Maedeh Azadi Moghadam | Artificial Intelligence | Best Researcher Award

Published on by Scientific World Research Awards

Dr. Maedeh Azadi Moghadam | Artificial Intelligence | Best Researcher Award

Biomedical Engineer | Semnan University | Iran

Dr. Maedeh Azadi Moghadam is an emerging researcher whose work advances the fields of biomedical engineering, neurotechnology, and human–machine interaction, with a particular focus on developing more reliable and human-centered brain–computer interface (BCI) systems. Her research interests span neural signal processing, SSVEP-based BCI optimization, cognitive fatigue detection, biomarker-based performance measurement, and the integration of physiological signals into more adaptive computational models. She is especially interested in understanding how fatigue and cognitive variability influence BCI accuracy, and her work aims to design intelligent systems capable of adjusting in real time to user states, ultimately improving usability for rehabilitation, assistive technologies, and next-generation neuroengineering applications. Dr. Moghadam’s research skills include biosignal analysis, EEG processing, feature extraction, algorithmic modeling, quantitative measurement techniques, and scientific writing, demonstrating her multidisciplinary strengths across engineering and neuroscience. According to Scopus, she has 3 indexed documents, 2 citations, and an h-index of 1, reflecting growing visibility and early academic impact in her domain. Although no formal awards or honors are listed for her in the available Scopus record, her contributions to innovative metrics—such as a continuous fatigue index for SSVEP-based BCI performance—highlight her potential for future recognition in neurotechnology and biomedical measurement science. Her publications demonstrate a commitment to improving the efficiency, accuracy, and adaptability of neuroengineering systems, particularly those intended for people with motor impairments or communication limitations. In conclusion, Dr. Maedeh Azadi Moghadam represents a promising researcher whose interdisciplinary work is helping shape the future of intelligent BCIs, cognitive state monitoring, and biomedical signal-driven technologies. Her expanding scientific contributions, combined with her advancing research skill set, position her for continued impact in the global scientific community and future leadership in neurotechnology innovation.

Profiles: Scopus | Google Scholar | LinkedIn

Featured Publications

Azadi Moghadam, M., & Maleki, A. (2023). Fatigue factors and fatigue indices in SSVEP-based brain–computer interfaces: A systematic review and meta-analysis. Frontiers in Human Neuroscience, 17, 1248474. Citations: 33

Maleki, A., & Azadimoghadam, M. (2022). Fatigue assessment using frequency features in SSVEP-based brain–computer interfaces. Iranian Journal of Biomedical Engineering, 16(3), 229–240.
Citations: 4

Moghadam, M. A., & Maleki, A. (2023). Fatigue detection in SSVEP-based BCIs using biomarkers: A comparative study. 2023 31st International Conference on Electrical Engineering (ICEE), 496–500. Citations: 2

Azadi Moghadam, M., & Maleki, A. (2024). Comparative study of frequency recognition techniques for steady-state visual evoked potentials according to the frequency harmonics and stimulus number. Journal of Biomedical Physics and Engineering. Citations: 1

Moghadam, M. A., & Maleki, A. (2025). A continuous fatigue index based on biomarkers for SSVEP-based brain–computer interfaces. Measurement, 118598.

The Dr. Maedeh Azadi moghadam’s research advances global innovation in neurotechnology by improving the accuracy, stability, and human-centered design of brain–computer interface systems through biomarker-driven fatigue detection and advanced signal analysis. By enhancing the reliability of assistive technologies and cognitive monitoring tools, the nominee’s work contributes meaningful benefits to science, healthcare, and industry, ultimately supporting more accessible, intelligent, and high-performing human–machine interaction solutions for society.

 

Maliki Moustapha | Computer Science | Best Researcher Award

Published on by Scientific World Research Awards

Dr. Maliki Moustapha | Computer Science | Best Researcher Award

PhD | Erciyes University | Turkey

Dr. Maliki Moustapha, an accomplished researcher from Erciyes University, is recognized for his expertise in Artificial Intelligence (AI), Deep Transfer Learning, and Data Engineering, with a strong focus on the integration of intelligent algorithms and data-driven models to address real-world computational challenges. His academic background is rooted in computer science and engineering, where he developed advanced skills in machine learning, neural networks, data mining, and smart systems design. Professionally, Dr. Moustapha has been actively engaged in both research and academic mentorship, contributing to the development of innovative solutions in AI-powered automation, pattern recognition, and intelligent monitoring systems. His major research interests encompass computer vision, deep learning model optimization, spatiotemporal data analysis, and Internet of Things (IoT)-based smart healthcare systems. Among his most cited contributions is the publication titled “A Novel YOLOv5 Deep Learning Model for Handwriting Detection and Recognition” in the International Journal on Artificial Intelligence Tools (2023), which demonstrates superior accuracy and efficiency in image recognition. He has also published influential works on spatial and spatiotemporal clustering algorithms and IoT-based patient monitoring, bridging the gap between data intelligence and applied computing. His research skills span across Python programming, neural network modeling, big data analytics, data preprocessing, and model training for intelligent systems. Though early in his academic journey, Dr. Moustapha has earned recognition for his impactful work, showing promising potential in advancing AI technologies. According to Scopus and Google Scholar, he has achieved 9 citations, an h-index of 1, and several published documents reflecting growing international recognition. Dr. Moustapha’s research continues to contribute meaningfully to the fields of artificial intelligence and computational intelligence. In conclusion, his innovative approach, interdisciplinary mindset, and technological vision position him as a forward-thinking researcher committed to shaping the next generation of intelligent data systems and AI-driven innovations.

Profiles: ORCID | Google Scholar

Featured Publications

1. Moustapha, M., Taşyürek, M., & Öztürk, C. (2023). A novel YOLOv5 deep learning model for handwriting detection and recognition. International Journal on Artificial Intelligence Tools, 32(04), 2350016.

2. Moustapha, M. (2024). Spatial and spatiotemporal clustering algorithms in data mining. In Proceedings of the 3rd International Conference on Data and Electronics and Computing (ICDEC).

3. Moustapha, M. (2019). Alternative approach of patient monitoring system based on Internet of Things. In Proceedings of the II. International Science and Academic Congress (INSAC).

Nassim Bout | Computer Science | Best Researcher Award

Published on by Scientific World Research Awards

Mr. Nassim Bout | Computer Science | Best Researcher Award

Senior Central Officer |  Hassan II University of Casablanca | Morocco

Mr. Nassim Bout is an accomplished adjunct professor and researcher in computer engineering, healthcare information systems, and artificial intelligence, recognized for his innovative contributions to AI-driven healthcare solutions, digital hospital services, and bioinformatics applications. He holds a Ph.D. in Engineering Sciences (Computer Engineering) from ENSEM, Hassan II University, Casablanca (2021–2024), and a Research Master’s in Management and Modeling of Complex Information Systems from ENSIAS, Mohammed V University, Rabat (2019–2021), reflecting a strong academic foundation in both technical and management aspects of complex information systems. Professionally, he has served as Senior Central Officer at the Ministry of Interior, coordinating digital transformation and IT services, as well as Product Owner at Netopia Solutions, where he led IT ecosystem studies and consulting for healthcare institutions. He has also contributed to Afrihealth Solutions as a software engineer, implementing hospital information systems across multiple regions. His research interests include AI integration in patient-centered healthcare, enterprise architecture in oncology, bioinformatics, and healthcare digital transformation. Mr. Bout possesses advanced skills in artificial intelligence and machine learning (deep learning, NLP, computer vision), algorithms and programming (C, C++, Python, JavaScript, PHP, Django), system design and architecture (TOGAF, UML, ArchiMate), project management frameworks (Agile, SCRUM, KANBAN), database management (SQL, PostgreSQL, Oracle DB), and bioinformatics tools, alongside strong communication and organizational abilities. He has an extensive publication record in reputed journals and conferences, including Discover Internet of Things, IJECE, Springer Lecture Notes, and ISDA proceedings, with citations by 1 document, 1 document, and an h-index reflecting his scholarly impact. He actively participates in professional societies as a reviewer for TELKOMNIKA, Digital Health, Cyber-physical Systems, and Network Modeling Analysis in Health Informatics and leads social inclusion initiatives through e-MOBADARA. Awards and honors include international conference recognitions and certifications in project management, scientific publishing, and bioinformatics training. In conclusion, Mr. Bout demonstrates exceptional interdisciplinary expertise, leadership, and scholarly influence, with strong potential to advance global research in AI-driven healthcare systems, mentor emerging researchers, and contribute to high-impact publications, international collaborations, and societal development through innovative healthcare technologies.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

  1. Bout, N., Khazaz, R., Azougaghe, A., El-Hfid, M., Abik, M., & Belhadaoui, H. (2021). Implementation of the business process model and notation in the modelling of patient’s clinical workflow in oncology. International Conference on Intelligent Systems Design and Applications, 576–586. Citations: 2

  2. Bout, N., Moukhliss, G., Belhadaoui, H., Afifi, N., & Abik, M. (2025). Integrating emotional AI, IoT, and robotics for patient-centered healthcare: Challenges and future directions. Discover Internet of Things, 5(75), 18. Citations: 1

  3. Bout, N., Azougaghe, A., Belhadaoui, H., El-Hfid, M., & Khazaz, R. (2022). Business process model and notation implemented in the hospital, any use? Case of the patient clinical workflow. Journal of Network and Innovative Computing, 10, 8–8. Citations: 1

  4. Bout, N., Ouzayd, F., & Retmi, K. (2025). Erratum for Role of Hospital Digital Services in Improvement of Clinical Regime of Patients [Health Tech Asmnt Act. 2021; 5(1)]. Health Technology Assessment in Action.

  5. Bout, N., Belhadaoui, H., Afifi, N., Abik, M., El-Hfid, M., & Azougaghe, A. (2025). Towards a standardized enterprise architecture: Enhancing decision-making in oncology multidisciplinary team meetings. International Journal of Electrical and Computer Engineering (IJECE), 15(2), 2224–2236.