Hamed Khodadadi | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Hamed Khodadadi | Artificial Intelligence | Best Researcher Award

Faculty Member at Khomeinishahr Branch, Islamic Azad University, Iran

Dr. Hamed Khodadadi is an accomplished researcher and academic with extensive expertise in biomedical engineering, control systems, and machine learning, particularly in healthcare applications. His work focuses on developing advanced computer-aided diagnosis systems for detecting diseases such as cancer, brain disorders, cardiovascular conditions, ADHD, Parkinson’s, and Schizophrenia. He has also contributed significantly to biomedical control systems, medical drug dosing strategies, and applications of chaos theory in medical research. With a strong background in intelligent modeling, nonlinear and adaptive control, and optimization techniques, Dr. Khodadadi has published widely and earned multiple prestigious awards recognizing his impact. His research has not only advanced scientific understanding but also demonstrated practical value through patents and innovative devices. Alongside research, he has mentored numerous graduate and doctoral students, demonstrating dedication to academic growth and leadership. His combination of innovation, productivity, and mentorship positions him as a highly influential figure in biomedical engineering and applied machine learning.

Professional Profile 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Hamed Khodadadi holds a Ph.D. in Electrical Engineering with a specialization in Control Systems from Azad University, Science and Research Branch, Tehran. His doctoral research focused on extracting nonlinear indices for image patterns and evaluating their application in cancer tumor control, bridging the gap between control theory and biomedical diagnosis. He earned his M.Sc. in Electrical Engineering, also in Control Systems, where his thesis involved designing and constructing a two-degree-of-freedom inertial stabilized platform, showcasing his strong foundation in system modeling and control. His academic journey began with a B.Sc. in Electrical Engineering at Iran University of Science and Technology, where he worked on PID controller design for pan-tilt movement in a gimbal system. This educational progression demonstrates a consistent focus on control systems with increasing application toward biomedical challenges, reflecting his ability to integrate engineering principles into healthcare innovations. His education has provided the solid technical base underpinning his interdisciplinary research career.

Experience

Dr. Khodadadi has over a decade of academic and research experience, serving as Assistant Professor and later Associate Professor at Azad University, Khomeinishahr Branch, where he supervises M.Sc. and Ph.D. students. His work includes designing advanced computer-aided diagnosis systems using biomedical signals and images for applications in cancer, cardiovascular disorders, ADHD, Parkinson’s, and Schizophrenia. He has also applied advanced control methods such as nonlinear, adaptive, fuzzy, and model predictive control to medical drug dosing, robotics, and industrial systems. His experience extends to the construction of biomedical and engineering devices, including prosthetic hands and robotic platforms. In addition to teaching graduate and undergraduate courses, he has actively guided thesis projects, contributing to the growth of young researchers. He has also undertaken collaborative roles in collecting biomedical databases, such as cardiovascular biomarkers and EEG signals, supporting clinical research. His broad experience demonstrates both depth in biomedical applications and versatility across engineering and industrial domains.

Research Focus

Dr. Khodadadi’s research centers on biomedical engineering, control systems, and machine learning, with a strong emphasis on healthcare applications. His work integrates computational intelligence, signal and image processing, and control theory to design advanced computer-aided diagnosis systems for life-threatening diseases, including various forms of cancer, brain disorders, and cardiovascular conditions. He has pioneered the application of nonlinear control, adaptive control, and metaheuristic optimization in medical drug dosing and disease modeling, contributing to precision medicine. Additionally, his research explores chaos theory and its role in biomedical image analysis, providing novel tools for early disease detection. He also investigates intelligent optimization and robust control techniques for diverse engineering applications, from robotics and power systems to industrial processes. His interdisciplinary focus blends theory with practical innovation, producing outcomes that advance both medical research and engineering systems. Ultimately, his research vision aims to improve diagnostic accuracy, treatment strategies, and patient outcomes through advanced engineering methods.

Award and Honor

Dr. Khodadadi has been recognized through numerous awards and honors that highlight his excellence in research, innovation, and mentorship. He has received multiple Best Researcher Awards at Azad University, including recognition at both departmental and institutional levels. His international visibility is reflected in honors such as Best Oral Presentation at the International Conference of Research in Europe and being a finalist for the Best Student Award at an IEEE international conference. He has also received recognition for supervising graduate theses with strong industrial impact, reflecting the practical value of his mentorship. His academic achievements include top rankings in national and Ph.D. entrance examinations, along with an Exceptional Talents Award early in his career. Furthermore, he earned the Best International Book Award at a university research festival, showcasing his contributions to scientific literature. Collectively, these accolades underscore his sustained contributions to advancing biomedical engineering, control systems, and healthcare-focused machine learning research.

Publication Top Notes

  • Title: Adaptive super-twisting non-singular terminal sliding mode control for tracking of quadrotor with bounded disturbances
    Authors: H. Ghadiri, M. Emami, H. Khodadadi
    Year: 2021
    Citations: 95

  • Title: Self-tuning PID controller design using fuzzy logic for half car active suspension system
    Authors: H. Khodadadi, H. Ghadiri
    Year: 2018
    Citations: 90

  • Title: Heart arrhythmia diagnosis based on the combination of morphological, frequency and nonlinear features of ECG signals and metaheuristic feature selection algorithm
    Authors: V. Mazaheri, H. Khodadadi
    Year: 2020
    Citations: 83

  • Title: Robust control and modeling a 2-DOF inertial stabilized platform
    Authors: H. Khodadadi, M.R.J. Motlagh, M. Gorji
    Year: 2011
    Citations: 78

  • Title: The Diagnosis of Attention Deficit Hyperactivity Disorder Using Nonlinear Analysis of the EEG Signal
    Authors: Y. Kiani, A.A. Rastegari, H. Khodadadi
    Year: 2019
    Citations: 72

  • Title: Human brain tumor diagnosis using the combination of the complexity measures and texture features through magnetic resonance image
    Authors: S. Salem Ghahfarrokhi, H. Khodadadi
    Year: 2020
    Citations: 54

  • Title: The effects of poplar bark and wood content on the mechanical properties of wood-polypropylene composites
    Authors: V. Safdari, H. Khodadadi, S.K. Hosseinihashemi, E. Ganjian
    Year: 2011
    Citations: 53

  • Title: Fuzzy logic self-tuning PID control for a single-link flexible joint robot manipulator in the presence of uncertainty
    Authors: A. Dehghani, H. Khodadadi
    Year: 2015
    Citations: 41

  • Title: Designing a Neuro-Fuzzy PID Controller Based on Smith Predictor for Heating System
    Authors: A. Dehghani, H. Khodadadi
    Year: 2017
    Citations: 35

  • Title: Malignant melanoma diagnosis applying a machine learning method based on the combination of nonlinear and texture features
    Authors: S. Salem Ghahfarrokhi, H. Khodadadi, H. Ghadiri, F. Fattahi
    Year: 2023
    Citations: 33

  • Title: Climate control of an agricultural greenhouse by using fuzzy logic self-tuning PID approach
    Authors: M. Heidari, H. Khodadadi
    Year: 2017
    Citations: 28

  • Title: Fuzzy Logic Self-tuning PID Controller Design Based on Smith Predictor for Heating System
    Authors: H. Khodadadi, A. Dehghani
    Year: 2016
    Citations: 25

  • Title: Fuzzy Logic Self-Tuning PID Controller Design for Ball Mill Grinding Circuits Using an Improved Disturbance Observer
    Authors: H. Khodadadi, H. Ghadiri
    Year: 2019
    Citations: 24

  • Title: Speed control of a DC motor using a fractional order sliding mode controller
    Authors: S. Heidarpoor, M. Tabatabaei, H. Khodadadi
    Year: 2017
    Citations: 23

  • Title: Emerging Technologies in Medicine: Artificial Intelligence, Robotics, and Medical Automation
    Authors: M. Rezaei, S. Saei, S.J. Khouzani, M.E. Rostami, M. Rahmannia, …
    Year: 2023
    Citations: 21

Conclusion

Dr. Hamed Khodadadi’s research contributions reflect a strong blend of theoretical innovation and practical application across biomedical engineering, control systems, and machine learning. His highly cited works demonstrate significant impact in fields such as disease diagnosis, biomedical signal and image processing, and intelligent control methods. The breadth of his publications, spanning healthcare applications, robotics, and industrial systems, highlights both versatility and depth. With consistent recognition through citations, patents, and international awards, his research not only advances academic knowledge but also addresses real-world medical and engineering challenges. Collectively, his achievements establish him as a leading researcher whose contributions are both impactful and enduring, making him a deserving candidate for prestigious recognition such as the Best Researcher Award.

Karthikeyan P | Deeplearning | Best Paper Award

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Dr. Karthikeyan P | Deeplearning | Best Paper Award

Associate Professor, RV University, India

Dr. P. Karthikeyan is an Associate Professor at the School of Computer Science and Engineering, RV University, Bengaluru, with extensive expertise in Cloud Computing, Deep Learning, and Blockchain. With over 50 published research papers in international journals and conferences, Dr. Karthikeyan has significantly contributed to advancing knowledge in computer science. He has delivered over 30 talks and conducted workshops on emerging technologies, emphasizing practical applications of cloud infrastructure, deep learning, and programming. Throughout his career, he has worked with governmental projects in Taiwan and Saudi Arabia. His commitment to teaching is driven by his belief in using computer science to uplift society and improve lives. He is passionate about creating enriching learning experiences for students while fostering a collaborative academic environment.

Profile

Education 

Dr. P. Karthikeyan holds a Ph.D. from Anna University, Chennai (2018), with a focus on improving Virtual Machine instance allocation algorithms in cloud environments. He earned his M.E. in Computer Science & Engineering from Anna University Coimbatore (2009), with an impressive CGPA of 9.36/10.00. His undergraduate studies were completed at Muthayammal Engineering College, Namakkal, where he obtained a B.E. in Computer Science & Engineering (2005) with a percentage of 73%. Additionally, he holds a Diploma in Electrical and Electronics Engineering from Muthayammal Polytechnic College, Namakkal, with a percentage of 87%. Dr. Karthikeyan’s educational foundation laid the groundwork for his future academic and research achievements in the field of computer science, particularly in cloud computing, deep learning, and blockchain technologies.

Experience

Dr. P. Karthikeyan has a rich teaching and research background, currently serving as an Associate Professor at RV University, Bengaluru, since September 2023. He previously held academic positions as Associate Professor at Jain Deemed-to-be University (2021–2022) and Parul University (2020–2021), where he also led the Department of Computer Science and Engineering. Dr. Karthikeyan’s international experience includes serving as a Post-Doctoral Researcher at National Chung Cheng University, Taiwan (2022–2023). His early career includes teaching roles at Presidency University, Bengaluru, and Karunya Institute of Technology and Sciences, Coimbatore, where he shaped the next generation of computer scientists. He has also contributed to the development of significant government projects in Taiwan and Saudi Arabia. Dr. Karthikeyan teaches subjects related to System Software, Cloud Computing, Deep Learning, and Cryptography, while guiding students in advanced research in these areas.

Awards and Honors 

Dr. P. Karthikeyan has been recognized for his exceptional contributions to research and academia. Notably, he received the Best Young Researcher Award (Male) at the 2nd International Academic and Research Excellence Awards (IARE) in 2020, acknowledging his significant research achievements in the field of computer science. His work in cloud computing, deep learning, and blockchain has led to over 50 publications in prestigious journals and conferences, further solidifying his reputation as a leading academic in these fields. Dr. Karthikeyan has also been a key speaker and workshop facilitator, sharing his knowledge in over 30 talks globally. His continued commitment to research and education, coupled with his active involvement in international collaborations and government projects, highlights his contribution to the development of technology and education.

Research Focus 

Dr. P. Karthikeyan’s research interests lie at the intersection of Cloud Computing, Deep Learning, and Blockchain technologies. He focuses on optimizing cloud environments, particularly through virtual machine instance allocation algorithms, and has contributed significantly to enhancing cloud infrastructure. His work in deep learning spans multiple domains, from intrusion detection in IoT environments to applications in medical imaging and intelligent traffic management. Additionally, Dr. Karthikeyan’s research in blockchain explores its use in IoT security, supply chain management, and energy transaction markets. He is also deeply involved in research on federated learning, explainable AI, and blockchain-enabled solutions for privacy and security. Through his research, Dr. Karthikeyan aims to develop socially relevant applications that not only advance academic knowledge but also contribute to solving real-world problems, such as improving supply chain security and advancing sustainable smart grid technologies.

Publications

  1. Hybrid electro search with genetic algorithm for task scheduling in cloud computing – Ain Shams Engineering Journal 🌐📅
  2. Detection of distributed denial of service attack in cloud computing using optimization-based deep networks – Journal of Experimental & Theoretical AI 📉⚡
  3. Blockchain Technology: Challenges and Security issues in Consensus algorithm – International Conference on Computer Communication and Informatics 🔗🔐
  4. A survey on blockchain techniques for Internet of Vehicles security – Transactions on Emerging Telecommunication Technologies 🚗🔒
  5. Review of Blockchain-based IoT application and its security issues – International Conference on Intelligent Computing 🌐🔍
  6. Efficient lightweight privacy-preserving mechanism for Industry 4.0 based on elliptic curve cryptography – IEEE Transactions on Industrial Informatics 🏭🔐
  7. Reinforcement learning integrated in heuristic search for self-driving vehicle using blockchain in supply chain management – International Journal of Intelligent Networks 🚗📊
  8. Seafood supply chain management using blockchain – International Conference on Advanced Computing 🦐🔗
  9. Hybrid optimization scheme for intrusion detection using feature selection – Neural Computing and Applications 🛡️🧠
  10. Perishable food products in cold supply chain management using blockchain technology🌐

Ritu Tanwar | Artificial intelligence | Best Researcher Award

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Ms. Ritu Tanwar | Artificial intelligence | Best Researcher Award

Research Scholar, NIT Uttarakhand, India

Ms. Ritu Tanwar is a dedicated Research Scholar at the National Institute of Technology, Uttarakhand, India, specializing in stress and emotion recognition through advanced machine learning techniques. Her innovative research harnesses deep learning and artificial intelligence to interpret physiological signals, contributing significantly to the field of affective computing. Ritu’s academic journey and teaching roles underline her commitment to advancing both theoretical and practical aspects of her research.

Profile

Scopus

Research for “Best Researcher Award” for Ms. Ritu Tanwar

Strengths for the Award

Ms. Ritu Tanwar, currently pursuing her PhD at the National Institute of Technology, Uttarakhand, has demonstrated exceptional strengths in her field of research. Her primary area of focus—stress and emotion recognition through physiological signals—highlights her deep engagement with cutting-edge technology and data analysis. Ritu’s work utilizes advanced techniques in deep learning and machine learning to address significant challenges in affective state recognition.

Innovative Research Contributions: Ritu’s research integrates multimodal physiological signals to enhance stress recognition, showcasing her ability to develop and implement novel frameworks. Her attention-based hybrid deep learning models for wearable stress recognition, published in prestigious journals like Engineering Applications of Artificial Intelligence and Computers and Electrical Engineering, underline her proficiency in blending theory with practical application.

High-Impact Publications: Her publications in high-impact journals and conferences, including Computers in Biology and Medicine and the International Conference on Artificial Intelligence, reflect the substantial impact of her work on the field. Her innovative models, such as the CNN-LSTM based stress recognition system, are well-received and contribute to advancing the state of the art in affective computing.

Diverse Expertise: Ritu’s skill set spans various domains, from deep learning and artificial intelligence to data analysis and signal processing. Her ability to apply these skills effectively in her research demonstrates a well-rounded expertise that is crucial for a leading researcher.

Areas for Improvement

While Ms. Tanwar’s achievements are commendable, there are areas where she could further enhance her profile:

Broader Research Collaboration: Expanding her collaborative network with researchers from diverse fields could provide new insights and foster interdisciplinary approaches. Engaging in more collaborative projects could also increase the visibility and applicability of her research outcomes.

Broadened Publication Scope: Although Ritu has published extensively, diversifying her publication portfolio to include more interdisciplinary journals or higher-impact venues could further amplify the reach and influence of her research.

Enhanced Outreach: Increasing her participation in academic and industry conferences, workshops, and seminars could boost her professional network and provide more platforms to showcase her research. Additionally, contributing to review articles or special issues in her field could enhance her visibility as a thought leader.

Education 🎓

Ms. Tanwar is currently pursuing a PhD in Electronics Engineering at the National Institute of Technology, Uttarakhand, India, focusing on developing a deep learning framework for affective state recognition using multimodal physiological signals (April 2021-present). She earned her M.Tech. in Electronics & Communication Engineering from the University Institute of Engineering & Technology, Kurukshetra, India, with a thesis on emotion recognition from audio signals (July 2018). Her foundational B.Tech. in Electronics & Communication Engineering was also completed at the same institute (July 2013).

Experience 💼

Ms. Tanwar has a robust academic background, having worked as a Teaching Assistant at the National Institute of Technology, Uttarakhand, where she taught courses on Microcontroller and Interfacing, Digital Signal Processing, and Speech & Image Processing. Her research experience includes contributions as an Assistant/Associate Supervisor for undergraduate students and active participation in administrative and outreach activities, including her roles as Session Coordinator and Reviewer for the IC2E3 IEEE Conference.

Research Interests 🔬

Ms. Tanwar’s research interests are centered around stress and emotion recognition, physiological signals, and advanced data analysis techniques. She specializes in applying deep learning, machine learning, and artificial intelligence to improve the accuracy and applicability of affective state recognition systems.

Awards 🏆

Senior Research Fellow Scholarship (2021-present): Awarded for her exceptional research capabilities and contributions to her field.

Publication Recognition: Her work has been accepted and recognized in leading journals and conferences, reflecting her significant contributions to the field of artificial intelligence and machine learning.

Publications Top Notes

Tanwar, R., Phukan, O. C., Singh, G., Pal, P. K., & Tiwari, S. (2024). Attention based hybrid deep learning model for wearable based stress recognition. Engineering Applications of Artificial Intelligence, 127, 107391.

Tanwar, R., Singh, G., & Pal, P. K. (2024). A Hybrid Transposed Attention Based Deep Learning Model for Wearable and Explainable Stress Recognition. Computers and Electrical Engineering (Accepted).

Tanwar, R., Singh, G., & Pal, P. K. (2024). Explainable Artificial Intelligence System For Stress Recognition Using Multimodal Physiological Signals. Computers in Biology and Medicine (under review).

Tanwar, R., Singh, G., & Pal, P. K. (2024). Stress-Wed: Stress recognition autoencoder using Wearables Data. In Second International Conference on Artificial Intelligence: Towards Sustainable Intelligence. Springer (Accepted).

Conclusion

Ms. Ritu Tanwar’s research on stress and emotion recognition using physiological signals is both innovative and impactful, making her a strong candidate for the “Best Researcher Award.” Her contributions to deep learning and machine learning in affective computing are significant, and her academic and teaching experiences add to her profile as a dedicated and knowledgeable researcher. By addressing areas for improvement, such as expanding collaboration and publication scope, Ritu can further strengthen her position as a leading researcher in her field. Her ongoing research promises to make substantial contributions to both theoretical and applied aspects of artificial intelligence and emotion recognition.

Xiaodan Shi | Deep Learning | Best Researcher Award

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Dr. Xiaodan Shi | Deep Learning | Best Researcher Award

Postdoctoral researcher, Malardalen University,

Congratulations to Dr. Xiaodan Shi for receiving the Best Researcher Award in Deep Learning! 🏆 As a postdoctoral researcher at Malardalen University in Sweden, Dr. Shi has demonstrated exceptional dedication and innovation in advancing the field of deep learning. Their contributions have not only expanded our understanding but also paved the way for groundbreaking applications across various domains. Dr. Shi’s expertise and commitment to excellence serve as an inspiration to peers and aspiring researchers alike. This prestigious recognition is a testament to their outstanding achievements and significant impact on the scientific community. 🌟

Profile

Orcid

Education Background 📚

Xiaodan Shi has pursued an impressive academic journey, including a Ph.D. in Center for Spatial Information Science from The University of Tokyo, Japan. With a Master’s in Photogrammetry and Remote Sensing from Wuhan University, China, and a Bachelor’s in Remote Sensing and Information Science, Xiaodan Shi has built a solid foundation in engineering and spatial information science.

Work Experiences 💼

Xiaodan Shi brings a wealth of experience to the table, serving as a PostDoc at the Future Energy Center, Malardalen University, and previously as a Researcher at the Center for Spatial Information Science, The University of Tokyo. With expertise in algorithm engineering and urban remote sensing image processing, Xiaodan Shi has made significant contributions to the field of spatial information science.

Research Interests 🔬

Xiaodan Shi’s research interests encompass deep learning in sequential prediction and clustering, as well as urban remote sensing image processing. With a focus on developing innovative solutions for complex spatial data analysis, Xiaodan Shi’s work addresses critical challenges in areas such as trajectory prediction and time series forecasting.

Awards 🏆

Xiaodan Shi’s academic achievements have been recognized through various awards and scholarships, including the ISPRS Best Young Author Award and the MEXT Scholarship from the Japanese Government. Xiaodan Shi’s dedication to research excellence is evident in their contributions to top-tier conferences and journals in the field of artificial intelligence and remote sensing.

 Publications Top Notes 📖

“Multivariate Time Series Prediction for CO2 Concentration and Flue Gas Flowrate from Biomass-Fired Power Plant” – Fuel, 2023

“MetaTraj: Meta-learning for Cross-scene Cross-object Trajectory Prediction” – IEEE Transactions on Intelligent Transportation Systems, 2023y

“MobCovid: Confirmed Cases Dynamics Driven Time Series Prediction of Crowd in Urban Hotspot” – IEEE Transactions on Neural Networks and Learning Systems, 2023

“PredLife: Predicting Fine-grained Future Activity Patterns” – IEEE Transactions on Big Data, 2023

“Mutual Adaptation: Learning from Prototype for Time Series Prediction” – IEEE Transactions on Artificial Intelligence, 2023