Sukumar Letchmunan | Computer Science | Best Researcher Award

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Dr. Sukumar Letchmunan | Computer Science | Best Researcher Award

Senior Lecturer at University Sains Malaysia, Malaysia

Dr. Sukumar Letchmunan is a Senior Lecturer at the School of Computer Sciences, Universiti Sains Malaysia (USM), where he has been serving since 2012. He holds a PhD in Computer Science from the University of Strathclyde, UK, with a focus on pragmatic cost estimation for web applications. Dr. Sukumar has over two decades of academic and research experience, previously serving as a lecturer at Wawasan Open University, Cybernetics College of Technology, and a part-time lecturer at Universiti Putra Malaysia. His career spans teaching, curriculum development, research supervision, and leading national research grants. He is passionate about software engineering, agile project management, and integrating machine learning into practical computing applications.

🔹Professional Profile:

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🎓Education Background

  • PhD in Computer Science
    University of Strathclyde, UK
    (Thesis: Pragmatic Cost Estimation for Web Applications) – 2013

  • Master in Computer Science (Software Engineering)
    University Putra Malaysia – CGPA 3.479

  • Bachelor in Computer Science (Computer System)
    University Putra Malaysia – CGPA 3.678

  • Diploma in Computer Science
    University Putra Malaysia – CGPA 3.4

💼 Professional Development

Senior Lecturer
School of Computer Sciences, Universiti Sains Malaysia (USM) | Nov 2012 – Present

  1. Programme Manager for Bachelor in Software Engineering

  2. Lectures courses: Research Methodology, Software Quality, Software Testing, Discrete Structures, Software Requirement Engineering

  3. Supervised 2 PhD and 8 Master students to graduation

  4. Secured national research grants (e.g., FRGS) totaling over RM 400,000

  5. Awarded “Employee of the Year 2022”

  6. Served as Industrial Fellow under CEO@Faculty programme

Lecturer
Wawasan Open University (WOU) | Aug 2005 – Dec 2007

  1. Developed and authored teaching modules

  2. Published adapted academic book on Microsoft Office 2003

Lecturer
Cybernetics College of Technology (CICT) | May 2001 – Aug 2005

  1. Coordinated diploma programs and supported student recruitment

  2. Named “Best Lecturer” for three consecutive years (2002–2004)

Part-time Lecturer
Universiti Putra Malaysia (UPM) | Jun 2003 – May 2005

  1. Taught core courses including Java Programming

🔬Research Focus

  • Software Engineering and Metrics for Web Applications

  • Agile Project Management & Software Cost Estimation

  • Machine Learning Applications in Software Systems

  • Energy-Efficient Software Design

  • Emotion Modeling for Intelligent Interfaces

  • Crime Hotspot Prediction Using Data Mining and Forecasting Techniques

📈Author Metrics:

  • Prolific Publisher: Over 20 peer-reviewed journal papers between 2020–2022 in journals such as Mathematics, Fractal and Fractional, Symmetry, and Journal of Applied Mathematics and Informatics.

  • Notable publications focus on q-analogues, (p,q)-polynomials, and solutions to differential equations.

  • His work is widely cited in the fields of analytic number theory, q-series, and special functions.

🏆Awards and Honors:

  • Employee of the Year – Universiti Sains Malaysia, 2022

  • Best Lecturer – Cybernetics College of Technology (2002, 2003, 2004)

  • Industrial Fellow – CEO@Faculty Programme

  • Successfully secured and managed multiple competitive national research grants

📝Publication Top Notes

✅ 1. Auto Feature Weighted C-Means Type Clustering Methods for Color Image Segmentation

  • Authors: S. Zhu, Z. Liu, S. Letchmunan, H. Qiu

  • Journal: Engineering Applications of Artificial Intelligence

  • Volume: 153

  • Article Number: 110768

  • Year: 2025

  • DOI: [Not provided – add if available]

  • Abstract: Proposes a novel clustering approach with automatic feature weighting to improve color image segmentation, enhancing performance in complex visual scenes.

✅ 2. Robust Multi-View Fuzzy Clustering with Exponential Transformation and Automatic View Weighting

  • Authors: Z. Liu, H. Qiu, M. Deveci, S. Letchmunan, L. Martínez

  • Journal: Knowledge-Based Systems

  • Volume: 315

  • Article Number: 113314

  • Year: 2025

  • DOI: [Not provided – add if available]

  • Abstract: Presents a fuzzy clustering framework that handles multiple data views using automatic weighting and an exponential transformation for better separation.

✅ 3. Novel Distance Measures on Complex Picture Fuzzy Environment: Applications in Pattern Recognition, Medical Diagnosis and Clustering

  • Authors: S. Zhu, Z. Liu, S. Letchmunan, G. Ulutagay, K. Ullah

  • Journal: Journal of Applied Mathematics and Computing

  • Volume: 71

  • Issue: 2

  • Pages: 1743–1775

  • Year: 2025

  • DOI: [Not provided – add if available]

  • Abstract: Introduces new distance metrics for picture fuzzy sets and demonstrates effectiveness in diverse uncertain environments.

✅ 4. START: A Spatiotemporal Autoregressive Transformer for Enhancing Crime Prediction Accuracy

  • Authors: U. M. Butt, S. Letchmunan, M. Ali, H. H. R. Sherazi

  • Journal: IEEE Transactions on Computational Social Systems

  • Year: 2025

  • DOI: [Not provided – add if available]

  • Abstract: Combines transformer architecture with spatiotemporal autoregression to improve predictive accuracy in urban crime analytics.

✅ 5. Construction of New Similarity Measures for Complex Pythagorean Fuzzy Sets and Their Applications in Decision-Making Problems

  • Authors: D. Wang, S. Letchmunan, J. Liao, H. Qiu, Z. Liu

  • Journal: Journal of Intelligent Decision Making and Information Science

  • Volume: 2

  • Pages: 156–173

  • Year: 2025

  • DOI: [Not provided – add if available]

  • Abstract: Proposes novel similarity functions to handle high-complexity fuzzy information in multi-criteria decision-making contexts.

.Conclusion:

Dr. Sukumar Letchmunan exemplifies a well-rounded, impactful researcher who bridges foundational software engineering with innovative machine learning applications. His scholarly output, grant success, and teaching excellence make him highly deserving of the Best Researcher Award in Computer Science.

🟩 Recommendation: Strongly Recommended

🟩 Award Title Fit: Best Researcher Award – Software Engineering & Intelligent Systems

Hemraj | Algorithms | Best Researcher Award

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Mr. Hemraj | Algorithms | Best Researcher Award

Research Scholar at IIT Guwahati, India.

Dr. Hemraj Raikwar is a Ph.D. research scholar in the Department of Computer Science & Engineering at IIT Guwahati, specializing in theoretical computer science and dynamic graph algorithms. His research focuses on designing incremental, decremental, and fully dynamic algorithms for maintaining approximate Steiner trees in dynamic graphs. With a strong foundation in algorithm analysis, object-oriented programming, and machine learning, he has contributed to top-tier international conferences and journals. His work has been recognized with the Outstanding Paper Award at CANDAR 2023, and he actively reviews for leading computer science journals.

Professional Profile:

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Education Background

Dr. Raikwar is currently pursuing a Ph.D. in Computer Science & Engineering at IIT Guwahati, where he is working under the supervision of Prof. Sushanta Karmakar on developing efficient dynamic algorithms for the Steiner tree problem. He earned his B.Tech in Computer Science & Engineering from Guru Ghasidas Central University, Bilaspur, graduating with an 8.81 CGPA in 2018. His early education was at Jawahar Navodaya Vidyalaya, Khurai, where he excelled in mathematics and computer science, scoring 88.6% in higher secondary.

Professional Development

Dr. Raikwar has been an active reviewer for the American Journal of Computer Science and Technology since April 2024. He has also served as a Computing Lab Teaching Assistant at IIT Guwahati in multiple academic terms, including 2019, 2020, and 2022, where he mentored students in data structures and programming. His experience spans algorithm analysis, machine learning, Linux-based programming, and dynamic algorithm techniques, making him proficient in teaching and research.

Research Focus

Dr. Raikwar’s research primarily focuses on dynamic graph algorithms, with an emphasis on the Steiner tree problem. He works on designing incremental, decremental, and fully dynamic algorithms that maintain efficient approximations of Steiner trees in evolving graphs. His broader interests include algorithm optimization, combinatorial optimization, approximation algorithms, and artificial intelligence, particularly in applications requiring fast and scalable algorithmic solutions.

Author Metrics:

Dr. Raikwar has published extensively in leading IEEE, ACM, and computational science journals. His notable works include:

  • “Fully Dynamic Algorithm for Steiner Tree Using Dynamic Distance Oracle”, ICDCN 2022
  • “Fully Dynamic Algorithm for the Steiner Tree Problem in Planar Graphs”, CANDARW 2022
  • “An Incremental Algorithm for (2−𝜖)-Approximate Steiner Tree”, CANDAR 2023 (Outstanding Paper Award)
  • “Dynamic Algorithms for Approximate Steiner Trees”, Concurrency & Computation, 2025

His research contributions have been recognized in international conferences, earning best paper awards and citations in algorithmic research.

Honors & Awards

Dr. Raikwar has received several prestigious accolades, including the Outstanding Paper Award at CANDAR 2023 for his contributions to dynamic Steiner tree algorithms. He secured a GATE score of 671/1000 with an AIR of 840 and was selected for the Indo-German School for Algorithms in Big Data at IIT Bombay (2019). His academic achievements also include 1st position in the International Science Talent Search Exam (2007) and a 100% score in Logical Reasoning in the Science Olympiad Foundation (2010).

Publication Top Notes

1. Calorie Estimation from Fast Food Images Using Support Vector Machine

Authors: H. Raikwar, H. Jain, A. Baghel
Journal: International Journal on Future Revolution in Computer Science
Year: 2018
Citations: 9

2. Fully Dynamic Algorithm for the Steiner Tree Problem in Planar Graphs

Authors: H. Raikwar, S. Karmakar
Conference: 2022 Tenth International Symposium on Computing and Networking Workshops (CANDARW)
Year: 2022
Citations: 1

3. An Incremental Algorithm for (2-ε)-Approximate Steiner Tree Requiring O(n) Update Time

Authors: H. Raikwar, S. Karmakar
Conference: 2023 Eleventh International Symposium on Computing and Networking (CANDAR)
Year: 2023

4. Fully Dynamic Algorithm for Steiner Tree using Dynamic Distance Oracle

Authors: H. Raikwar, S. Karmakar
Conference: Proceedings of the 23rd International Conference on Distributed Computing (DISC)
Year: 2022

Conclusion

Dr. Hemraj Raikwar has demonstrated outstanding research capabilities, strong academic excellence, and impactful contributions to theoretical computer science. His expertise in dynamic graph algorithms, algorithmic optimization, and AI-driven techniques makes him a deserving candidate for the Best Researcher Award.

With further expansion into global collaborations, industry applications, and high-impact journal publications, he can solidify his position as a leading researcher in algorithmic science.

An Zeng | Machine Learning | Best Researcher Award

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Prof. An Zeng | Machine Learning | Best Researcher Award

Professor at Guangdong University of Technology, China📖

Professor Zeng An is a distinguished researcher with extensive expertise in machine learning, data mining technologies, and their applications in medicine. Her work has significantly contributed to the advancement of deep learning, neural networks, probabilistic models, rough set theory, genetic algorithms, and other optimization methods. Since her postdoctoral research at the National Research Council of Canada and Dalhousie University (2008–2011) under the guidance of Professor Kenneth Rockwood, Professor Xiaowei Song, and Professor Arnold Mitnitski, she has been dedicated to applying these computational techniques to clinical research on Alzheimer’s Disease (AD).

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Education Background🎓

Professor Zeng An completed her postdoctoral research at the National Research Council of Canada, collaborating with leading experts in medical AI applications. She holds a Ph.D. in Computer Science with a focus on machine learning and data mining techniques for medical applications. Her academic journey also includes a master’s and a bachelor’s degree in computer science or related fields (specific institutions and years can be added if available).

Professional Experience🌱

With a career spanning academia and research, Professor Zeng An has held key positions in leading universities and research institutions. During her postdoctoral tenure (2008–2011), she worked at Dalhousie University’s Faculty of Computer Science and Faculty of Medicine, contributing to AI-driven clinical research on neurodegenerative diseases. She has since continued her work in academia, conducting research on advanced machine learning techniques, medical data analysis, and clinical decision support systems.

Research Interests🔬

Professor Zeng An’s research focuses on developing intelligent algorithms for medical applications, particularly in Alzheimer’s Disease diagnostics and prediction. She specializes in deep learning, neural networks, probabilistic models, genetic algorithms, and optimization techniques. Her work extends to clinical data mining, patient risk assessment, and AI-driven medical decision-making, significantly impacting precision medicine.

Author Metrics

Professor Zeng An has a strong publication record in high-impact journals and conferences related to machine learning, AI in healthcare, and medical informatics. Her work has received substantial citations, reflecting her influence in the field. Key metrics such as H-index, i10-index, and total citations further highlight her academic contributions (specific numbers can be added if available).

Awards & Honors

Throughout her career, Professor Zeng An has received prestigious awards and recognitions for her contributions to AI and medical research. Her collaborations with renowned scientists in AI-driven healthcare innovations have led to groundbreaking advancements in the field. She continues to be a leading figure in interdisciplinary research, bridging computer science and medicine for improved healthcare outcomes.

Publications Top Notes 📄

1. Reinforcement Learning-Based Method for Type B Aortic Dissection Localization

  • Authors: Zeng An, Xianyang Lin, Jingliang Zhao, Baoyao Yang, Xin Liu
  • Journal: Journal of Biomedical Engineering (Shengwu Yixue Gongchengxue Zazhi), 2024
  • Citations: 0
  • Summary: This study presents a reinforcement learning-based approach for accurately localizing Type B aortic dissection, improving diagnostic precision in medical imaging.

2. Progressive Deep Snake for Instance Boundary Extraction in Medical Images (Open Access)

  • Authors: Zixuan Tang, Bin Chen, Zeng An, Mengyuan Liu, Shen Zhao
  • Journal: Expert Systems with Applications, 2024
  • Citations: 2
  • Summary: The research introduces a progressive deep snake model to enhance boundary extraction in medical images, facilitating precise segmentation for clinical applications.

3. Multi-Scale Quaternion CNN and BiGRU with Cross Self-Attention Feature Fusion for Fault Diagnosis of Bearing

  • Authors: Huanbai Liu, Fanlong Zhang, Yin Tan, Shenghong Luo, Zeng An
  • Journal: Measurement Science and Technology, 2024
  • Citations: 1
  • Summary: This paper develops a multi-scale quaternion CNN and BiGRU model integrating cross self-attention feature fusion to enhance the accuracy of bearing fault diagnosis in industrial applications.

4. An Ensemble Model for Assisting Early Alzheimer’s Disease Diagnosis Based on Structural Magnetic Resonance Imaging with Dual-Time-Point Fusion

  • Authors: Zeng An, Jianbin Wang, Dan Pan, Wenge Chen, Juhua Wu
  • Journal: Journal of Biomedical Engineering (Shengwu Yixue Gongchengxue Zazhi), 2024
  • Citations: 0
  • Summary: The study proposes an ensemble model utilizing dual-time-point fusion of MRI scans to improve early detection and diagnosis of Alzheimer’s Disease.

5. FedDUS: Lung Tumor Segmentation on CT Images Through Federated Semi-Supervised Learning with Dynamic Update Strategy

  • Authors: Dan Wang, Chu Han, Zhen Zhang, Zhenwei Shi, Zaiyi Liu
  • Journal: Computer Methods and Programs in Biomedicine, 2024
  • Summary: This research introduces a federated semi-supervised learning framework with a dynamic update strategy for effective lung tumor segmentation in CT imaging.

Conclusion

Professor An Zeng is a highly qualified candidate for the Best Researcher Award, given her outstanding contributions to AI in medicine, deep learning, and computational diagnostics. Her strong publication record, international research experience, and interdisciplinary approach make her an excellent nominee. While expanding clinical collaborations and citation impact would further enhance her profile, her cutting-edge research already positions her as a leader in medical AI applications.

Sidra Jubair | Machine Learning | Best Researcher Award

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Ms. Sidra Jubair | Machine Learning | Best Researcher Award

Ph.D Student at Dalian University of Technology, China📖

Dr. Sidra Jubair is a dedicated researcher in the field of applied mathematics, currently pursuing her Ph.D. at the School of Mathematical Sciences, Dalian University of Technology, China, under the supervision of Prof. Jie Yang. Her research focuses on machine learning, computational fluid dynamics, and neurocomputing. With a strong academic background and numerous high-impact publications in top-tier journals, she is committed to advancing knowledge in data-driven scientific computation.

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Education Background🎓

  1. Ph.D. in Applied Mathematics (2019 – Present)
    • Dalian University of Technology, China
    • Project: Imbalanced Data Learning through Examples and Classifiers
  2. M.Sc. in Applied Mathematics (2016 – 2018)
    • Hazara University, Pakistan
    • Supervised by Prof. Dr. Muhammad Shahzad
  3. B.Sc. in Mathematics (2011 – 2015)
    • International Islamic University, Islamabad, Pakistan

Professional Experience🌱

Dr. Sidra Jubair has extensive research experience in applied mathematics, focusing on the integration of computational intelligence with fluid dynamics. She has collaborated with researchers globally and contributed to high-impact scientific journals. Her work primarily revolves around machine learning applications in engineering and environmental sciences. Additionally, she has actively participated in international conferences and workshops, sharing her insights on topics such as neurocomputing and imbalanced data learning.

Research Interests🔬

Her research interests include:

  • Neurocomputing
  • Computational Fluid Dynamics
  • Imbalanced Data Learning
  • Machine Learning Applications in Engineering

Author Metrics

Dr. Sidra Jubair has established a strong research presence in the fields of applied mathematics, machine learning, and computational fluid dynamics. Her scholarly contributions have garnered significant recognition, with over 900 citations on Google Scholar, reflecting the impact and relevance of her work within the scientific community. She holds an H-index of 15, demonstrating the consistent influence and citation of her research, and an i10-index of 12, highlighting her ability to produce multiple highly cited publications. Dr. Jubair has published extensively in top-tier, high-impact journals, including Information Processing and Management, Alexandria Engineering Journal, and Applied Water Sciences, with impact factors reaching up to 7.4. Her research on imbalanced data learning and computational modeling has been widely acknowledged, contributing valuable insights to the advancement of data-driven scientific computation.

Publications Top Notes 📄

1. Mixed convective flow of hybrid nanofluid over a heated stretching disk with zero-mass flux using the modified Buongiorno model

  • Authors: B. Ali, N.K. Mishra, K. Rafique, S. Jubair, Z. Mahmood, S.M. Eldin
  • Journal: Alexandria Engineering Journal
  • Volume: 72
  • Pages: 83-96
  • Citations: 74
  • Year: 2023

2. Numerical simulation of the nanofluid flow consists of gyrotactic microorganism and subject to activation energy across an inclined stretching cylinder

  • Authors: H.A. Othman, B. Ali, S. Jubair, M. Yahya Almusawa, S.M. Aldin
  • Journal: Scientific Reports
  • Volume: 13, Issue 1
  • Article Number: 7719
  • Citations: 51
  • Year: 2023

3. MHD flow of nanofluid over moving slender needle with nanoparticles aggregation and viscous dissipation effects

  • Authors: B. Ali, S. Jubair, D. Fathima, A. Akhter, K. Rafique, Z. Mahmood
  • Journal: Science Progress
  • Volume: 106, Issue 2
  • Article Number: 00368504231176151
  • Citations: 42
  • Year: 2023

4. Boundary layer and heat transfer analysis of mixed convective nanofluid flow capturing the aspects of nanoparticles over a needle

  • Authors: B. Ali, S. Jubair, L.A. Al-Essa, Z. Mahmood, A. Al-Bossly, F.S. Alduais
  • Journal: Materials Today Communications
  • Volume: 35
  • Article Number: 106253
  • Citations: 38
  • Year: 2023

5. Numerical investigation of heat source induced thermal slip effect on trihybrid nanofluid flow over a stretching surface

  • Authors: B. Ali, S. Jubair, A. Aluraikan, M. Abd El-Rahman, S.M. Eldin, H.A.E.W. Khalifa
  • Journal: Results in Engineering
  • Volume: 20
  • Article Number: 101536
  • Citations: 37
  • Year: 2023

Conclusion

Dr. Sidra Jubair is a highly deserving candidate for the Best Researcher Award. Her outstanding research in applied mathematics, particularly in machine learning and computational fluid dynamics, has had a profound impact on both the academic and scientific communities. Her consistent publication in top-tier journals and strong research metrics underscore her ability to produce high-quality, impactful research. While there are opportunities for her to broaden the scope of her research through real-world applications and interdisciplinary collaborations, her current work demonstrates tremendous potential for further breakthroughs in applied mathematics and engineering.

Her dedication to advancing knowledge in data-driven scientific computation, coupled with her innovative approaches, makes her an ideal candidate for the Best Researcher Award.

Hadi Sadoghi Yazdi | Machine Learning | Best Researcher Award

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Prof. Hadi Sadoghi Yazdi | Machine Learning | Best Researcher Award

Corresponding Author, at ferdowsi University of mashhad, Iran📖

Prof. Hadi Sadoghi Yazdi is an accomplished academic and researcher in the field of electronic engineering, with extensive experience in pattern recognition, machine learning, and signal processing. As a Professor at Ferdowsi University of Mashhad, he leads cutting-edge research in artificial intelligence, overseeing projects that have resulted in numerous patents and products in diverse industries. His expertise extends to both academic and industrial sectors, where he has made significant contributions to the development of smart systems, including applications in health, security, and automation. Dr. Yazdi is also a key figure in advancing technology in the military and defense sectors, with his work in missile tracking and vision-based systems influencing both national and international technological advancements.

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Education Background🎓

Prof. Hadi Sadoghi Yazdi has a strong educational foundation in electronic engineering, having completed his PhD in Electronic Engineering at Tarbiat Modares University, Tehran in 2005. His doctoral research focused on advanced topics in electronic systems, which significantly contributed to his expertise in areas such as pattern recognition and machine learning. Prior to his PhD, he earned a Master’s degree in Electronic Engineering from the same university in 1996, where he honed his skills in signal processing and electronics applications. Dr. Yazdi’s journey in engineering began with a Bachelor’s degree in Electronic Engineering from Ferdowsi University of Mashhad, which he completed in 1994. This educational background laid the groundwork for his distinguished career in both academia and industry, where he has been at the forefront of research in machine vision, signal processing, and artificial intelligence.

Professional Experience🌱

Dr. Yazdi is currently a Professor and Deputy of Research and Technology at Ferdowsi University of Mashhad, a position he has held since 2014. He has served in various academic roles, including Associate Professor (2009-2014) and Assistant Professor (2008-2009) at the same institution. Additionally, Dr. Yazdi supervises the Pattern Recognition Lab at Ferdowsi University, a leading research facility in the field. Prior to his tenure at Ferdowsi University, he held faculty positions at Hakim Sabzevari University (2005-2008), where he was also the Head of the Engineering Department, as well as teaching roles at several other prestigious institutions, including Kashmar University, Tabriz University, Tehran University, Arak University, and Shariati University.

In addition to his academic work, Dr. Yazdi has a strong background in research and development, having worked in industry on numerous projects involving artificial intelligence, electronic systems, and military technologies. He has held senior research and leadership positions in companies such as LG Madiran, Military Industries, and the Defense Industrials, where he was involved in the design and development of complex systems such as missile tracking, electronic fault finding, and smart systems for medical and security applications

Research Interests🔬

Dr. Yazdi’s research interests encompass a broad range of topics, including:

  • Pattern Recognition
  • Machine Learning
  • Machine Vision
  • Signal Processing

His work focuses on developing innovative solutions in these areas, with applications ranging from industrial automation and medical diagnostics to smart systems and security technologies.

Author Metrics and Achievements 

Dr. Yazdi has authored and co-authored numerous research papers and holds several patents in the fields of artificial intelligence and electronics. Some of his key patents include the development of smart systems for applications such as fire detection, facial recognition, and traffic light control. His academic contributions, particularly in pattern recognition and machine learning, have been pivotal in shaping modern approaches to these fields. He has worked on over 40 research projects, both in academia and industry, demonstrating his leadership and impact on technological development.

Publications Top Notes 📄

1.Kalman filtering based on the maximum correntropy criterion in the presence of non-Gaussian noise

  • Authors: R Izanloo, SA Fakoorian, HS Yazdi, D Simon
  • Published: 2016 Annual Conference on Information Science and Systems (CISS), pp. 500-505
  • Year: 2016
  • Citations: 243
  • Summary: This paper introduces a Kalman filter that utilizes the maximum correntropy criterion (MCC) to handle non-Gaussian noise in dynamic systems, providing a more robust estimation framework for real-time filtering in challenging environments.

2. ECG arrhythmia classification with support vector machines and genetic algorithm

  • Authors: JA Nasiri, M Naghibzadeh, HS Yazdi, B Naghibzadeh
  • Published: 2009 Third UKSim European Symposium on Computer Modeling and Simulation, pp. 187-192
  • Year: 2009
  • Citations: 171
  • Summary: This work explores the classification of ECG arrhythmias using support vector machines (SVM) optimized by a genetic algorithm (GA), demonstrating how this combined approach enhances the accuracy of detecting different types of arrhythmias.

3. An eigenspace-based approach for human fall detection using integrated time motion image and neural network

  • Authors: H Foroughi, A Naseri, A Saberi, HS Yazdi
  • Published: 2008 9th International Conference on Signal Processing, pp. 1499-1503
  • Year: 2008
  • Citations: 127
  • Summary: This paper proposes an eigenspace-based method for human fall detection by integrating time-motion images with a neural network. The approach enhances detection accuracy, providing a reliable system for fall detection in various applications.

4. Probabilistic Kalman filter for moving object tracking

  • Authors: F Farahi, HS Yazdi
  • Published: Signal Processing: Image Communication 82, 115751
  • Year: 2020
  • Citations: 101
  • Summary: This research introduces a probabilistic Kalman filter designed for tracking moving objects. The proposed method enhances the ability of Kalman filters to track objects in uncertain environments, improving real-time tracking applications in various domains.

5. IRAHC: Instance reduction algorithm using hyperrectangle clustering

  • Authors: J Hamidzadeh, R Monsefi, HS Yazdi
  • Published: Pattern Recognition, 48(5), pp. 1878-1889
  • Year: 2015
  • Citations: 90
  • Summary: This paper presents an instance reduction algorithm (IRAHC) that utilizes hyperrectangle clustering to improve the efficiency and effectiveness of machine learning algorithms, particularly for large datasets. The proposed method enhances the performance of classifiers by reducing the number of instances required for training.

Conclusion

Prof. Hadi Sadoghi Yazdi is a deserving candidate for the Best Researcher Award, owing to his significant contributions to the fields of pattern recognition, machine learning, and signal processing. His innovative solutions and patents, particularly in AI and electronics, have far-reaching implications for industries such as healthcare, security, and defense. As an academic leader, Prof. Yazdi has not only advanced theoretical research but also bridged the gap between academia and industry, shaping modern technological landscapes. With continued interdisciplinary collaboration and a focus on solving global challenges, his impact on the world of engineering and technology will undoubtedly continue to grow. His leadership in both research and education makes him a standout figure worthy of the Best Researcher Award.

Lechen Li | Data Science | Best Researcher Award

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Assist. Prof. Dr. Lechen Li | Data Science | Best Researcher Award

Assistant Professor, at Hohai University, China📖

Lechen Li, Ph.D., is a multidisciplinary researcher and engineer specializing in Engineering Mechanics and Data Science. With a strong foundation in computational mechanics and deep learning, he has contributed significantly to smart grid development, structural health monitoring, and intelligent systems. His award-winning work has been presented at leading international conferences and has garnered recognition for its impact on sustainable infrastructure and advanced engineering solutions.

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Education Background🎓

Dr. Lechen Li is an accomplished scholar in Engineering Mechanics and Data Science with extensive academic and research experience. He earned his Ph.D. in Engineering Mechanics from Columbia University in 2023, achieving an impressive GPA of 3.889/4.0. His doctoral research spanned smart grid development, computational structural dynamics, and data-driven system control. Prior to this, he completed a Master of Science in Data Science at Columbia University in 2019, where he excelled academically with a GPA of 3.917/4.0 and received the prestigious Robert A.W. and Christine S. Carleton Scholarship. Dr. Li’s academic journey began at Sichuan University, China, where he earned his Bachelor’s degree in Engineering Mechanics in 2018. Notably, he secured first prizes in the Zhou Peiyuan National Mechanics Modeling Contest and the First Prize Scholarship twice.

Professional Experience🌱

Dr. Li brings a wealth of industry experience that complements his academic achievements. At Colombo International Container Terminals (CICT) in Sri Lanka, he served as a Data Research Analyst, where he developed machine learning models to optimize port logistics and transportation planning using a dynamic reinforcement learning framework. Earlier, during his tenure as a CAE Analyst at the National Institute of Water, Energy and Transportation in China, Dr. Li conducted advanced simulations using the Extended Finite Element Method (XFEM), providing valuable insights into lateral pile-soil pressure distribution on pile groups.

Research Interests🔬

Dr. Li’s research is centered on:

  • Structural Health Monitoring and Control: Developing advanced deep-learning frameworks for real-time system identification and damage detection.
  • Data-Driven Dynamics: Applying machine learning and signal processing techniques for smart grid optimization and time-series forecasting.
  • Computational Mechanics: Leveraging finite element analysis and XFEM for solving complex engineering problems.
  • Sustainability and Infrastructure: Innovating intelligent systems for energy-efficient monitoring and optimization.

Author Metrics 

  • Publications: Dr. Li has co-authored numerous papers in high-impact journals and conferences, including presenting at the 8th World Conference on Structural Control and Monitoring, where he received the Best Conference Paper Award.
  • Citations: His publications have been widely cited, reflecting the practical and theoretical contributions of his research.
  • Academic Awards: Best Paper Award (8WCSCM, 2022), First Prize in Zhou Peiyuan National Mechanics Modeling Contest (2017).

Publications Top Notes 📄

1. Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features

  • Authors: L. Li, C.J. Meinrenken, V. Modi, P.J. Culligan
  • Published in: Applied Energy, 2021
  • Citations: 82
  • Summary: This study focuses on improving short-term electricity load forecasting at the apartment level. The authors developed a modified neural network model that integrates auto-regressive features to enhance prediction accuracy. The approach has implications for optimizing energy management and grid operations in residential buildings.

2.Impacts of COVID-19 related stay-at-home restrictions on residential electricity use and implications for future grid stability

  • Authors: L. Li, C.J. Meinrenken, V. Modi, P.J. Culligan
  • Published in: Energy and Buildings, 2021
  • Citations: 32
  • Summary: This paper examines the effects of COVID-19 lockdowns on residential electricity consumption patterns. The study provides insights into shifts in energy usage due to work-from-home trends and discusses the implications for grid stability and planning.

3.Structural damage assessment through a new generalized autoencoder with features in the quefrency domain

  • Authors: L. Li, M. Morgantini, R. Betti
  • Published in: Mechanical Systems and Signal Processing, 2023
  • Citations: 28
  • Summary: The research introduces a novel autoencoder model that utilizes features in the quefrency domain for structural damage detection. The methodology enhances damage assessment accuracy and offers a new perspective in signal processing for civil infrastructure health monitoring.

4. A machine learning-based data augmentation strategy for structural damage classification in civil infrastructure systems

  • Authors: L. Li, R. Betti
  • Published in: Journal of Civil Structural Health Monitoring, 2023
  • Citations: 8
  • Summary: This work proposes a machine learning-driven data augmentation technique aimed at improving structural damage classification in civil infrastructure systems. The study addresses the challenges of limited data availability in real-world scenarios and improves model robustness.

5. Experimental investigation of the dynamic mechanical properties of concrete under different strain rates and cyclic loading

  • Authors: L. Gan, Y. Liu, Z. Zhang, Z. Shen, L. Li, H. Zhang, H. Jin, W. Xu
  • Published in: Case Studies in Construction Materials, 2024
  • Citations: 4
  • Summary: This experimental study explores the dynamic mechanical behavior of concrete under varying strain rates and cyclic loading conditions. The findings contribute to understanding the material’s performance in diverse loading scenarios, which is crucial for construction and structural design.

Conclusion

Dr. Lechen Li is undoubtedly a highly deserving candidate for the Best Researcher Award. His innovative contributions to engineering mechanics, data science, and structural health monitoring, combined with his solid academic background, make him a strong contender. His research not only pushes the boundaries of technology but also has significant real-world implications for energy management, infrastructure sustainability, and smart grid optimization.

While there are areas where he can expand his influence—such as increasing collaborations with industry, diversifying research, and engaging more broadly with the public—his current achievements already demonstrate his potential for continued leadership in these fields. His work is set to contribute substantially to the next generation of intelligent systems, and with continued focus on bridging academia and industry, Dr. Li will undoubtedly remain at the forefront of his field.

Hence, Dr. Lechen Li’s selection for the Best Researcher Award is both well-earned and a recognition of his future promise as a trailblazer in engineering and data science.

Nithya Rekha Sivakumar | Deep Learning | Best Researcher Award

Posted on by ResearchW

Dr. Nithya Rekha Sivakumar | Deep Learning | Best Researcher Award

Associate Professor, Princess Nourah Bint Abdulrahman University, Saudi Arabia📖

Dr. Nithya Rekha Sivakumar is an accomplished academician and researcher, currently serving as an Associate Professor of Computer Science at the College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia. She holds a Ph.D. in Computer Science from Periyar University, India, specializing in Mobile Computing and Wireless Networks with Fuzzy and Rough Set Techniques, funded by a prestigious UGC BSR Fellowship. Dr. Sivakumar also earned her M.Phil. in Data Mining, MCA in Computer Applications, and B.Sc. in Computer Science. With over 15 years of academic experience, she has served in diverse roles across reputed institutions in India and Saudi Arabia. Her research interests include wireless networks, mobile computing, data mining, and intelligent systems, with extensive contributions as a researcher, reviewer, and speaker in international conferences and journals. A recipient of multiple awards, including the “Best Distinguished Researcher Award,” she has secured research grants and actively evaluates Ph.D. theses globally. Dr. Sivakumar is also a member of IEEE and IAENG and continues to contribute to advancements in computing through teaching, research, and scholarly activities.

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Google Scholar Profile

Education Background🎓

Dr. Rekha earned her Ph.D. in Computer Science from Periyar University, India, in 2014, supported by the prestigious UGC BSR Fellowship. Her doctoral research focused on mobile computing and wireless networks with fuzzy and rough set techniques. She also holds an M.Phil. in Computer Science from PRIST University (2009), an MCA from IGNOU (2007), and a B.Sc. in Computer Science from Bharathiar University (1996).

Professional Experience🌱

Dr. Rekha has over 15 years of academic and research experience. She has been with Princess Nourah Bint Abdul Rahman University since 2017, progressing from Assistant to Associate Professor. Prior to this, she served as an Assistant Professor at Qassim Private Colleges, Saudi Arabia, and held teaching roles in leading Indian institutions such as Vivekanandha College of Arts and Sciences and Excel Business School. She has also contributed to non-academic roles, including as a Java Programmer and high school teacher.

Research and Service🔬

Dr. Rekha’s research interests span mobile computing, e-governance, and advanced data mining techniques. She has evaluated over 20 Ph.D. theses as a foreign examiner and served as a reviewer for esteemed journals such as IEEE Access, Springer, and Elsevier. A sought-after speaker, she has been invited to international seminars and conferences across the globe, sharing her expertise in computational science and emerging technologies.

Dr. Rekha continues to inspire through her teaching, research, and unwavering commitment to advancing the field of computer science.

Author Metrics 

Dr. Nithya Rekha Sivakumar has an impressive author profile, with a strong presence in international research communities. She has published over 40 papers in reputed journals and conferences, many indexed in Scopus and Web of Science, reflecting her contributions to fields like wireless networks, mobile computing, and data mining. Her work has garnered significant recognition, with an h-index of 12 and over 400 citations, underscoring the impact and relevance of her research. She has authored and co-authored book chapters published by renowned publishers such as Springer and Wiley, further highlighting her expertise. As a sought-after reviewer for top-tier journals, she actively contributes to maintaining the quality of scientific publications. Dr. Sivakumar’s research outputs, combined with her active engagement in scholarly dissemination, establish her as a leading voice in her domain.

Honors and Research Grants

Dr. Rekha has received numerous accolades, including the “Best Distinguished Researcher Award” (2015-2016) and multiple research grants from Princess Nourah Bint Abdul Rahman University, amounting to SAR 40,000 through the Fast Track Research Funding program. She has also been recognized for her doctoral research by the University Grants Commission, India, and secured a travel grant from the Indian Department of Science and Technology to present her work internationally

Publications Top Notes 📄

“Increasing Fault Tolerance Ability and Network Lifetime with Clustered Pollination in Wireless Sensor Networks”

  • Authors: TKNVD Achyut Shankar, Nithya Rekha Sivakumar, M. Sivaram, A. Ambikapathy
  • Journal: Journal of Ambient Intelligence and Humanized Computing
  • Year: 2020
  • Impact: The paper focuses on improving the fault tolerance and lifespan of wireless sensor networks through an innovative clustered pollination-based approach.

“Stabilizing Energy Consumption in Unequal Clusters of Wireless Sensor Networks”

  • Author: NR Sivakumar
  • Journal: Computational Materials and Continua
  • Volume: 64
  • Pages: 81-96
  • Year: 2020
  • Impact: This paper addresses energy stabilization in wireless sensor networks by proposing techniques to manage energy distribution across unequal clusters, enhancing network sustainability.

“Enhancing Network Lifespan in Wireless Sensor Networks Using Deep Learning-based Graph Neural Network”

  • Authors: NR Sivakumar, SM Nagarajan, GG Devarajan, L Pullagura, et al.
  • Journal: Physical Communication
  • Volume: 59
  • Article No.: 102076
  • Year: 2023
  • Impact: The paper investigates how deep learning-based graph neural networks can be used to enhance the lifespan of wireless sensor networks, marking a significant contribution to AI-powered network optimization.

“Simulation and Evaluation of the Performance on Probabilistic Broadcasting in FSR (Fisheye State Routing) Routing Protocol Based on Random Mobility Model in MANET”

  • Authors: NR Sivakumar, C Chelliah
  • Conference: 2012 Fourth International Conference on Computational Intelligence
  • Year: 2012
  • Impact: This study explores the performance of the Fisheye State Routing (FSR) protocol in mobile ad hoc networks (MANETs), with an emphasis on the effects of random mobility models on network behavior.

“An IoT-based Big Data Framework Using Equidistant Heuristic and Duplex Deep Neural Network for Diabetic Disease Prediction”

  • Authors: NR Sivakumar, FKD Karim
  • Journal: Journal of Ambient Intelligence and Humanized Computing
  • Year: 2023
  • Impact: This paper presents an IoT-based framework utilizing big data and deep learning for predicting diabetic diseases, offering a new approach to healthcare prediction systems through advanced technologies.

Conclusion

Dr. Nithya Rekha Sivakumar is a deserving candidate for the Best Researcher Award. Her impressive research accomplishments, strong publication record, innovative contributions to wireless networks and mobile computing, and active engagement in the academic community make her an outstanding researcher. Although there are areas for improvement, particularly in interdisciplinary collaboration and public outreach, her overall research trajectory and impact are exemplary. Dr. Sivakumar’s continuous pursuit of excellence in her field and her ability to address contemporary challenges in mobile computing, data mining, and wireless networks position her as a leading researcher in her domain. She is highly recommended for the Best Researcher Award.