Prof. Hedi Sakli | Artificial Intelligence | Research Excellence Award

Published on by Computer Scientists Awards

Prof. Hedi Sakli | Artificial Intelligence | Research Excellence Award

Professor | University of Tunis El Manar | Tunisia

Hedi Sakli is a senior researcher and academic expert in telecommunications, electromagnetics, antennas, wave propagation, and advanced radiofrequency systems, with strong contributions spanning 5G technologies, metamaterials, optical communications, signal processing, and applied artificial intelligence. His research integrates theoretical electromagnetism with practical engineering applications, including IoT, sensor networks, and AI-assisted health and communication systems. He has authored an extensive body of peer-reviewed scientific work with significant international visibility. According to indexed databases, his research impact includes more than 116 Scopus-indexed documents with over 1,197 citations and an h-index of 14, alongside more than 1,755 Google Scholar citations and an h-index of 17, reflecting sustained scholarly influence and research leadership.

Citation Metrics (Scopus)

1250

1000

750

500

250

0

 

 

Citations
1,197

Documents
116

h-index
14

           🟦 Citations   🟥 Documents   🟩 h-index


View Scopus Profile
 View ORCID Profile
 View Google Scholar Profile

Featured Publications

Mr. Abir Das | Artificial Intelligence | Research Excellence Award

Published on by Computer Scientists Awards

Mr. Abir Das | Artificial Intelligence | Research Excellence Award

Siliguri Government Polytechnic College | India

Abir Das is an emerging AI/ML researcher whose work spans deep learning, computer vision, medical imaging, and explainable AI. With a strong foundation in developing end-to-end AI systems, his research focuses on Vision Transformers, self-supervised learning, noisy-label correction, and interpretable models for high-stakes applications such as healthcare, EEG signal analysis, and industrial fault diagnosis. He has contributed as the first author to multiple international journals, working extensively on hybrid deep learning models, CLIP-based zero-shot learning, EEG motor imagery classification, and sensor-driven diagnostic pipelines. His research integrates expertise in PyTorch, TensorFlow, and modern transformer architectures, emphasizing human-centered, reliable, and transparent AI solutions. He has actively explored the intersection of computer vision and embedded systems, enhancing drone autonomy, depth estimation, and real-time object detection, while also contributing to speech technologies through accent-conversion and multimodal learning. His scientific output includes publications in reputable venues such as Scientific Reports, MDPI Sensors, and Computers, Materials & Continua. His growing scholarly impact is reflected in Scopus metrics: 11 citations from 11 documents with an h-index of 1, and Google Scholar metrics: 12 citations, h-index 1, i10-index 1. His work continues to advance practical and theoretically grounded AI methodologies, blending deep learning innovations with real-world applications across biomedical imaging, EEG analysis, and industrial AI systems.

Publication Profile

Scopus | Google Scholar

Featured Publications

Das, A., Singh, S., Kim, J., Ahanger, T. A., & Pisa, A. A. (2025). Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models. Scientific Reports, 15(1), 27161.

Zereen, A. N., Das, A., & Uddin, J. (2024). Machine fault diagnosis using audio sensor data and explainable AI techniques: LIME and SHAP. Computers, Materials & Continua, 80(3).

Das, S. S. A. (2025). Few-shot and zero-shot learning for MRI brain tumor classification using CLIP and Vision Transformers. Sensors, 25(23), 7341.

Assist. Prof. Dr. Mehtab Alam | Artificial Intelligence | Best Researcher Award

Published on by Computer Scientists Awards

Assist. Prof. Dr. Mehtab Alam | Artificial Intelligence | Best Researcher Award

Assist. Prof. Dr. Mehtab Alam |Assistant Professor | Delhi University | India

Dr. Mehtab Alam is an accomplished IT professional and academic specializing in Artificial Intelligence (AI), Internet of Things (IoT), Cyber Forensics, and Information Security. His research primarily focuses on developing AI-based smart IoT frameworks for intelligent healthcare systems, with a strong emphasis on predictive modeling, machine learning integration, and cloud-based data analytics. His scholarly contributions demonstrate a multidisciplinary approach combining computer science, data-driven healthcare innovation, and digital transformation. He has explored diverse research areas including smart city technologies, blockchain applications in e-governance, cybersecurity frameworks, and the application of swarm intelligence in network optimization. Dr. Alam has published extensively in reputed international journals and conferences, contributing to advancements in AI-driven sustainable systems and smart healthcare solutions. His works reflect technical depth and practical applicability, addressing modern challenges in digital infrastructure, public health informatics, and secure communication systems. He has authored 15 Scopus-indexed publications, with 30 Scopus citations and an h-index of 4. On Google Scholar, his research has received 256 citations with an h-index of 10 and an i10-index of 11, showcasing his growing academic influence.

Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications

Alam, M., Khan, E. R., Alam, A., Siddiqui, F., & Tanweer, S. (2023). The DIABACARE CLOUD: Predicting diabetes using machine learning. Acta Scientiarum Technology, 46(1).

Alam, M., Khan, I. R., Alam, A., Siddiqui, F., & Tanweer, S. (2023). Smart healthcare: Making medicine intelligent. Journal of Propulsion Technology, 44(3).

Alam, M., Khan, R., Alam, A., Siddiqui, F., & Tanweer, S. (2023). AI for sustainable smart city healthcare. China Petroleum Processing and Petrochemical Technology Catalyst Research, 23(2), 2245–2258.

Ansari, A. A., Narain, L., Prasad, S. N., & Alam, M. (2022). Behaviour of motion of infinitesimal variable mass oblate body in the generalized perturbed circular restricted three-body problem. Italian Journal of Pure and Applied Mathematics, 47, 221–239.

Alam, M., Parveen, S. (2021). Shipment delivery and COVID-19: An Indian context. International Journal of Advanced Engineering Research and Science, 8(8), 145–154.

Assist. Prof. Dr. Mustaqeem Khan | Artificial intelligence | Best Researcher Award

Published on by Computer Scientists Awards

Assist. Prof. Dr. Mustaqeem Khan | Artificial intelligence | Best Researcher Award

Assist. Prof. Dr. Mustaqeem Khan | Assistant Professor | United Arab Emirates University | United Arab Emirates

Academic Background

Dr. Mustaqeem Khan is a distinguished researcher and academic in the field of Artificial Intelligence and Signal Processing. He earned his Doctorate in Software Convergence from Sejong University, South Korea, where his research focused on emotion recognition using deep learning. He also holds a Master’s degree in Computer Science from Islamia College Peshawar, Pakistan, where he was awarded a Gold Medal for academic excellence, and a Bachelor’s degree in Computer Science from the University of Agriculture, Peshawar. Dr. Khan’s scholarly impact is reflected in his remarkable research record, with Scopus indexing 47 documents and over 2,412 citations, resulting in an h-index of 20. On Google Scholar, his work has gained over 2,934 citations, maintaining an h-index of 21 and an i10-index of 31, positioning him among the top two percentage scientists globally.

Research Focus

His research primarily explores Speech and Audio Signal Processing, Emotion Recognition, and Deep Learning. Dr. Khan’s studies integrate multi-modal data analysis through advanced architectures, such as CNNs and Transformers, for applications in speech emotion recognition, computer vision, and energy analytics.

Work Experience

Dr. Khan serves as an Assistant Professor at the United Arab Emirates University, contributing to teaching, research supervision, and curriculum development. Previously, he worked as a Postdoctoral Fellow and Lab Coordinator at the Mohamed Bin Zayed University of Artificial Intelligence, where he collaborated with the Technical Innovation Institute on drone detection systems and managed multidisciplinary AI research teams. Before that, he gained substantial academic and research experience as a Research Assistant at Sejong University and as a Lecturer at Islamia College Peshawar, mentoring students in core computer science and artificial intelligence subjects.

Key Contributions

Dr. Khan has developed several advanced deep learning models, including hybrid attention transformers, multimodal cross-attention networks, and ensemble architectures for audio-visual recognition tasks. His work has contributed to advancements in emotion recognition, drone-based surveillance, and smart city analytics. He has also participated in major funded projects supported by the National Research Foundation of Korea and the Technology Innovation Institute, UAE.

Awards & Recognition

He has been honored with multiple distinctions, including Best Paper Awards, an Outstanding Research Award during his Ph.D., and recognition as a Gold Medalist for academic performance. His inclusion among the Top 2% Scientists (2023–2024) underscores his exceptional research influence and scholarly excellence.

Professional Roles & Memberships

Dr. Khan is an editorial board member and associate editor for several international journals, including the Annals of Applied Sciences and the European Journal of Mathematical Analysis. He serves as a reviewer for over 35 prestigious journals such as IEEE Access, Applied Soft Computing, and Knowledge-Based Systems, actively contributing to academic quality and peer review.

Profile

Scopus | Google Scholar | ORCID

Featured Publications

Khan, M., Ahmad, J., El Saddik, A., & Gueaieb, W. (2025). Joint Multi-Scale Multimodal Transformer for Emotion Using Consumer Devices. IEEE Transactions on Consumer Electronics.

Khan, M., Tran, P. N., Pham, N. T., & Othmani, A. (2025). MemoCMT: Multimodal Emotion Recognition Using Cross-Modal Transformer-Based Feature Fusion. Nature Scientific Reports.

Khan, M., Ahmad, J., El Saddik, A., & Gueaieb, W. (2024). Drone-HAT: Hybrid Attention Transformer for Complex Action Recognition in Drone Surveillance Videos. Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Khan, M., Kwon, S. (2021). Optimal Feature Selection Based Speech Emotion Recognition Using Two-Stream Deep Convolutional Neural Network. International Journal of Intelligent Systems.

Khan, M., Kwon, S. (2021). Att-Net: Enhanced Emotion Recognition System Using Lightweight Self-Attention Module. Applied Soft Computing.

Impact Statement / Vision

Dr. Mustaqeem Khan envisions advancing AI systems capable of understanding human emotions and behaviors with precision and empathy. His goal is to integrate deep learning and multimodal intelligence into real-world applications that enhance human–machine interaction, healthcare, and smart technologies. His ongoing commitment to innovation continues to shape the future of intelligent computing and global research collaboration.