discharge pattern recognition method based on CNN-LSTM driven by few-shot learning Q Zheng, R Wang, X Tian, Z Yu, H Wang, A Elhanashi, S Saponara Electric Power Systems Research 219, 109241 , 2023 2023 Citations: 109
Integration of deep learning into the iot: A survey of techniques and challenges for real-world applications A Elhanashi, P Dini, S Saponara, Q Zheng Electronics 12 (24), 4925 , 2023 2023 Citations: 97
Rethinking the multi-scale feature hierarchy in object detection transformer (DETR) F Liu, Q Zheng, X Tian, F Shu, W Jiang, M Wang, A Elhanashi, ... Applied Soft Computing 175, 113081 , 2025 2025 Citations: 88
Application of wavelet-packet transform driven deep learning method in PM2. 5 concentration prediction: A case study of Qingdao, China Q Zheng, X Tian, Z Yu, N Jiang, A Elhanashi, S Saponara, R Yu Sustainable Cities and Society 92, 104486 , 2023 2023 Citations: 87
Recent advances in automatic modulation classification technology: Methods, results, and prospects Q Zheng, X Tian, L Yu, A Elhanashi, S Saponara International Journal of Intelligent Systems 2025 (1), 4067323 , 2025 2025 Citations: 81
Machine learning techniques for anomaly-based detection system on CSE-CIC-IDS2018 dataset A Elhanashi, K Gasmi, A Begni, P Dini, Q Zheng, S Saponara International Conference on Applications in Electronics Pervading Industry … , 2022 2022 Citations: 74
Robust automatic modulation classification using asymmetric trilinear attention net with noisy activation function Q Zheng, X Tian, Z Yu, M Yang, A Elhanashi, S Saponara Engineering Applications of Artificial Intelligence 141, 109861 , 2025 2025 Citations: 70
Application of complete ensemble empirical mode decomposition based multi-stream informer (CEEMD-MsI) in PM2. 5 concentration long-term prediction Q Zheng, X Tian, Z Yu, B Jin, N Jiang, Y Ding, M Yang, A Elhanashi, ... Expert Systems with Applications 245, 123008 , 2024 2024 Citations: 61
Overview of AI-models and tools in embedded IIoT applications P Dini, L Diana, A Elhanashi, S Saponara Electronics 13 (12), 2322 , 2024 2024 Citations: 57
Developing a real-time social distancing detection system based on YOLOv4-tiny and bird-eye view for COVID-19 S Saponara, A Elhanashi, Q Zheng Journal of Real-Time Image Processing 19 (3), 551-563 , 2022 2022 Citations: 55
TeleStroke: real-time stroke detection with federated learning and YOLOv8 on edge devices A Elhanashi, P Dini, S Saponara, Q Zheng Journal of Real-Time Image Processing 21 (4), 121 , 2024 2024 Citations: 49
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
Early Stroke Detection: A Mobile Application for Real-Time Stroke Diagnosis Using Video and Lightweight Deep Learning
Industry, Institute, or Organisation Collaboration
Hiroshima University
University of Strathclyde
University of Glasgow
Shandong Management University
Society for Imaging Informatics in Medicine -t SIIM
OpenLearning
