Samaneh Saeedinia | Engineering | Best Researcher Award

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Prof.Dr. Samaneh Saeedinia | Engineering | Best Researcher Award

Iran University of Science and Technology | Iran

Dr. Samaneh Alsadat Saeedinia is an accomplished researcher and engineer specializing in Control Electrical Engineering, Computational Neuroscience, Robotics, and Artificial Intelligence. She earned her Ph.D. from Iran University of Science and Technology (IUST), where her thesis focused on designing and stabilizing intelligent decision-making systems to support the treatment of adult focal epilepsy, receiving an excellent grade. She also holds a Master’s degree in Control Electrical Engineering from IUST and a Bachelor’s degree from Imam Khomeini International University. Dr. Saeedinia has extensive experience in signal processing, machine learning, spiking neural networks, EEG and MRI data analysis, and adaptive control systems. She has contributed to numerous high-impact publications in IEEE Transactions, Scientific Reports, and other international journals. Her research interests include computational modeling, intelligent control, data fusion, robotics path planning, and neuroinformatics. Beyond academia, she has led industrial projects in electrical power systems, automation, and instrumentation. She has received multiple awards, including the Khwarizmi International Award and top ranks in academic performance. Dr. Saeedinia is also an experienced educator and mentor, guiding students in advanced control systems, neural networks, and data analysis. Her work bridges engineering, neuroscience, and artificial intelligence, aiming to develop innovative solutions for healthcare and robotic applications.

Profile : Google Scholar

Featured Publications

Saeedinia, S.A., Jahed-Motlagh, M.R., Tafakhori, A., & Kasabov, N.K. (2024). “Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine” in Scientific Reports, 14(1), 10667.

Mohaghegh, M., Saeedinia, S.A., & Roozbehi, Z. (2023). “Optimal predictive neuro-navigator design for mobile robot navigation with moving obstacles” in Frontiers in Robotics and AI, 10, 1226028.

Roozbehi, Z., Narayanan, A., Mohaghegh, M., & Saeedinia, S.A. (2024). “Dynamic-structured reservoir spiking neural network in sound localization” in IEEE Access, 12, 24596–24608.

Saeedinia, S.A., & Tale Masouleh, M. (2022). “The synergy of the multi-modal MPC and Q-learning approach for the navigation of a three-wheeled omnidirectional robot based on the dynamic model with obstacle collision” in Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.

Roozbehi, Z., Narayanan, A., Mohaghegh, M., & Saeedinia, S.A. (2025). “Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks” in IEEE Access.

Abdelouahad Achmamad | Engineering | Best Researcher Award

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Dr. Abdelouahad Achmamad | Engineering | Best Researcher Award

Universite De Mans | France

Dr. Abde Louahad Achmamad is an embedded software engineer and researcher specializing in intelligent systems, AI algorithms, biomedical signal processing, and advanced embedded technologies for electrical, biomedical, and IoT applications. He holds a PhD in Electrical Engineering, complemented by master’s degrees in biomedical engineering and electrical/electronic engineering, supported by earlier qualifications in electromechanics and electronics. His professional experience spans biomedical R&D, embedded system design, AI-based medical diagnostics, MRI segmentation, IoT health monitoring, wearable sensor development, and security applications for STM32 microcontrollers. He has worked with leading institutions and companies across France and Morocco, including STMicroelectronics, Akkodis, Diabtech, Université de Rouen, ENSIAS, and multiple research laboratories. His scientific output includes 102 citations by 95 documents, 16 publications, and an h-index of 6, reflecting contributions in EMG-based diagnostics, phonoangiography, neuromuscular disorder detection, sensor fusion, and few-shot learning for medical imaging. He has also reviewed more than 200 scientific manuscripts for major journals from Springer, Elsevier, and MDPI, demonstrating recognized expertise in signal processing and embedded systems. His research interests include embedded AI, biomedical instrumentation, wearable sensors, IoT-based healthcare, security-focused embedded design, and intelligent diagnostic systems. Dr. Achmamad remains dedicated to advancing high-impact engineering solutions that enhance healthcare, sensing technologies, and intelligent embedded platforms.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Achmamad, A., & Jbari, A. (2020). A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform. Bulletin of Electrical Engineering and Informatics.

Achmamad, A., Belkhou, A., & Jbari, A. (2020). Fast automatic detection of amyotrophic lateral sclerosis disease based on Euclidean distance metric. In 2020 International Conference on Electrical and Information Technologies .

Belkhou, A., Achmamad, A., & Jbari, A. (2019). Classification and diagnosis of myopathy EMG signals using the continuous wavelet transform. In 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science .

Belkhou, A., Achmamad, A., & Jbari, A. (2019). Myopathy detection and classification based on the continuous wavelet transform. Journal of Communications Software and Systems.

Abdelouahad, A., Belkhou, A., Jbari, A., & Bellarbi, L. (2018). Time and frequency parameters of sEMG signal–force relationship. In 2018 International Conference on Optimization and Applications .