Prof . Len Gelman | Artificial Intelligence | Best Researcher Award

Prof . Len Gelman | Artificial Intelligence | Best Researcher Award

Prof. Len Gelman , University of Huddersfield , United Kingdom

Professor Len Gelman 🇬🇧 is a globally recognized expert in signal processing and condition monitoring 🔍. He currently serves as Chair Professor and Director at the University of Huddersfield 🏫. With over two decades of academic leadership, he has significantly contributed to vibro-acoustics and non-destructive testing 🔧. A Fellow of multiple prestigious organizations 🌐, Prof. Gelman’s international collaborations span across Europe, Asia, and the USA 🌏. His innovations have advanced aerospace and medical diagnostics ✈️🧬. He continues to lead global initiatives and research committees, shaping the future of engineering diagnostics and reliability technologies 🔬🛠️.

Professional Profile

SCOPUS

Education and Experience 

Prof. Len Gelman holds a PhD and Doctor of Science (Habilitation) 🎓, with BSc (Hons) and MSc (Hons) degrees in engineering 📘. He is a British citizen 🇬🇧. Since 2017, he has been a Professor and Chair at the University of Huddersfield 🏛️. Prior to that, he served at Cranfield University (2002–2017) as Chair in Vibro-Acoustical Monitoring 🔊. His distinguished academic journey includes visiting professorships in China 🇨🇳, Denmark 🇩🇰, Poland 🇵🇱, Spain 🇪🇸, Italy 🇮🇹, and the USA 🇺🇸. Prof. Gelman combines deep technical expertise with global educational outreach 🌍👨‍🏫.

Professional Development 

Prof. Gelman has held key international leadership roles including Chair of the International Scientific Committee of the Condition Monitoring Society 🌐. He is a Fellow of BINDT, IAENG, IDE, and HEA 🎖️, and an Academician of the Academy of Sciences of Applied Radio Electronics 🧠. He chairs award and honors committees for top acoustics and vibration institutions 🏅. As Visiting Professor at Tsinghua, Jiao Tong, and Aalborg Universities, among others 🎓, he mentors emerging researchers globally 🌎. Prof. Gelman’s commitment to professional excellence shapes the advancement of diagnostic technologies and engineering education 📈🔧.

Research Focus 

Prof. Gelman’s research focuses on signal processing, vibro-acoustics, and condition monitoring of engineering systems 🔍🔊. His work spans non-destructive testing (NDT), fault diagnostics, and performance optimization in sectors such as aerospace, healthcare, and manufacturing ✈️🏥🏭. He develops advanced algorithms for fault detection and predictive maintenance using machine learning and big data 🧠📊. His interdisciplinary approach benefits both industry and academia 🌐🔬. Prof. Gelman also pioneers applications in medical diagnostics and intelligent systems for real-time monitoring 🧬⚙️. His innovations contribute to safer, more efficient engineering systems across global platforms 🌍🚀.

Awards and Honors 

Prof. Gelman has received numerous prestigious awards for innovation and research excellence 🏅. These include the Rolls-Royce Innovation Award (2012, 2019) ✈️, William Sweet Smith Prize by IMechE 🛠️, and COMADIT Prize by BINDT for impactful contributions to condition monitoring 🧲. He also received Best Paper Awards at CM/MFPT conferences 📄 and recognition from the USA Navy and Acoustical Society of America 🇺🇸🔊. His European and UK fellowships support cutting-edge human capital projects 🧠🇪🇺. He has chaired international committees in NDT and acoustics, continuing to shape future technologies through global leadership and innovation 🌐👨‍🔬.

Publication Top Notes

1. Vibration Analysis of Rotating Porous Functionally Graded Material Beams Using Exact Formulation

  • Citation: Amoozgar, M.R., & Gelman, L.M. (2022). Vibration analysis of rotating porous functionally graded material beams using exact formulation. Journal of Vibration and Control, 28(21–22), 3195–3206. https://doi.org/10.1177/10775463211027883Nottingham Repository+1SAGE Journals+1

  • Summary: This study investigates the free vibration behavior of rotating functionally graded material (FGM) beams with porosity, employing geometrically exact fully intrinsic beam equations. The research considers both even and uneven porosity distributions to simulate manufacturing imperfections. Findings reveal that material gradation and porosity significantly influence natural frequencies and mode shapes, emphasizing the necessity of accounting for these factors in the design and analysis of rotating FGM structures. Huddersfield Research Portal+2SAGE Journals+2Nottingham Repository+2

2. Vibration Health Monitoring of Rolling Bearings Under Variable Speed Conditions by Novel Demodulation Technique

  • Citation: Zhao, D., Gelman, L.M., Chu, F., & Ball, A.D. (2021). Vibration health monitoring of rolling bearings under variable speed conditions by novel demodulation technique. Structural Control and Health Monitoring, 28(2), e2672. https://doi.org/10.1002/stc.2672Wiley Online Library

  • Summary: Addressing the challenges of diagnosing rolling bearing faults under variable speed conditions, this paper introduces an optimization-based demodulation transform method. The technique effectively estimates fault characteristic frequencies with weak amplitudes and adapts to time-varying rotational speeds. Validation through simulations and experimental data demonstrates the method’s superior diagnostic capabilities compared to existing approaches. Huddersfield Research Portal+1Wiley Online Library+1

3. Novel Method for Vibration Sensor-Based Instantaneous Defect Frequency Estimation for Rolling Bearings Under Non-Stationary Conditions

  • Citation: Zhao, D., Gelman, L.M., Chu, F., & Ball, A.D. (2020). Novel method for vibration sensor-based instantaneous defect frequency estimation for rolling bearings under non-stationary conditions. Sensors, 20(18), 5201. https://doi.org/10.3390/s20185201MDPI

  • Summary: This research presents a novel approach for estimating instantaneous defect frequencies in rolling bearings operating under non-stationary conditions. Utilizing vibration sensor data, the method enhances the accuracy of defect frequency estimation, facilitating improved fault diagnosis in dynamic operational environments. MDPI

4. Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing

  • Citation: Ciszewski, T., Gelman, L.M., & Ball, A.D. (2020). Novel fault identification for electromechanical systems via spectral technique and electrical data processing. Electronics, 9(10), 1560. https://doi.org/10.3390/electronics9101560MDPI

  • Summary: This paper introduces an innovative method for fault identification in electromechanical systems by integrating spectral analysis with electrical data processing. The approach enhances the detection and diagnosis of faults, contributing to the reliability and efficiency of electromechanical system operations. MDPI

5. Novel Prediction of Diagnosis Effectiveness for Adaptation of the Spectral Kurtosis Technology to Varying Operating Conditions

  • Citation: Kolbe, S., Gelman, L.M., & Ball, A.D. (2021). Novel prediction of diagnosis effectiveness for adaptation of the spectral kurtosis technology to varying operating conditions. Sensors, 21(20), 6913. https://doi.org/10.3390/s21206913PMC

  • Summary: This study proposes two novel consistency vectors combined with machine learning algorithms to adapt spectral kurtosis technology for optimal gearbox damage diagnosis under varying operating conditions. The approach enables computationally efficient online condition monitoring by predicting diagnosis effectiveness, thereby improving maintenance strategies.

Conclusion

Professor Len Gelman exemplifies the ideal candidate for the Best Researcher Award due to his groundbreaking contributions to condition monitoring, signal processing, and diagnostic technologies. His work not only advances academic knowledge but also addresses critical industry challenges in aerospace, healthcare, and manufacturing. With a sustained record of high-impact research, international leadership, and technological innovation, he stands out as a world-class researcher whose work continues to benefit both academia and society.

 

Jaecheol Ha | Computer Science | Best Researcher Award

Prof .  Jaecheol Ha | Computer Science | Best Researcher Award

Professor at Hoseo University , South Korea

Professor Jaecheol Ha is a seasoned academic with a Ph.D. in Electronics Engineering from Kyungpook National University and over 25 years of research and teaching experience. Currently a full professor at Hoseo University, he has also held academic positions at Korea Nazarene University and was a visiting researcher at Purdue University, USA. His research focuses on critical areas such as AI security, mobile network security, hardware security, and side-channel attacks—fields of growing importance in today’s digital world. As the honorary president of the Korea Institute of Information and Cryptography (KIISC), he demonstrates recognized leadership in the cybersecurity research community. While his academic background and research interests are highly relevant, more information on his publication record, research impact, and mentorship contributions would further strengthen his case. Nonetheless, based on the available information, Professor Ha presents a strong and credible profile for the Best Researcher Award, particularly in the domain of cybersecurity.

Professional Profile 

Education🎓

Professor Jaecheol Ha has a solid academic foundation in electronics engineering, having earned his Bachelor’s (BE) in 1989, Master’s (ME) in 1993, and Ph.D. in 1998 from Kyungpook National University in the Republic of Korea. His progression through all three degrees at a single institution reflects a consistent and focused commitment to his field of study. Kyungpook National University is recognized for its strong engineering programs, providing him with a rigorous education and research training environment. His doctoral studies likely laid the groundwork for his later specialization in areas such as AI security and hardware-based cryptographic methods. This strong educational background has supported his successful academic career, enabling him to contribute meaningfully to research and teaching. His education not only equipped him with deep technical knowledge but also prepared him to take on leadership roles in academic and research institutions, both domestically and internationally.

Professional Experience📝

Professor Jaecheol Ha has extensive professional experience spanning over two decades in academia and research. He is currently a full professor in the Division of Computer Engineering at Hoseo University in Asan, Republic of Korea, where he plays a key role in teaching and research. Prior to this, from 1998 to 2006, he served as a professor in the Department of Information and Communication at Korea Nazarene University in Cheonan. His academic career reflects a strong commitment to education and research in the fields of computer engineering and cybersecurity. In 2014, he broadened his international experience by working as a visiting researcher at Purdue University in the United States, further enhancing his global academic perspective. In addition to his teaching and research roles, he currently serves as the honorary president of the Korea Institute of Information and Cryptography (KIISC), a position that highlights his leadership and influence in the Korean cybersecurity research community.

Research Interest🔎

Professor Jaecheol Ha’s research interests lie in the critical and rapidly evolving field of cybersecurity, with a focus on AI security, mobile network security, hardware security, and side-channel attacks. His work addresses some of the most pressing challenges in digital security, particularly as emerging technologies like artificial intelligence and mobile communication continue to expand. By exploring vulnerabilities in hardware and communication systems, as well as developing methods to protect against side-channel attacks, his research contributes to building more resilient and secure digital infrastructures. His interest in AI security reflects a forward-thinking approach, recognizing the increasing integration of AI in sensitive systems and the corresponding need for robust protective measures. Through his work, Professor Ha seeks to bridge theoretical understanding with practical applications, providing solutions that can be implemented in real-world systems. His research not only supports academic advancement but also contributes to national and global efforts to strengthen cybersecurity.

Award and Honor🏆

Professor Jaecheol Ha has received recognition for his contributions to the field of cybersecurity through his leadership role as the honorary president of the Korea Institute of Information and Cryptography (KIISC). This prestigious position reflects his respected status within the academic and research communities, as well as his long-standing commitment to advancing knowledge in information security. While specific awards or honors are not listed, his appointment to such a significant role within a national institute suggests a high level of trust and acknowledgment by his peers. It highlights his influence in shaping research directions and policies in cryptography and cybersecurity in Korea. His professional journey, including his international research collaboration at Purdue University, also indicates recognition of his expertise beyond national boundaries. These honors affirm his impact as a leader and researcher, underscoring his suitability for further accolades such as the Best Researcher Award in his field of specialization.

Research Skill🔬

Professor Jaecheol Ha possesses a wide range of research skills that are crucial for tackling complex problems in the field of cybersecurity. His expertise spans several critical areas, including AI security, mobile network security, hardware security, and side-channel attacks. With a deep understanding of both theoretical and practical aspects of these fields, he is skilled at identifying vulnerabilities in systems and developing innovative solutions to mitigate them. His ability to bridge the gap between cutting-edge research and real-world applications demonstrates his strong problem-solving capabilities. Additionally, his international research experience, particularly as a visiting researcher at Purdue University, indicates a high level of adaptability and collaboration in global research environments. His leadership as honorary president of the Korea Institute of Information and Cryptography (KIISC) further highlights his ability to mentor, guide, and foster collaboration among researchers, strengthening his research skills in both individual and team-based contexts.

Conclusion💡

Professor Jaecheol Ha appears to be a well-qualified and experienced researcher with a strong focus on cybersecurity, leadership experience, and international exposure. These factors support his eligibility for a Best Researcher Award, especially if the focus is on long-term contribution and domain impact.

However, to make a fully confident endorsement, it would be ideal to see quantitative evidence of research excellence — such as high-impact publications, citations, or funded projects. If such data exists and supports the narrative, then he is a strong and suitable candidate for this award.

Publications Top Noted✍

  1. Title: SSIM-Based Autoencoder Modeling to Defeat Adversarial Patch Attacks
    Authors: Seungyeol Lee, Seongwoo Hong, Gwangyeol Kim, Jaecheol Ha
    Year: 2024
    Citations: 1
  2. Title: Implementation of Disassembler on Microcontroller Using Side-Channel Power Consumption Leakage
    Authors: Daehyeon Bae, Jaecheol Ha
    Year: 2022
    Citations: 6
  3. Title: Deep Learning-based Attacks on Masked AES Implementation
    Authors: Daehyeon Bae, Jongbae Hwang, Jaecheol Ha
    Year: 2022
    Citations: 1
  4. Title: Performance Metric for Differential Deep Learning Analysis
    Authors: Daehyeon Bae, Jaecheol Ha
    Year: 2021
    Citations: 26

 

Prof. Dr. Wei Fang | Analytics Award | Best Researcher Award

Prof. Dr. Wei Fang | Analytics Award | Best Researcher Award

Prof. Dr. Wei Fang, Nanjing University of Information Science & Technology, China

Prof. Dr. Wei Fang is a Professor in the Department of Computer Science at Nanjing University of Information Science & Technology, China, and a member of the State Key Laboratory for Novel Software Technology, Nanjing University. He holds a Ph.D. and M.Sc. in Computer Science from Soochow University. Wei was a visiting scholar at the University of Florida in 2015-2016. His research interests include Artificial Intelligence, Big Data, Data Mining, and Meteorological Information Processing. He has led several research projects funded by the National Natural Science Foundation of China and is an active reviewer for international journals. Wei is a senior member of the CCF and ACM.

Professional Profile:

GOOGLE SCHOLAR

ORCID

SCOPUS

Summary of Suitability for Best Researcher Award – Prof. Wei Fang

Prof. Wei Fang of Nanjing University of Information Science & Technology stands out as a highly meritorious candidate for the Best Researcher Award. With a solid academic foundation, national and international research exposure, and extensive contributions in Artificial Intelligence, Big Data, Computer Vision, and Applied Meteorology, his work bridges theoretical innovation with real-world application.

🎓 Education

  • Ph.D. in Computer Science – Soochow University, China

  • M.Sc. in Computer Science – Soochow University, China

📚 Visiting Scholar – University of Florida, USA (Faculty of Computer Science, Sept 2015 – Sept 2016)

💼 Work Experience

  • 👨‍🏫 Professor, Department of Computer Science, NUIST

  • 🧪 Affiliated with the State Key Lab for Novel Software Technology, Nanjing University

  • 🤝 Program Committee Member for multiple international conferences

  • 📝 Reviewer for various international journals

  • 🌍 International Research Scientist

🏆 Achievements & Honors

  • 🧠 Recognized for impactful research in:

    • Artificial Intelligence 🤖

    • Big Data & Cloud Computing ☁️📊

    • Computer Vision 👁️

    • Applied Meteorology 🌦️

  • 🔬 Project Leader of national and industrial research projects funded by:

    • National Natural Science Foundation of China

    • Guodian Nari Nanjing Control System Co., Ltd.

    • Baoshan Iron and Steel Co., Ltd.

  • 🎖️ Senior Member of CCF (China Computer Federation) & ACM

  • 📈 Cited in SCI-indexed journals

Publication Top Notes:

A rapid learning algorithm for vehicle classification

CITED: 562

A Method for Improving CNN-Based Image Recognition Using DCGAN.

CITED: 230

Efficient feature selection and classification for vehicle detection

CITED: 220

A survey of big data security and privacy preserving

CITED: 117

Survey on research of RNN-based spatio-temporal sequence prediction algorithms

CITED: 100

Mr Seemant Tiwari | AI & ML | Best Researcher Award | 1086

Mr Seemant Tiwari | AI & ML | Best Researcher Award

Ph. D Student of Southern Taiwan University of Science and Technology, Tainan City, Taiwan,Taiwan

Seemant Tiwari graduated with a Bachelor of Technology, in Electrical Engineering from AKTU (formerly known as UPTU & GBTU), in Lucknow, Uttar Pradesh, India. He earned his Master of Technology, in Power Electronics, in May 2013 from Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, in Chennai, Tamil Nadu, India. He obtained his Post Graduate Certificate Program, in Petroleum and Natural Gas Flow Measurement & Control Techniques, in November 2013 from the Fluid Control Research Institute, in Palakkad, Kerala, India. He has been working on a Ph.D. since September 2019 at the Department of Electrical Engineering (Renewable & Intelligent Power System Laboratory) at Southern Taiwan University of Science and Technology, in Yongkang District, Tainan City, Taiwan. Before joining STUST in Taiwan, he was a lecturer in the Electrical Department at R.B.S. Polytechnic, in Agra, Uttar Pradesh, India. His current research interests include wind speed forecasting, renewable energy forecasting, and electric load forecasting.

Profile

orcid

Education

  • Bachelor of Technology (B.Tech.) in Electrical Engineering from AKTU (formerly UPTU & GBTU), Lucknow, India.
  • Master of Technology (M.Tech.) in Power Electronics from Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India (2013).
  • Post Graduate Certificate Program in Petroleum and Natural Gas Flow Measurement & Control Techniques from the Fluid Control Research Institute, Palakkad, Kerala, India (2013).

Seemant’s educational journey reflects a strong foundation in electrical engineering, complemented by specialized training in power electronics and fluid control techniques.

Experience

Before joining STUST in Taiwan, Seemant worked as a lecturer in the Electrical Department at R.B.S. Polytechnic, Agra, India. During his tenure, he focused on teaching electrical engineering fundamentals and guiding students in their practical understanding of the subject. His transition to academia in Taiwan marked a shift toward research, where he is now delving into predictive modeling for energy systems.

Awards and Recognition

Throughout his academic journey, Seemant has been recognized for his research efforts in renewable energy. He has received commendations for his innovative contributions to wind speed and electric load forecasting using AI techniques. His expertise in power systems has made him a valuable asset in his current research environment.

Academic and Professional

Seemant Tiwari graduated with a Bachelor of Technology, in Electrical Engineering from AKTU (formerly known as UPTU & GBTU), in Lucknow, UttarPradesh, India. He earned his Master of Technology, in Power Electronics, in May 2013 from Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, in Chennai, Tamil Nadu, India. He obtained his Post Graduate Certificate Program, in Petroleum and Natural Gas Flow Measurement & Control Techniques, in November 2013 from the Fluid Control Research Institute, in Palakkad, Kerala, India

Areas of Research: AI & ML

Seemant has been working on a Ph.D. since September 2019 at the Department of Electrical Engineering (Renewable & Intelligent Power System Laboratory) at Southern Taiwan University of Science and Technology, in Yongkang District, Tainan City, Taiwan. Before joining STUST in Taiwan, he was a lecturer in the Electrical Department at R.B.S. Polytechnic, in Agra, Uttar Pradesh, India. His current research interests include wind speed forecasting,renewable energy forecasting, and electric load forecasting.

 Publications: 

  • Big Data Analytics: Energy Forecasting Computational Intelligence Methods
  • Mathematics for Machine Learning
  • A Survey on Big Data Analytics for Load Prediction in Smart Grids
  • Industries Application of Type-2 Fuzzy Logic
  • Segmentation and Clustering of Time Series Data
  • Modification Metric of Class Document on Naïve Bayes for Sentiment Analysis of Online Learning Evaluation
  • Artificial Intelligence (AI) in the Sustainable Energy Sector
  • Implications of Machine Learning in Renewable Energy
  • Applications of Smart Technologies Regarding Promoting Energy Efficiency and Sustainable Resource Utilization
  • Approaches Involving Big Data Analytics Using Machine Learning
  • Electrical Load Forecasting Methodologies and Approaches
    • Conference Paper
    • Published in Eurasia Proceedings of Science, Technology, Engineering and Mathematics, 2022
    • DOI: 10.55549/epstem.1218629
  • Supervised Machine Learning: A Brief Introduction
    • Conference Paper
    • Published in Proceedings of the International Conference on Virtual Learning, 2022
    • DOI: 10.58503/icvl-v17y202218
  • Wind Speed Forecasting Methods for Wind Energy Generation
  • Concepts and Strategies for Machine Learning
    • Book Chapter
    • Published in Current Studies in Basic Sciences, Engineering and Technology, 2022
    • ISBN: 978-605-81654-2-7
  • Artificial Intelligence Implications in Engineering and Production
  • Various Models for Predicting Wind Energy Production

Conclusion

Given his focus on renewable energy, AI, and practical applications in energy forecasting, Mr. Seemant Tiwari’s research has the potential to drive significant community-level impact, making him a fitting candidate for the Research for Community Impact Award.