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.

Ashutosh K Giri | Engineering | Best Researcher Award

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Dr. Ashutosh K Giri | Engineering | Best Researcher Award

Government Engineering College Bharuch | India

Dr. Ashutosh K. Giri is an Assistant Professor in the Department of Electrical Engineering at Government Engineering College, Bharuch, Gujarat, India, with over 20 years of teaching and research experience. He earned his Ph.D. from Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, focusing on control algorithms for power quality issues in wind-based distributed power generation systems. He also holds an M.E. in Electrical Engineering from L.D. College of Engineering, Gujarat University, and a B.Tech in Electrical Engineering from G.B. Pant University of Agriculture and Technology, Pantnagar. Dr. Giri’s research interests include renewable energy power converters, microgrid topology, operation and control, power quality enhancement, electric vehicle technologies, and adaptive control algorithms. He has published extensively in leading journals including IEEE, IET, Springer, Wiley, Elsevier, and Taylor & Francis, authored and edited multiple international books, and contributed to state- and national-level funded research projects. His work has been cited 478 times across 234 documents, with an h-index of 14, reflecting his significant impact in the field. Recognized among the top 2% of scientists globally, Dr. Giri continues to advance research and innovation in distributed generation systems and renewable energy, mentoring students and contributing to the scientific community through high-quality research and academic leadership.

Profiles : Scopus | Orcid | Google Scholar

Featured Publications

Joshi, D., Deb, D., & Giri, A. K. (2025). “Metaheuristic adaptive input output feedback linearization control for BLDC motor drive” in IEEE Transactions on Consumer Electronics.

Kanojia, S. S., Suthar, B. N., & Giri, A. K. (2025). “Hybrid optimization approach for optimal location of electric vehicle charging station (EVCS) in distribution network considering voltage stability” in Smart Grids and Sustainable Energy.

Kundu, S., Giri, A. K., & Kadiyan, S. (2025). “An adaptive mixed-step size normalized least means fourth control approach for stand-alone power generation system considering dynamic conditions” in IEEE Journal of Emerging and Selected Topics in Power Electronics.

Bhatt, K. A., Bhalja, B. R., & Giri, A. K. (2024). “Controlled single-phase auto-reclosing technique for shunt reactor compensated lines in electrical energy systems transmission lines” in International Journal of Ambient Energy.

Parmar, D. B., & Giri, A. K. (2024). “Integration of field-oriented and steady-state linear Kalman filter control in PMSG-based grid-connected system for improving voltage control and power balance operation” in Electrical Engineering.

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 .

Mittar Pal | Engineering | Women Researcher Award

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Dr. Mittar Pal | Engineering | Women Researcher Award

Deenbandhu Chhotu Ram University of Science and Technology | India

Dr. Mittar Pal is an experienced academic and researcher in Electronics & Communication Engineering with over twelve years of teaching and research experience, specializing in renewable energy systems and AI‑based engineering applications. He holds a B.Tech in ECE (2003), M.Tech in Nanoscience & Technology (2013) and completed his Pre‑PhD in ECE (2019) from renowned Indian institutions, and recently earned his PhD on barrier identification and prioritisation for solar photovoltaic system deployment in dairy farming in India (2023). His work on techno‑economic analysis of photovoltaic systems in dairy farming and optimization of utility‑integrated solar PV systems has appeared in SCI and Scopus‑indexed outlets, and he has supervised numerous B.Tech and M.Tech projects while contributing to NBA documentation and curriculum development. He is UGC‑NET qualified for Electronic Science (Assistant Professor). His research interests span signal & system theory, digital electronics, power electronics, renewable solar energy modelling (using MATLAB, HOMER Pro, SAM, RETScreen), AI/ML, generative AI, IoT and pattern recognition. With a growing scholarly profile (h‑index ~ 5 and citation count ~5 as per publicly listed profiles), he continues to publish, present at international conferences, and drive innovation in teaching and research. His dedication to transforming traditional dairy farming through solar PV integration underscores his commitment to sustainability and engineering education.

Profile: Scopus 

Featured Publications

Mittarpal. (2018). “Modeling and Simulation of Solar Photovoltaic Module using Matlab-Simulink.” International Journal for Research in Engineering Application & Management .

Mittarpal. (2018). “Modeling Design and Simulation of Photovoltaic Module with Tags Using Simulink.” International Journal of Advance & Innovative Research.

Mittarpal, & Asha. (2018). “Simulation and Modeling of Photovoltaic Module Using Simscape.” National Conference on Advances in Power, Control & Communication Systems .

Mittarpal, Naresh Kumar, & Priyanka Anand, Sunita. (2017). “Realization of Flip Flops Using LabVIEW and MATLAB.”

Mittarpal, Naresh Kumar, & Sunita Rani. (2017). “Realisation of Digital Circuits Systems Using Embedded Function on MATLAB.” International Journal of Engineering and Technology.

Mohammad Zeyauddin | Qualitative Research | Best Researcher Award

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Assist. Prof. Dr. Mohammad Zeyauddin | Qualitative Research | Best Researcher Award

Jubail Industrial College | Saudi Arabia

Dr. Mohammad Zeyauddin is an accomplished Assistant Professor in the Department of General Studies, Mathematics Section, at Jubail Industrial College, Saudi Arabia. With over fourteen years of teaching experience at undergraduate and postgraduate levels, he has contributed significantly to curriculum development, academic mentorship, and student engagement. Dr. Zeyauddin holds a Ph.D. in Applied Mathematics from Banaras Hindu University, India, with research focused on spatially homogeneous and anisotropic Bianchi Type V cosmological models in general relativity and scalar-tensor theories of gravitation. He also completed a Post-Doctoral Fellowship at the Joint Institute for Nuclear Research, Dubna, Russia, specializing in relativistic cosmology. His teaching repertoire spans Linear Algebra, Differential and Integral Calculus, Differential Equations, Complex and Functional Analysis, Tensor Analysis, Probability and Statistics, and Computational Mathematics. He has published over 30 international research papers and serves as a reviewer for the American Mathematical Society. His research interests include non-linear differential equations, general relativity, cosmology, and scalar-tensor theories of gravitation. Recognized for academic excellence, he has received UGC Junior and Senior Research Fellowships, postdoctoral fellowships, and appreciation for curriculum contributions. Dr. Zeyauddin continues to inspire students and peers through innovative teaching, research, and contributions to mathematical sciences.

Profile : Orcid

Featured Publications

Dayanandan, B., Pradhan, A., Zeyauddin, M., & Banerjee, A. (2025). Quark stars with strongly interacting quark matter in energy-momentum squared gravity. International Journal of Geometric Methods in Modern Physics.

Pradhan, A., Zeyauddin, M., Dixit, A., & Krishnannair, S. (2025). Dust-fluid accelerating flat cosmological models in f(R,T,Lm)-gravity with observational constraints. International Journal of Geometric Methods in Modern Physics.

Pradhan, A., Zeyauddin, M., Dixit, A., & Ghaderi, K. (2025). Phantom dark energy behavior in Weyl type f(Q,T) gravity models with observational constraints. Universe, 11(8).

Pradhan, A., Dayanandan, B., Zeyauddin, M., & Banerjee, A. (2025). On the possible existence of wormholes in Rastall–Rainbow gravity. International Journal of Modern Physics A.

Pradhan, A., Dixit, A., Zeyauddin, M., & Krishnannair, S. (2024). A flat FLRW dark energy model in f(Q,C)-gravity theory with observational constraints. International Journal of Geometric Methods in Modern Physics.

Syed Abrar Ahmed | Optical Communication | Best Researcher Award

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Mr. Syed Abrar Ahmed | Optical Communication | Best Researcher Award

Presidency University | India

Mr. Syed Abrar Ahmed is an Assistant Professor in the Department of Electronics and Communication Engineering at Presidency University, Bangalore, with over 14 years of academic experience. He holds an M.Tech in VLSI Design and Embedded Systems from Dr. Ambedkar Institute of Technology, Bangalore, under Visvesvaraya Technological University (VTU), and a B.E. in Electronics and Communication Engineering from Ghousia College of Engineering, Ramanagaram. His teaching expertise spans Network Analysis, Embedded System Design, Microcontrollers, Microprocessors, and Analog and Digital Electronics. His research contributions focus on embedded systems, Internet of Things (IoT), machine learning applications in cognitive radio, and assistive technologies for visually impaired individuals. He has published conference papers, journal articles, and a book chapter, including contributions to Scopus-indexed proceedings and reputed journals such as the International Journal of Recent Technology and Engineering. He has guided numerous undergraduate projects in microcontrollers, robotics, IoT, GSM/GPS systems, and FPGA-based designs, and has actively participated in faculty development programs, workshops, and seminars on advanced topics. His career is distinguished by dedication to academic excellence, innovation in teaching, and contributions to emerging fields in electronics and communication engineering.

Profiles : Scopus | Orcid

Featured Publications

“QML-Based Abstraction Techniques for Predictive Modeling in Banking”

“Performance analysis of a PPM-OOK based hybrid modulation scheme for Satellite-to-Underwater FSO communication”

“Decision Tree Based 5modular Redundancy Algorithm for Fault Identification in Full Adder Circuit”

“5G’s Impact on the Evolution of Smart Cities”

“Multifunctional Frequency Management With 5g Cognitive Networking”

“A Glimpse of the 5G-Enabled Modern City”

“Third Eye A Writing Aid for Visually Impaired People”

Hugo Hervé-Côte – Machine Learning and AI Applications – Best Researcher Award 

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Mr. Hugo Hervé-Côte - Machine Learning and AI Applications - Best Researcher Award 

Ecole de Technologie Supérieure - Canada 

Author Profile

Early Academic Pursuits

Mr. Hugo Hervé-Côté embarked on his academic journey with a strong foundation in mechanical and industrial engineering. He began his education at the prestigious Ecole Nationale Supérieure des Arts et Métiers (ENSAM) in Angers, France, where he pursued a Diploma in Mechanical, Industrial, and Energy Engineering. During his time at ENSAM, from September 2019 to July 2021, Hugo honed his expertise in various engineering disciplines, setting the stage for his future endeavors in artificial intelligence and machine learning.

Following his graduation from ENSAM, Hugo continued to build on his technical knowledge and skills by pursuing a Master's degree in Automated Production Engineering (M.Sc.A) at the Ecole de Technologie Supérieure (ETS) in Montreal, Canada. From September 2021 to December 2023, he delved deeper into advanced engineering concepts and specialized in machine learning and computer vision. This dual-degree experience equipped him with a robust academic background and practical skills, making him a versatile engineer ready to tackle complex challenges in the industry.

Professional Endeavors

Mr. Hugo's professional journey has been marked by significant contributions to the fields of non-destructive testing (NDT) and industrial inspection. His career began with a notable role as an Artificial Intelligence Engineer at EddyFi Technologies in Quebec, Canada, from December 2023 to March 2024. In this position, Hugo focused on the development and implementation of machine learning models for analyzing eddy current images in non-destructive testing. He was instrumental in creating a user-friendly interface for visualizing and labeling complex magnetic data, and he developed a comprehensive data pipeline for training, validating, and testing machine learning models. His work culminated in a proof of concept that demonstrated the effectiveness of these models in real-world applications.

Prior to his role at EddyFi Technologies, Hugo gained valuable experience as a Student Researcher in Ultrasonic Inspection using Machine Learning at ETS and in collaboration with Nucleom. From September 2021 to December 2023, he worked on detecting defects in ultrasonic images, developing a standardized database for ultrasonic imaging, and addressing industrial challenges through regular project progress meetings with Nucleom and EddyFi. His efforts contributed to the advancement of ultrasonic inspection techniques and the integration of machine learning in this domain.

Contributions and Research Focus

Mr. Hugo's research focus has been centered on the application of machine learning and artificial intelligence in non-destructive testing and industrial inspection. His master's thesis involved the development of a tool for analyzing ultrasonic images using machine learning. This project, conducted from September 2021 to December 2023, included the acquisition of defect data from welds using Full Matrix Capture (FMC) and the development of algorithms for image reconstruction and defect detection. Hugo also created a significant labeled database of over 100,000 images, which served as a foundation for training deep learning models. His work was presented at conferences such as OnDuty in Windsor, ON (2022) and Toronto, ON (2023), and was published in the scientific journal Ultrasonics.

Hugo's research contributions extend to developing post-processors for CNC machines and designing cryogenic tool mounts. His projects have involved the practical application of engineering principles to solve real-world problems, demonstrating his ability to bridge the gap between theory and practice.

Accolades and Recognition

Mr. Hugo Hervé-Côté's work has been recognized both academically and professionally. His publication in Ultrasonics, titled "Automatic flaw detection in sectoral scans using machine learning," has garnered attention in the scientific community, showcasing his expertise in applying advanced technologies to improve industrial inspection processes. This publication is a testament to his dedication to research and innovation in the field of non-destructive testing.

Throughout his academic and professional career, Hugo has demonstrated excellence in his field, earning the respect of his peers and supervisors. His involvement in various projects and his ability to present his findings at conferences highlight his commitment to advancing knowledge and contributing to the engineering community.

Impact and Influence

Mr. Hugo's work has had a significant impact on the field of non-destructive testing and industrial inspection. By integrating machine learning techniques into traditional inspection methods, he has contributed to the development of more accurate and efficient defect detection processes. His research on ultrasonic imaging and eddy current testing has the potential to revolutionize how industries approach quality control and maintenance, leading to safer and more reliable infrastructure.

Hugo's contributions have also influenced the development of standardized databases and formats for storing and analyzing inspection data, facilitating collaboration and knowledge sharing among researchers and industry professionals. His efforts to create user-friendly interfaces and data pipelines have made advanced technologies more accessible to engineers and technicians, promoting the adoption of machine learning in industrial applications.

Legacy and Future Contributions

Mr. Hugo Hervé-Côté's legacy lies in his innovative approach to solving complex engineering problems through the application of machine learning and artificial intelligence. His work has paved the way for future advancements in non-destructive testing and industrial inspection, setting a high standard for integrating cutting-edge technologies into practical applications.

Looking ahead, Hugo's future contributions are likely to continue shaping the field of engineering. His strong foundation in mechanical and industrial engineering, combined with his expertise in artificial intelligence, positions him as a leader in the development of next-generation inspection techniques. As industries increasingly rely on data-driven solutions, Hugo's work will remain at the forefront of technological innovation, driving progress and improving safety and efficiency across various sectors.

Notable Publication