Mohammad Reza Nikpour | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Mohammad Reza Nikpour | Artificial Intelligence | Best Researcher Award

Mohammad Reza Nikpour at University of Mohaghegh Ardabili, Iranđź“–

Dr. Mohammad Reza Nikpour is an esteemed scholar in Water Engineering, currently serving as a faculty member at the University of Mohaghegh Ardabili, Iran. His expertise lies in hydrodynamics, river engineering, and water resource management, with extensive contributions to computational modeling and environmental sustainability.

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Education Background🎓

  • Ph.D. in Water Engineering, University of Mohaghegh Ardabili, Iran
  • M.Sc. in Water Engineering, University of Mohaghegh Ardabili, Iran
  • B.Sc. in Water Engineering, University of Mohaghegh Ardabili, Iran

Professional Experience🌱

Dr. Nikpour has been actively involved in academic research and teaching at the University of Mohaghegh Ardabili. His work focuses on computational hydrodynamics, groundwater quality assessment, and flood prediction modeling. He has collaborated with international researchers and contributed to innovative water management solutions through data-driven models.

Research Interests🔬

Her research interests include:

  • Hydrodynamics and River Engineering
  • Groundwater Quality Assessment
  • Soft Computing and AI Applications in Water Resource Management
  • Flood Prediction and Climate Change Impact Studies

Author Metrics

Dr. Mohammad Reza Nikpour has established a strong academic presence with numerous publications in high-impact journals, including River Research and Applications, Journal of Cleaner Production, and Stochastic Environmental Research and Risk Assessment. His research contributions have been widely recognized, earning him a growing citation count on Google Scholar and an impressive h-index on Scopus (to be verified). As a highly cited researcher in water engineering, his work has significantly influenced hydrodynamics, groundwater quality assessment, and computational water resource management. His ORCID ID is 0000-0003-4332-0525, and his research continues to shape innovative solutions in environmental sustainability and AI-driven water system modeling.

Awards and Honors
  • Recognized for outstanding contributions in hydrodynamic modeling and water resource sustainability.
  • Published multiple high-impact research papers in top-tier journals such as River Research and Applications, Journal of Cleaner Production, and Stochastic Environmental Research and Risk Assessment.
  • Recipient of research grants and funding for pioneering studies in environmental and computational water management.
Publications Top Notes đź“„

1. Estimation of daily pan evaporation using two different adaptive neuro-fuzzy computing techniques

  • Authors: H. Sanikhani, O. Kisi, M.R. Nikpour, Y. Dinpashoh
  • Journal: Water Resources Management
  • Volume: 26
  • Pages: 4347-4365
  • Year: 2012
  • Citations: 70
  • Summary: This study applies adaptive neuro-fuzzy inference system (ANFIS) models to estimate daily pan evaporation, comparing their accuracy and efficiency in hydrological forecasting.

2. Experimental and numerical simulation of water hammer

  • Authors: M.R. Nikpour, A.H. Nazemi, A.H. Dalir, F. Shoja, P. Varjavand
  • Journal: Arabian Journal for Science and Engineering
  • Volume: 39
  • Pages: 2669-2675
  • Year: 2014
  • Citations: 48
  • Summary: This paper investigates water hammer phenomena using both experimental methods and numerical simulations, providing insights into fluid dynamics and pipeline safety.

3. Exploring the application of soft computing techniques for spatial evaluation of groundwater quality variables

  • Authors: F. Esmaeilbeiki, M.R. Nikpour, V.K. Singh, O. Kisi, P. Sihag, H. Sanikhani
  • Journal: Journal of Cleaner Production
  • Volume: 276
  • Article: 124206
  • Year: 2020
  • Citations: 31
  • Summary: This research explores soft computing techniques, such as machine learning, for the spatial analysis of groundwater quality, enhancing environmental monitoring and sustainability.

4. Hydrodynamics of river-channel confluence: toward modeling separation zone using GEP, MARS, M5 Tree, and DENFIS techniques

  • Authors: O. Kisi, P. Khosravinia, M.R. Nikpour, H. Sanikhani
  • Journal: Stochastic Environmental Research and Risk Assessment
  • Volume: 33 (4-6)
  • Pages: 1089-1107
  • Year: 2019
  • Citations: 28
  • Summary: The study applies various data-driven models, including gene expression programming (GEP) and M5 Tree, to model separation zones in river confluences, improving hydrodynamic predictions.

5. Application of novel data mining algorithms in prediction of discharge and end depth in trapezoidal sections

  • Authors: P. Khosravinia, M.R. Nikpour, O. Kisi, Z.M. Yaseen
  • Journal: Computers and Electronics in Agriculture
  • Volume: 170
  • Article: 105283
  • Year: 2020
  • Citations: 16
  • Summary: This paper investigates the use of advanced data mining techniques to predict discharge and end depth in trapezoidal channels, optimizing water resource management and agricultural planning.

Conclusion

Dr. Mohammad Reza Nikpour is an exceptional researcher in AI-driven water resource management, making him a strong candidate for the Best Researcher Award. His pioneering work in soft computing and AI applications for hydrology and environmental sustainability sets him apart in his field. Expanding into deep learning, increasing industry collaborations, and engaging in AI conferences could further solidify his leadership in AI for water engineering.

Faisal Mehmood | Computer Vision | Best Researcher Award

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Dr. Faisal Mehmood | Computer Vision | Best Researcher Award

Post Doctorate at Shenzhen University, Chinađź“–

Faisal Mehmood is a passionate PhD researcher at Zhengzhou University, specializing in Electrical and Information Engineering. With a strong academic background, including degrees in Computer Science, he has a diverse range of expertise in deep learning, computer vision, and human action recognition (HAR). Faisal has authored numerous research papers in prominent journals and conferences and has practical experience as a software developer, database developer, and lecturer. His contributions are recognized in academia, where he also actively reviews for esteemed journals. Faisal continues to focus on advancing technologies in machine learning and artificial intelligence.

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Education Background🎓

  • Ph.D. in Electrical and Information Engineering (2019–2024), Zhengzhou University, Henan, China (1st Division).
  • MS in Computer Science (2015–2017), University of Agriculture Faisalabad (UAF), Punjab, Pakistan (1st Division).
  • MSc in Computer Science (2013–2015), University of Agriculture Faisalabad (UAF), Punjab, Pakistan (1st Division).
  • BSc (2011–2013), Islamia University Bahawalpur, Punjab, Pakistan (1st Division).
  • Intermediate (2008–2010), BISE Bahawalpur, Punjab, Pakistan (1st Division).
  • Matriculation (2006–2008), BISE Bahawalpur, Punjab, Pakistan (1st Division).

Professional Experience🌱

Faisal Mehmood has accumulated a wealth of teaching and industry experience over the years. He has served as a lecturer at institutions such as the University of Agriculture Faisalabad, University of Education Faisalabad, and GC University Faisalabad. Faisal has also gained industry experience as a Software Developer and Database Developer, working on various projects involving database management, web development, and system software design. He has supervised several undergraduate projects and contributed to academic workshops and seminars, fostering an environment of interactive learning and development.

Research Interests🔬

Faisal’s research interests include:

  • Deep Learning: Exploring advanced neural network architectures.
  • Computer Vision: Enhancing image and video processing for real-world applications.
  • Human Action Recognition (HAR): Developing systems for detecting and recognizing human actions through innovative algorithms.
  • Natural Language Processing: Applying machine learning techniques for language understanding and processing.

Author Metrics

Faisal Mehmood has published several research papers in reputed journals, such as IEEE Transactions on Consumer Electronics, Soft Computing, and Computers in Human Behavior, with numerous articles under review. His work has contributed significantly to advancements in the fields of human action recognition, machine learning, and data science. He has received merit scholarships throughout his academic career and has been recognized with awards such as the Chief Minister’s Laptop Scheme and various programming competition wins. Faisal actively contributes to the academic community as a reviewer for top journals and conferences, further enriching his research endeavors.

Publications Top Notes đź“„

1. Human action recognition of spatiotemporal parameters for skeleton sequences using MTLN feature learning framework

  • Authors: F Mehmood, E Chen, MA Akbar, AA Alsanad
  • Journal: Electronics
  • Volume: 10
  • Issue: 21
  • Article: 2708
  • Year: 2021
  • Citations: 21

2. Three-dimensional agricultural land modeling using unmanned aerial system (UAS)

  • Authors: F Mahmood, K Abbas, A Raza, MA Khan, PW Khan
  • Journal: International Journal of Advanced Computer Science and Applications
  • Volume: 10
  • Issue: 1
  • Year: 2019
  • Citations: 18

3. Intelligent Transmission Control for Efficient Operations in SDN

  • Authors: R Alkanhel, A Ali, F Jamil, M Nawaz, F Mehmood, A Muthanna
  • Journal: Computers, Materials & Continua
  • Volume: 71
  • Issue: 2
  • Year: 2022
  • Citations: 11

4. Effect of human-related factors on requirements change management in offshore software development outsourcing: A theoretical framework

  • Author: FM Sukana Z
  • Journal: Soft Computing and Machine Intelligence
  • Volume: 1
  • Issue: 1
  • Pages: 36-52
  • Year: 2021
  • Citations: 11

5. Towards successful global software development

  • Authors: M Shafiq, Q Zhang, MA Akbar, T Kamal, F Mehmood, MT Riaz
  • Conference: Proceedings of the 24th International Conference on Evaluation and …
  • Year: 2020
  • Citations: 11

Conclusion

Dr. Faisal Mehmood is undoubtedly a highly deserving candidate for the Best Researcher Award due to his exceptional contributions to deep learning, computer vision, and human action recognition. His innovative frameworks, high-quality publications, and academic success distinguish him as a leader in his field. While there are areas for further improvement, particularly in expanding his research reach and increasing industrial collaborations, his continued growth and success make him a strong candidate for the award. His work, particularly in applying AI and deep learning for practical applications, has great potential to shape the future of technology.

Dr. Mehmood’s combination of academic rigor, technical expertise, and research impact make him a promising figure in the academic community and an excellent candidate for this prestigious recognition.

Raheleh Ghouchan Nezhad Noor Nia | Artificial Intelligence | Best Researcher Award

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Dr. Raheleh Ghouchan Nezhad Noor Nia | Artificial Intelligence | Best Researcher Award

Postdoc Researcher at Mashhad University of Medical Sciences, Mashhad, Iranđź“–

Dr. Raheleh Ghouchan Nezhad Noor Nia is a Senior Data Scientist and Postdoctoral Researcher specializing in the application of machine learning, artificial intelligence, and medical informatics. She is currently based at the Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran. Her extensive academic and professional experience spans multiple domains, including medical informatics, AI in medicine, and data science.

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Education Background🎓

  • Postdoc in Medical Informatics – Data Science & AI in Medicine (2022 – Present), Department of Medical Informatics, Mashhad University of Medical Sciences, Mashhad, Iran.
  • Ph.D. in Computer Engineering – Software (2016 – 2022), Department of Computer Engineering, Azad University, Mashhad, Iran.
  • Master’s in Computer Engineering – AI and Robotics (2013 – 2015), Department of Computer Engineering, Azad University, Mashhad, Iran.
  • Bachelor’s in Computer Engineering – Software (2009 – 2013), Department of Computer Engineering, Azad University, Mashhad, Iran.

Professional Experience🌱

Dr. Ghouchan Nezhad Noor Nia currently serves as a Postdoctoral Researcher and Senior Data Scientist at Mashhad University of Medical Sciences, where she is involved in several pioneering research projects related to AI in healthcare. In addition to her role as a researcher, she is a lecturer at various institutions, including the Department of Computer Engineering at Mashhad Azad University, Khayyam University, and Toos University. She is also actively contributing as a reviewer for prestigious journals such as Materials Today Communication and the Medical Informatics Europe conferences.

Her collaborative efforts extend internationally, having worked with prominent researchers at Karlsruhe Institute of Technology, Germany. Dr. Ghouchan Nezhad Noor Nia has also led and contributed to numerous conferences and workshops focused on AI in medical sciences and health technology.

Research Interests🔬

Dr. Ghouchan Nezhad Noor Nia’s research interests include the intersection of AI, machine learning, and medical informatics. Her focus is on big data mining, social mining, graph mining, material science, and AI applications in medical diagnostics, specifically in diseases like lupus nephritis and pulmonary thromboembolism. She is also interested in ontology engineering, metadata management, health social networks, deep learning, and point-of-interest recommendation systems

Author Metrics

Dr. Ghouchan Nezhad Noor Nia has contributed to numerous high-impact publications and has an active research profile with publications in reputed journals and conferences. Her work focuses on innovative solutions and machine learning methods to solve complex challenges in healthcare and material science. She has co-authored papers presented at various prestigious international conferences, including the 12th Neuroscience Congress and International Health Literacy Congress. Her Google Scholar profile reflects her growing influence in the field.

Publications Top Notes đź“„

1. A Graph-Based k-Nearest Neighbor (KNN) Approach for Predicting Phases in High-Entropy Alloys

  • Authors: R Ghouchan Nezhad Noor Nia, M Jalali, M Houshmand
  • Journal: Applied Sciences
  • Volume: 12
  • Issue: 16
  • Article ID: 8021
  • Year: 2022
  • DOI: 10.3390/app12168021
  • Summary: This paper introduces a graph-based k-nearest neighbor (KNN) algorithm for phase prediction in high-entropy alloys, leveraging machine learning techniques for material science applications.

2. Machine Learning Approach to Community Detection in a High-Entropy Alloy Interaction Network

  • Authors: R Ghouchan Nezhad Noor Nia, M Jalali, M Mail, Y Ivanisenko, C KĂĽbel
  • Journal: ACS Omega
  • Volume: 7
  • Issue: 15
  • Pages: 12978-12992
  • Year: 2022
  • DOI: 10.1021/acsomega.2c02625
  • Summary: This work focuses on community detection in a high-entropy alloy interaction network using machine learning methods, exploring the structure and relationships between various alloy elements.

3. Non-Alcoholic Fatty Liver Disease Diagnosis with Multi-Group Factors

  • Authors: A Arzehgar, RG Nezhad Noor Nia, V Dehdeleh, F Roudi, S Eslami
  • Journal: Healthcare Transformation with Informatics and Artificial Intelligence
  • Pages: 503-506
  • Year: 2023
  • Summary: This paper proposes a novel methodology for diagnosing non-alcoholic fatty liver disease (NAFLD) by considering multiple influencing factors and utilizing advanced informatics and artificial intelligence.

4. RecMem: Time Aware Recommender Systems Based on Memetic Evolutionary Clustering Algorithm

  • Authors: RG Nezhad Noor Nia, M Jalali
  • Journal: Computational Intelligence and Neuroscience
  • Article ID: 8714870
  • Year: 2022
  • DOI: 10.1155/2022/8714870
  • Summary: The paper presents RecMem, a time-aware recommender system that integrates a memetic evolutionary clustering algorithm, aiming to improve recommendation accuracy in dynamic environments.

5. A Community Detection-based Approach in Social Networks to Improve the Equation Analysis in Material Science

  • Authors: R Ghouchan Nezhad Noor Nia, M Jalali, M Houshmand
  • Journal: Journal of Iranian Association of Electrical and Electronics Engineers
  • Volume: 21
  • Issue: 1
  • Year: 2024 (upcoming)
  • Summary: This study proposes a community detection-based approach within social networks to enhance equation analysis methods used in material science, specifically in the context of high-entropy alloys.

Conclusion

Dr. Raheleh Ghouchan Nezhad Noor Nia is a highly deserving candidate for the Best Researcher Award. Her contributions to AI, machine learning, and medical informatics are transformative and have the potential to address critical healthcare challenges. She exhibits strengths in multidisciplinary research, with a particular focus on medical diagnostics and healthcare innovation. With further growth in clinical and practical applications, Dr. Ghouchan Nezhad Noor Nia’s work could have an even broader and more profound impact on both academia and real-world healthcare solutions. Her dedication to research, teaching, and international collaboration makes her an exemplary figure in her field.

Sathishkumar Moorthy | Computer Vision | Best Researcher Award

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Dr. Sathishkumar Moorthy | Computer Vision | Best Researcher Award

Post-Doctoral Researcher at Sejong University, South Koreađź“–

Dr. Sathishkumar Moorthy is an accomplished researcher specializing in artificial intelligence (AI), machine learning (ML), and deep learning (DL) with a focus on computer vision applications. With a proven track record in innovative research, he has developed cutting-edge techniques for video object detection, human emotion recognition, and intelligent surveillance systems. His expertise includes self-attention-based models, image processing, and multimodal data analysis. Dr. Moorthy has contributed to academia and industry through impactful publications and collaborative research projects, striving to advance computer vision and AI technology.

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Education Background🎓

Dr. Sathishkumar Moorthy earned his Doctorate of Philosophy (Ph.D.) from Kunsan National University, South Korea (2017–2024), with a commendable CGPA of 4.16. His doctoral thesis focused on developing an enhanced self-attention-based Vision Transformer model for robust video object detection systems. He completed his Master of Engineering (M.E.) in 2013 from Karpagam Academy of Higher Education, Tamil Nadu, India, achieving an impressive CGPA of 9.05. His master’s thesis explored automatic diagnosis of breast cancer lesions using Gaussian Mixture Model and Expectation-Maximization algorithms. He holds a Bachelor of Engineering (B.E.) in Computer Science and Engineering from Anna University, Tamil Nadu, India (2011), graduating with a CGPA of 7.87. His undergraduate thesis analyzed and compared parsing techniques for asynchronous messages.

Professional Experience🌱

Dr. Sathishkumar has accumulated extensive experience across academia, industry, and research roles. He is currently a Post-Doctoral Researcher at Sejong University, South Korea (2024–Present), focusing on multimodal human emotion recognition using advanced Transformer-based models. Prior to this, he served as Manager of the AI Research Team at Smart Vision Tech Inc., Seoul, where he specialized in developing advanced object detection and segmentation algorithms, leveraging frameworks such as YOLO and Faster R-CNN. His teaching experience includes roles as Assistant Professor at Karpagam College of Engineering (2017) and J.K.K. Munirajah College of Technology (2013–2016) in Tamil Nadu, India, where he delivered lectures on programming, data structures, and algorithms and conducted workshops on mobile application development and genetic algorithms.

Research Interests🔬

Dr. Moorthy’s research focuses on:

  • Computer Vision: Video object detection, intelligent surveillance systems, and multimodal emotion recognition.
  • Artificial Intelligence: Deep learning, Transformer models, and advanced neural network architectures.
  • Industry Applications: Real-time fault detection, anomaly tracking, and autonomous systems using AI/ML techniques.
  • Medical Imaging: Image segmentation and diagnosis using probabilistic and ML algorithms.

Author Metrics

Dr. Sathishkumar Moorthy has made significant contributions to the field of computer vision and artificial intelligence through his research and publications. His works focus on advanced AI/ML techniques, including Vision Transformers, multimodal emotion recognition, and object detection, particularly for real-world applications such as video surveillance and medical imaging.

He has authored several high-impact research papers in reputable journals and conferences, reflecting his expertise in image processing, deep learning, and robotics. His research output has garnered notable citations, showcasing the relevance and influence of his work in the academic and research communities. Dr. Sathishkumar’s Google Scholar profile highlights his active contributions to advancing AI-driven solutions for complex problems, affirming his position as a dedicated researcher in the field.

Publications Top Notes đź“„

1. Distributed Leader-Following Formation Control for Multiple Nonholonomic Mobile Robots via Bioinspired Neurodynamic Approach

  • Authors: S. Moorthy, Y.H. Joo
  • Journal: Neurocomputing
  • Volume: 492
  • Pages: 308–321
  • Year: 2022
  • Citations: 43
  • DOI/Link: [Check Neurocomputing journal for more details]

2. Gaussian-Response Correlation Filter for Robust Visual Object Tracking

  • Authors: S. Moorthy, J.Y. Choi, Y.H. Joo
  • Journal: Neurocomputing
  • Volume: 411
  • Pages: 78–90
  • Year: 2020
  • Citations: 31
  • DOI/Link: [Check Neurocomputing journal for more details]

3. Adaptive Spatial-Temporal Surrounding-Aware Correlation Filter Tracking via Ensemble Learning

  • Authors: S. Moorthy, Y.H. Joo
  • Journal: Pattern Recognition
  • Volume: 139
  • Article Number: 109457
  • Year: 2023
  • Citations: 21
  • DOI/Link: [Check Pattern Recognition journal for more details]

4. Multi-Expert Visual Tracking Using Hierarchical Convolutional Feature Fusion via Contextual Information

  • Authors: S. Moorthy, Y.H. Joo
  • Journal: Information Sciences
  • Volume: 546
  • Pages: 996–1013
  • Year: 2021
  • Citations: 21
  • DOI/Link: [Check Information Sciences journal for more details]

5. Instinctive Classification of Alzheimer’s Disease Using fMRI, PET, and SPECT Images

  • Authors: E. Dinesh, M.S. Kumar, M. Vigneshwar, T. Mohanraj
  • Conference: 7th International Conference on Intelligent Systems and Control (ISCO)
  • Year: 2013
  • Citations: 15
  • Pages: Available in the ISCO conference proceedings.

Conclusion

Dr. Sathishkumar Moorthy is an exemplary researcher whose work significantly contributes to advancing AI, ML, and computer vision. His combination of academic rigor, industry experience, and impactful research publications makes him a strong candidate for the Best Researcher Award.