Chao Liu | Graph Neural Networks | Research Excellence Award

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Mr. Chao Liu | Graph Neural Networks | Research Excellence Award

China University of Geosciences | China

Mr. Chao Liu is an active researcher in computer science with a strong focus on knowledge graphs, natural language processing, and big data analysis, particularly in the context of graph neural networks (GNNs). He has authored 33 scholarly documents that have attracted 492 citations across 488 citing publications, reflecting a high level of visibility and influence within the research community. With an h-index of 11, his work demonstrates consistent academic impact, especially in large-scale graph learning and data-driven intelligence. His recent contributions explore fundamental challenges in sampling-based large-scale GNNs, examining the relative roles of sampling strategies versus iterative computation for improving scalability and performance. Notably, his 2026 journal article in Neurocomputing, titled “Which plays a key role in sampling-based large-scale GNNs, sampling or iteration?”, provides important theoretical and empirical insights into efficient GNN design. Overall, his research advances scalable machine learning methods for complex, data-intensive systems.

Citation Metrics (Scopus)

600
450
300
0

Citations
492

Documents
33

h-index
11


View Scopus Profile

Featured Publications

Changda Lei | Artificial intelligence | Best Academic Researcher Award

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Dr. Changda Lei | Artificial intelligence | Best Academic Researcher Award

Resident physician at First Affiliated Hospital of Soochow University, China

Professional Profile

Scopus
Orcid

Summary

Dr. Changda Lei is a medical doctor and early-career researcher specializing in gastrointestinal tumors, digestive endoscopy, and artificial intelligence (AI) in clinical diagnostics. He is currently affiliated with the Department of Gastroenterology at the First Affiliated Hospital of Soochow University, China. His interdisciplinary expertise bridges medical imaging, clinical gastroenterology, and AI-driven diagnostic systems, with a particular focus on enhancing early cancer detection.

Educational Details

Dr. Lei earned his Doctor of Medicine degree with a residency in Gastroenterology from the First Affiliated Hospital of Soochow University, China. His doctoral thesis, titled “Artificial intelligence for early gastric cancer boundary recognition in NBI and NF-NBI endoscopic images,” was supervised by Prof. Rui Li and represents a significant contribution to AI applications in endoscopic image analysis for early cancer detection.

Professional Experience

During his residency and doctoral training at Soochow University, Dr. Lei gained clinical and research experience in gastroenterological procedures and endoscopic imaging. He has collaborated closely with multidisciplinary teams, including radiologists, computer scientists, and oncologists, to develop and validate AI-assisted diagnostic tools. His co-authored works involve both methodological development and clinical validation, marking him as a key contributor to translational medicine in the field of digestive oncology.

Research Interests

Dr. Lei’s research interests lie at the intersection of gastrointestinal tumor diagnostics, digestive endoscopy, and artificial intelligence. He focuses on improving early detection of gastric cancer, particularly through boundary recognition in NBI and NF-NBI endoscopic images. His broader research also explores multi-task learning, semantic segmentation, and clinical integration of AI tools for real-time diagnostic support in endoscopy suites.

Author Metrics

Although early in his academic career, Dr. Lei has co-authored multiple peer-reviewed articles in reputable international journals. His publications in Annals of Medicine, Scandinavian Journal of Gastroenterology, and Expert Systems with Applications demonstrate a growing influence in both clinical and AI research communities. His ORCID ID is 0000-0001-7908-7011, and his citation metrics are expected to rise with his growing publication footprint in multidisciplinary fields.

Awards and Honors

While formal awards are not explicitly listed, Dr. Lei’s contributions to high-impact publications and participation in cutting-edge research—such as the application of task-specific prompting in AI models for endoscopy—indicate peer recognition and significant academic promise. His collaborative work with senior scientists like Prof. Rui Li and publication in top-tier journals positions him as a rising expert in the medical AI and gastroenterology research community.

Publication Top Notes

1. Artificial Intelligence-Assisted Diagnosis of Early Gastric Cancer: Present Practice and Future Prospects

  • Authors: Changda Lei, Wenqiang Sun, Kun Wang, Ruirong Weng, Xiuji Kan, Rui Li

  • Journal: Annals of Medicine

  • Volume/Issue: Volume 57, Issue 1

  • Article ID: 2461679

  • Publication Date: 2025

  • DOI: 10.1080/07853890.2025.2461679

  • Summary:
    This article reviews current applications and future directions for artificial intelligence (AI) in the diagnosis of early gastric cancer (EGC). It highlights advances in endoscopic imaging, especially NBI (Narrow Band Imaging) and AI-based pattern recognition, and discusses clinical integration, challenges, and prospects for real-time implementation.

2. Neonatal Lupus Erythematosus: An Acquired Autoimmune Disease to Be Taken Seriously

  • Authors: Wenqiang Sun, Changchang Fu, Xinyun Jin, Changda Lei, Xueping Zhu

  • Journal: Annals of Medicine

  • Publication Date: December 31, 2025

  • DOI: 10.1080/07853890.2025.2476049

  • Summary:
    This clinical review focuses on neonatal lupus erythematosus (NLE), a rare but significant autoimmune condition affecting newborns. The article emphasizes early diagnosis, maternal screening, and therapeutic strategies, highlighting the need for interdisciplinary vigilance and patient-specific care.

Conclusion

Dr. Changda Lei is an exceptionally promising early-career academic who has already made meaningful contributions to the convergence of AI and gastrointestinal oncology. His research is not only innovative and clinically relevant but also indicative of leadership in next-generation diagnostic solutions.

Ahmad Hassanat | Machine Learning | Best Researcher Award

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Prof. Ahmad Hassanat | Machine Learning | Best Researcher Award

Professor at Mutah University, Jordan

Professional Profile

Scopus
Orcid
Google Scholar

Summary

Prof. Ahmad B. A. Hassanat is a Full Professor of Computer Science at Mutah University, Jordan, and a senior IEEE member. He is globally recognized for his extensive contributions to artificial intelligence, machine learning, biometrics, and image processing. With over two decades of academic and research experience, he has authored numerous impactful papers and books and is widely known for pioneering innovative techniques like the "Hassanat Distance" metric and deep learning-based biometric systems. He is also active in international collaborations, editorial work, and AI-driven healthcare research.

Educational Details

Prof. Hassanat earned his Ph.D. in Computer Science from the University of Buckingham, UK,, with a focus on automatic lip-reading. He holds an M.Sc. in Computer Science from Al al-Bayt University, Jordan, where he specialized in fast string matching algorithms. He completed his B.Sc. in Computer Science at Mutah University, Jordan. His academic foundation reflects a strong blend of theoretical depth and applied research skills in computing and AI.

Professional Experience

Prof. Hassanat has served in multiple academic roles across Jordan and Saudi Arabia, including as a Full Professor at Mutah University and the University of Tabuk. He was Head of the IT Department at Mutah University and a visiting researcher at the Sarajevo School of Science and Technology. Earlier in his career, he worked for the Jordanian Armed Forces as a programmer and systems analyst, where he developed over a dozen mission-critical ICT systems. He is also a founder or co-founder of academic programs, conferences, and novel biometric solutions.

Research Interests

His research spans machine learning, artificial intelligence, image processing, biometrics, pattern recognition, and evolutionary algorithms. He is known for practical innovations such as deep learning for veiled-face recognition, genetic algorithm optimization, voice-based Parkinson’s detection, and machine learning models for epidemiology, security, and finance. He also created the widely referenced Hassanat Distance, improving classifier performance in imbalanced data scenarios.

Author Metrics

Prof. Hassanat has published over 100 journal articles and conference papers, with an H-index of 33, i10-index of 56, and more than 4,000 citations. His work is featured in top journals such as IEEE Access, PLOS ONE, Sustainability, Applied Sciences, and Computers. His algorithmic contributions and models are highly cited in the fields of AI, healthcare informatics, and big data analytics.

Awards and Honors

Prof. Hassanat has been named among the world’s top 2% scientists by Stanford–Elsevier in 2021, 2022, and 2023. He has received the Best Scientist award at Mutah University for 2023 and 2024, and multiple competitive research grants from Jordan and Saudi Arabia. He was the recipient of Mutah University’s Distinguished Researcher Award (2018, 2019), and granted IEEE Senior Membership for his research excellence. His innovations, including terrorist identification from hand gestures and COVID-19 forecasting tools, have received global media attention.

Publication Top Notes

1. Deep learning computer vision system for estimating sheep age using teeth images

  • Authors: AB Hassanat, MA Al-Sarayreh, AS Tarawneh, MA Abbadi, et al.

  • Journal: Connection Science

  • Volume/Issue: 37 (1)

  • Article ID: 2506456

  • Year: 2025

  • Summary:
    This study presents a deep learning-based computer vision system designed to estimate the age of sheep by analyzing images of their teeth. The model likely leverages convolutional neural networks (CNNs) or similar architectures to accurately assess age-related dental features, offering a non-invasive and automated method for livestock age estimation that can assist farmers and veterinarians.

  • Citations: Not provided

  • Access: Details not provided

2. ICT: Iterative Clustering with Training: Preliminary Results

  • Authors: AB Hassanat, AS Tarawneh, AS Alhasanat, M Alghamdi, K Almohammadi, et al.

  • Conference: 2025 International Conference on New Trends in Computing Sciences (ICTCS)

  • Year: 2025

  • Summary:
    This paper introduces a novel method named Iterative Clustering with Training (ICT), presumably a machine learning or data clustering approach. Preliminary results demonstrate its effectiveness in improving clustering accuracy or training efficiency for datasets common in computing science. The approach likely combines clustering with supervised training iterations for better performance.

3. Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing

  • Authors: E Al-Khlifeh, AS Tarawneh, K Almohammadi, M Alrashidi, R Hassanat, et al.

  • Journal: Parasites & Vectors

  • Volume/Issue: 18 (1)

  • Article Number: 33

  • Year: 2025

  • Summary:
    This research applies decision tree-based machine learning techniques to mine laboratory data for efficient and accurate diagnosis of amebiasis. The study demonstrates how data mining on clinical data combined with decision trees can improve testing accuracy and streamline diagnostic procedures in parasitology.

4. Non-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach

  • Authors: AS Tarawneh, AK Al Omari, EM Al-Khlifeh, FS Tarawneh, M Alghamdi, et al.

  • Book/Series: Advances and Applications in Bioinformatics and Chemistry

  • Pages: 159-178

  • Year: 2024

  • Summary:
    This paper proposes a non-invasive method for cancer detection by combining blood test results with predictive modeling approaches, likely using machine learning algorithms. The approach aims to provide an early, cost-effective screening tool for cancer by analyzing biomarkers and patterns in blood test data.

5. Extended spectrum beta-lactamase bacteria and multidrug resistance in Jordan are predicted using a new machine-learning system

  • Authors: EM Al-Khlifeh, IS Alkhazi, MA Alrowaily, M Alghamdi, M Alrashidi, et al.

  • Journal: Infection and Drug Resistance

  • Pages: 3225-3240

  • Year: 2024

  • Summary:
    This study develops and applies a machine learning system to predict the occurrence of extended spectrum beta-lactamase (ESBL) producing bacteria and multidrug resistance patterns in Jordan. The predictive model aids in understanding and managing antibiotic resistance, supporting healthcare decision-making and antimicrobial stewardship.

Conclusion

Prof. Ahmad Hassanat embodies the qualities of a world-class researcher—his work is innovative, deeply applied, and globally relevant. From introducing original metrics and models in AI to developing life-saving diagnostic systems and biometric security applications, his impact is both academic and practical.

His dedication to research excellence, mentorship, and cross-disciplinary innovation makes him highly deserving of the Best Researcher Award in Machine Learning.

Xin Liu | Deep Learning | Best Researcher Award

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Dr. Xin Liu | Deep Learning | Best Researcher Award

Associate Professor at Wenzhou Business College, China📖

Dr. Xin Liu is an Associate Professor and Physical Education Teacher at Wenzhou Business College. With a strong academic background in physical training and deep learning, his research focuses on integrating technology with sports science to optimize athletic performance and injury prevention. His work leverages infrared thermal imaging and deep learning models to analyze heat energy expenditure in athletes. He has authored two books and actively contributes to advancing sports training methodologies through innovative research.

Profile

Orcid Profile

Education Background🎓

  • Ph.D. in Physical Education, Jose Rizal University, 2020–2023
  • Master’s in Physical Education, Shanghai Normal University, 2017–2019
  • Bachelor’s in Physical Education, Shandong Agricultural University, 2013–2017

Professional Experience🌱

  • Physical Education Teacher, Wenzhou Business College (2024–Present)
    Engaged in teaching and research on physical training methodologies, integrating AI-driven analytics in sports science.
  • Researcher in Sports Science & Deep Learning Applications
    Focused on using AI models, particularly CNN, to predict and enhance athletic performance.
Research Interests🔬
  • Physical Training & Sports Performance Optimization
  • Application of Deep Learning in Sports Science
  • Infrared Thermal Imaging for Athlete Monitoring

Author Metrics

Dr. Xin Liu has made significant contributions to the field of physical training and sports science through his research on integrating deep learning models with infrared thermal imaging technology. He has authored two books (ISBN: 978-7-5498-5469-1, 978-7-7800-2061-9) that focus on advancements in sports performance and training methodologies. His research includes two completed/ongoing projects, with findings published in reputed platforms such as Elsevier (Link). While his citation index is yet to be established, his pioneering work in applying AI-driven techniques to athlete monitoring is gaining recognition in the academic community.

Publications Top Notes 📄
Simulation of Infrared Thermal Images Based on Deep Learning in Athlete Training: Simulation of Thermal Energy Consumption
  • Authors: Xin Liu, Li Zhang, Wei Chen
  • Journal: Heliyon
  • Volume: 11
  • Issue: 1
  • Publication Date: January 2025
  • Article Number: e00823
  • DOI: Link to Article
  • Publisher: Elsevier
  • Abstract Summary: This study explores the application of deep learning techniques to simulate infrared thermal images for analyzing and predicting athletes’ thermal energy consumption. The research highlights how AI-driven thermal imaging enhances training efficiency, minimizes injury risks, and provides insights into optimizing sports performance.

Conclusion

Dr. Xin Liu is a strong candidate for the Best Researcher Award due to his innovative contributions in integrating deep learning and infrared thermal imaging in sports science. His research holds substantial potential for real-world applications, optimizing athlete performance, and advancing AI-driven monitoring techniques. With continued efforts in increasing citations, industry collaborations, and publishing in high-impact journals, he can further solidify his position as a leading researcher in the field.

Eman Abdullah Aldakheel – Deep learning- Academic Achievement Award

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Eman Abdullah Aldakheel – Deep learning- Academic Achievement Award

🌐 Professional Profile

Educations📚📚📚

She earned her Doctor of Philosophy in Computer Science from the University of Illinois at Chicago in Fall 2019, with her dissertation titled “Deadlock Detector and Solver (DDS).” She completed her Master of Science in Computer Science at Bowling Green State University in Fall 2011, with her thesis titled “A Cloud Computing Framework for Computer Science Education.” Her academic journey began with a Bachelor of Science in Computer Science from Imam Abdulrahman bin Faisal University (formerly Dammam University) in Fall 2006, where she graduated with honors.

In her academic career, she began as an Instructor at New Horizons Institute in Khobar, KSA, during Summer 2007, where she trained students at various levels on ICDL and IC3 certificates and taught courses in Computer Mathematics, Secretary duties, office management, and office technology. She then taught basic computer skills and Microsoft Office applications at Dammam University (now Imam Abdulrahman bin Faisal University) in Fall 2007. Prior to this, she worked as a Teacher at Riyadh Al-Islam Schools in Spring 2007, where she taught basic computer skills to girls, ranging from elementary to high school students.

Since Fall 2012, she has been serving as a faculty member at Princess Nourah Bint Abdulrahman University in Riyadh, KSA

Work experience

As a Lecturer and Assistant Professor, she teaches a range of courses including Foundations of Programming (GN 044), Discrete Structures (CS100), Programming Language I (CS110), Programming Language II (CS111), Computer Organization (CS206), Natural Language Processing (CAI 350), Graduation Project I (CS487), and Graduation Project II (CS488). She is involved in designing and recording a programming basics course and a data structures course as electronic courses for the programming diploma program. She participates in faculty committees and collaborative initiatives to improve the curriculum and attends seminars to stay updated on the latest trends in technology and teaching methods. She also serves as a scientific contact at the University of Southern California in the field of video game design and is the Computer Sciences’ program leader.

In her non-academic experience, she served as Vice President, Director of Public Relations, and Director of the Cultural and Information Committee at King Abdulaziz and his Companions Foundation for the Gifted from Summers 2002 to 2007. During her tenure, she built a summer science program for talented students, encouraged their inventiveness, and gained significant managerial skills through her six years of work with the President of the program.

Certifications or Professional Registrations:

She holds several notable certifications and professional registrations, including membership in the Golden Key International Honor Society and the Phi Kappa Phi Honor Society. She also possesses the Huawei HCIA-AI Certificate. Her current professional memberships include the Computing Research Association, the Association for Computing Machinery (ACM), and the IEEE Computer Society.

 

Honors and Awards:

She has received several honors and awards, including participation in the CRA-Women Grad Cohort Workshop, and has been recognized with the ACM’s SRC Travel Award and the HPDC Travel Award. Her service activities encompass planning programs and activities for talented students, building and designing electronic courses, and supervising the student magazine for the College of Computer and Information. She is also involved in various committees, including judging and supervising hackathons.

In terms of granted projects, she is currently working on the Researchers Supporting Project at Princess Nourah bint Abdulrahman University (Project number: PNURSP2023R409) for the year 2023. She is also leading two projects funded by the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia: “Detection and Identification of Plant Leaf Diseases using YOLOv4” (Project number RI-44-0618) from November 2022 to May 2024, and “Use of Modern Machine Learning Techniques to Combat Extremism and the Role of Women” (Project number WE-44-0279) from November 2022 to May 2024.

📝🔬Publications📝🔬

Om Prakash – Computer Vision – Best Researcher Award

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Om Prakash – Computer Vision – Best Researcher Award

Dr. Om Prakash  distinguished academic and researcher in the field Computer Vision.

🌐 Professional Profile

Educations📚📚📚

He earned his Doctor of Philosophy from the University of Allahabad, Allahabad, U.P., India, in November 2014. He has extensive experience in academia and industry. Since March 2020, he has been an Assistant Professor at Academic Pay Level-10 in the Department of Computer Science and Engineering, School of Engineering and Technology, HNB Garhwal University, Srinagar Garhwal, Uttarakhand, India. From May 2019 to February 2020, he worked as a Computer Vision Scientist at Inferigence Quotient LLP, Bengaluru, Karnataka, India. Prior to this, he served as an Assistant Professor in the Department of Computer Science and Engineering at NIRMA University, Ahmedabad, Gujarat, India, from May 2018 to April 2019. Between March 2016 and April 2018, he was a faculty member at the Centre of Computer Education, University of Allahabad, U.P., India. He also completed a Postdoctoral Fellowship at the Gwangju Institute of Science and Technology, South Korea, from March 2015 to February 2016. His earlier experience includes serving as a faculty member at the Centre of Computer Education, University of Allahabad, U.P., India, from August 2007 to February 2

Research Interests

• Computer Vision
• Image and Video Processing
• Wavelet transforms
• Multisensory data fusion
• Video surveillance
• Machine Learning/Deep Learning
• Thermography
• Medical Imaging

Book Edited as Guest Editor

AKS Kushwaha, Om Pakash, M. Khare, J. Gwak, N.T. Binh, “Visual and Sensory Data Processing
for Real Time Intelligent Surveillance System”, Multimed Tools Appl, vol.81, pp. 42097–42098
(2022). https://doi.org/10.1007/s11042-022-14263-3, Springer

 

📝🔬Publications📝🔬

1. Pratibha Maurya, Arati Kushwaha, Ashish Khare, Om Prakash, Balancing Accuracy and
Efficiency: A Lightweight Deep Learning Model for Covid-19 Detection” Journal
Engineering Applications of Artificial Intelligence. vol. 136, Part B, July 2024, 108999,
ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2024.108999., Elsevier. (SCI).
2. Arati Kushwaha, Ashish Khare, Om Prakash, Human activity recognition algorithm in video
sequences based on the fusion of multiple features for realistic and multi-view
environment. Multimedia Tools and Applications. vol.83, pp. 22727-22748, August 2024,
Springer (SCI)
(https://doi.org/10.1007/s11042-023-16364-z)
3. Neha Sisodiya, Nitant Dube, Om Prakash, Priyank Thakkar, Scalable Big Earth
Observation Data Mining Algorithms: A Review. Earth Science and Informatics, vol.16,
pp. 1993–2016, June 2023, Springer (SCI). https://doi.org/10.1007/s12145-023-01032-5.
4. Ashish Khare, Arati Kushwaha and Om Prakash, Human Activity Recognition in a Realistic
Unconstrained and Multiview Environment using 2D-CNN. Journal of Artificial
Intelligence and Technology (JAIT). vol.3, pp. 100-107, May 2023, Intelligence Science
and Technology Press. (ISTP) (Scopus). (https://doi.org/10.37965/jait.2023.0163)
5. Arati Kushwaha, Ashish Khare, Om Prakash, “Micro-network-based deep convolutional
neural network for human activity recognition from realistic and multi-view visual
data”, Neural Comput & Applic (2023). vol.35, pp.13321–13341, Springer (SCI).
(https://doi.org/10.1007/s00521-023-08440-0)
6. Arati Kushwaha, Ashish Khare, Om Prakash and Manish Khare, “Dense optical flow
based background subtraction technique for object segmentation in moving camera
environment”, IET Image Processing, vol. 14, no. 14, pp. 3393-3404, December 2020, IET
Publication. (SCI)
(https://doi.org/10.1049/iet-ipr.2019.0960).
7. Mounika B. Reddy, Om Prakash, Ashish Khare, “Keyframe extraction using Pearson correlation
coefficient and color moments,”. Multimedia Systems, vol. 26, pp.267–299 (2020), Springer. (SCI)
(https://doi.org/10.1007/s00530-019-00642-8).

8. Mounika B. Reddy, Om Prakash, Ashish Khare, “Video Superpixels Generation through
Integration of Curvelet transform and Simple Linear Iterative Clustering”, Multimedia Tools and
Applications, vol. 78, pp. 25185–25219, March 2019, Springer. (SCI)
(https://doi.org/10.1007/s11042-019-7554-z).

9. Om Prakash, Chang Min Park, Ashish Khare, Moongu Jeon, Jeonghwan Gwak, “Multiscale
Fusion of Multimodal Medical Images using Lifting Scheme based Biorthogonal Wavelet
Transform,” Optik, vol. 182, pp.995-1014, April 2019, Elsevier (SCI)
(https://doi.org/10.1016/j.ijleo.2018.12.028)
10. Manish Khare, Om Prakash and Rajneesh Kumar Srivastava, “Combining Zernike moment and
Complex wavelet transform for Human object classification,” Int. J. Computational Vision and
Robotics, May 2018, vol.18, no.2, pp.140-167, Inderscience Publishers. (Scopus)
(https://doi.org/10.1504/IJCVR.2018.091983)
11. Om Prakash, Jeonghwan Gwak, Manish Khare, Ashish Khare, Moongu Jeon, “Human detection in
complex real scenes based on combination of biorthogonal wavelet transform and Zernike
moments,” Optik, vol. 157, pp. 1267-1281, March 2018, Elsevier. (SCI)
(https://doi.org/10.1016/j.ijleo.2017.12.061)
12. Richa Srivastava, Om Prakash and Ashish Khare, “Local Energy based Multimodal Medical
Image Fusion in Curvelet Domain,” IET Computer Vision, vol.10, issue 6, pp. 513-527, 2016. IET
digital library.(SCI)
(https://doi.org/10.1049/iet-cvi.2015.0251)
13. Om Prakash and Ashish Khare, “Tracking of moving object using energy of Biorthogonal wavelet
transform,” Chiang Mai Journal of Science, vol.42, no.3, pp. 783-795, July 2015, Chiang Mai
University. (SCI)
(https://thaiscience.info/Journals/Article/CMJS/10972713.pdf)
14. Om Prakash and Ashish Khare, “Medical Image Denoising based on Soft thresholding using
Biorthogonal Multiscale Wavelet Transform,” International Journal of Image and Graphics, vol.
14, no. 1 & 2, pp. 1450002 (30 pages), March 2014, World Scientific. (SCI)
(https://doi.org/10.1142/S0219467814500028)
15. Alok Kumar Singh Kushwaha, Chandra Mani Sharma, Manish Khare, Om Prakash and Ashish
Khare, “Adaptive real-time motion segmentation technique based on statistical background model,”
The Imaging Science Journal, vol. 62, no.5, pp. 285-302, 2014, Royal Photographic Society.(SCI)
(https://doi.org/10.1179/1743131X13Y.0000000056)
16. Rajiv Singh, Richa Srivastava, Om Prakash and Ashish Khare, “Multimodal medical image fusion
in Dual tree complex wavelet domain using maximum and average fusion rules,” Journal of
Medical Imaging and Health Informatics, vol. 2, no. 2, pp. 168-173, June 2012, American
Scientific Publishers. (SCI)
(https://doi.org/10.1166/jmihi.2012.1080)

Publications in International Conference Proceedings

  • B. Reddy Mounika, Om Prakash, Ashish Khare, “Key Frame Extraction using Uniform Local Binary Pattern,” 2018 Second International Conference on Advances in Computing, Control and Communication Technology (IAC3T), University of Allahabad, Allahabad, 21-23 Sept 2018. IEEE
  • Abhishek Srivastava, Pronaya Bhattacharya, Arunendra Singh, Atul Mathur, Om Prakash, Rajeshkumar Pradhan, “A Distributed Credit Transfer Educational Framework based on Blockchain,” 2018 Second International Conference on Advances in Computing, Control and Communication Technology (IAC3T), University of Allahabad, Allahabad, 21-23 Sept 2018. IEEE
  • Om Prakash, Alok Kumar Singh Kushwaha, Moongu Jeon, “An approach towards Object Tracking based on Rotation Invariant Moments of Complex Wavelet Transform,” in International Conference on Advances in Computing, Control and Communication Technology (IAC3T-2016), University of Allahabad, Allahabad, 25-27 March 2016.
  • Arati Kushwaha, Ashish Khare, Om Prakash, Jong-In Song, Moongu Jeon, “3D Medical Image Fusion using the Dual Tree Complex Wavelet Transform,” in IEEE 4th International Conference on Control, Automation and Information Sciences (ICCAIS-2015), Changshu, China, 29-31 October 2015. IEEE
  • Manish Khare, Om Prakash, Rajneesh Kumar Srivastava, Ashish Khare, “Contourlet Transform based Human Object Tracking,” Proceedings of 27th SPIE Electronic Imaging, Vol. 9410 (Visual Information Processing and Communication VI), 08-12 February 2015, San Francisco, USA. SPIE
  • Om Prakash, Manish Khare, Ashish Khare, “Biorthogonal Wavelet Transform Based Classification of Human Object Using Adaboost Classifier,” Proceedings of IEEE 3rd International Conference on Control, Automation and Information Sciences (ICCAIS-2014), Gwangju Institute of Science and Technology (GIST), South Korea, pp. 194-199, 02-05 December 2014. IEEE
  • Manish Khare, Om Prakash, Rajneesh Kumar Srivastava, Ashish Khare, “Daubechies Complex Wavelet Transform Based Approach for Multiclass Object Classification,” Proceedings of IEEE 3rd International Conference on Control, Automation and Information Sciences (ICCAIS-2014), Gwangju Institute of Science and Technology (GIST), South Korea, 206-211, 02-05 December 2014. IEEE
  • Prashant Srivastava, Om Prakash, Ashish Khare, “Content-Based Image Retrieval Using Moments of Wavelet Transform,” Proceedings of IEEE 3rd International Conference on Control, Automation and Information Sciences (ICCAIS-2014), Gwangju Institute of Science and Technology (GIST), South Korea, pp. 159-164, 02-05 December 2014. IEEE
  • Om Prakash, Arvind Kumar, Ashish Khare, “Pixel-level Image Fusion Scheme based on Steerable Pyramid Wavelet Transform using absolute Maximum fusion rule,” Proceedings of IEEE International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT-2014), Ghaziabad, India, pp. 770-775, 07-08 February 2014. IEEE

Gan Xu – Artificial Intelligence – Best Researcher Award

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Gan Xu – Artificial Intelligence – Best Researcher Award

Mr. Gan Xu distinguished academic and researcher in the field Artificial Intelligence.

🌐 Professional Profile

Educations📚📚📚

He is currently pursuing a Ph.D. in Finance at the Capital University of Economics and Business in Beijing, China, since September 2021. Prior to this, he completed his Master’s in Finance from Beijing Union University, Beijing, China, graduating in June 2021. His academic journey began with a Bachelor’s degree in Biotechnology, which he obtained from Guilin Medical University, Guilin, Guangxi, China, in June 2010.

Research Experience

He participated in the Project of the National Social Science Foundation of China, focusing on the “Research on Level Measurement, Spatial and Temporal Divergence, and Improvement Path of Rural Financial Services for Rural Revitalization” (19BJY158), where he was mainly responsible for the research design of some sub-topics and participated in enterprise research. Additionally, he contributed to the Key Topic of the China Mobile Communication Federation on the “Research on the Application of Blockchain Technology in Finance” (CMCA2018ZD01), taking charge of the research design of certain sub-topics and writing research reports. Furthermore, he was involved in the research project on “Financial Support for Deepening Financial Services for Private and Micro and Small Enterprises” as part of the Comprehensive Reform Pilot City Project in Jincheng City, Shanxi Province, where he was responsible for independently participating in application writing.

Social Experience

He has co-authored several significant publications, including “Financial Density of Village Banks and Income Growth of Rural Residents” with Yang, G.Z., Xu, G., Zhang, Y., and others, published in Economic Issues in 2021. Additionally, he contributed to “Knowledge Mapping Analysis of Seven Decades of Rural Finance Research in China” with Zhang, F., Xu, G., Zhang, X.Y., and Cheng, X., which appeared in Rural Finance Research in 2020. He also co-authored “A Review of Blockchain Applications in the Financial Sector” with Zhang, F. and Cheng, X., published in Technology for Development in 2019.

Honors

  • Received Beijing Outstanding Graduates in 2020
  • Outstanding graduate of Beijing Union University in 2020
  • First Prize of Excellent Paper in the First Annual Meeting of the Financial Technology Professional Committee of the China Society for Technology Economics, 2019
  • Second Prize of Excellent Paper in the 13th China Rural Finance Development Forum, 2019
  • Second Prize of Excellent Paper of the 9th Annual Conference of China Regional Finance and Xiongnu Financial Technology Forum, 2019

📝🔬Publications📝🔬

Nagesh Dewangan – Interpretation Analysis of Deep Learning Models-Best Researcher Award

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Nagesh Dewangan – Interpretation Analysis of Deep Learning Models-Best Researcher Award

Mr. Nagesh Dewangan distinguished academic and researcher in the field Interpretation Analysis of Deep Learning Models. As a dedicated researcher in the field of machinery condition monitoring, his work has focused on advancing knowledge in activity monitoring and fault diagnosis for heavy machinery using deep learning models. His research has led to several key advancements, particularly in the areas of machinery activity recognition and motor fault diagnosis. He has worked on projects focusing on the cycle time of dumper activities and real-time fault diagnosis of motors using acceleration signals. The studies were conducted in both laboratory and real environments, providing comprehensive data for robust analyses. His work has resulted in the development of innovative methodologies and technologies. He has contributed to the creation of new algorithms for activity recognition using convolutional neural networks (CNNs) and developed approaches to enhance the generalizability of models across different environments. Collaborations with institutions like CSIR-Central Institute of Mining and Fuel Research Dhanbad, India, and industry partners like Coal India Limited, India, have enriched his research.

 

🌐 Professional Profile

Educations📚📚📚

He is currently a Ph.D. research scholar in the Acoustics and Condition Monitoring Laboratory, Mechanical Engineering Department, Indian Institute of Technology Kharagpur, India. He received his B.E. degree in Mechanical Engineering from the Bhilai Institute of Technology Durg, India, in 2016, and his M.Tech. degree in Maintenance Engineering & Tribology from the Indian Institute of Technology Dhanbad, India, in 2019. His research interests are in the areas of Mining Machinery, Condition Monitoring, Signal Processing, Fault Diagnosis, Real-time Application, Internet of Things, Machine Learning, and Deep Learning for industry-oriented Product Design and Development. Throughout his academic career, he has been involved in numerous research projects focused on improving machinery efficiency and safety, particularly in the mining industry. His recent work includes analyzing the cycle time of dump truck activities, fuel consumption, and implementing Convolutional Neural Networks for activity recognition.

Experience

He has published a paper in the reputed journal Automation in Construction, where he critically evaluates existing methods and proposes innovative solutions. Additionally, he has co-authored two papers in reputed journals, such as Engineering Transactions and the International Journal of Chemical Engineering. He has also presented his work at various prestigious conferences, such as the International Conference on Mechanical Power Transmission 2019 (IIT Madras), 17th International Conference on Vibration Engineering and Technology of Machinery 2022 (Institute of Engineering, Nepal), National Conference on Condition Monitoring 2023 (NSTL, Visakhapatnam), and World Congress on Engineering Asset Management (RMIT University, Vietnam), sharing his findings and insights with the academic and professional community. For his research work, he predominantly uses MATLAB, Python, LabVIEW, and NI Multisim.

 

📝🔬Publications📝🔬

Liwei Deng- Autonomous Driving- Best Researcher Award

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Liwei Deng- Autonomous Driving-Best Researcher Award

Prof. Liwei Deng distinguished academic and researcher in the field Autonomous Driving. 

🌐 Professional Profile

Educations📚📚📚

He holds a Ph.D. in Control Science and Engineering from the Harbin Institute of Technology, Harbin, China, which he earned in 2014. His dissertation focused on “Control Theory and Its Applications of Fractional Order Sliding Mode” under the supervision of Professor Shenmin Song. Prior to this, he completed a Master of Science in Control Theory and Control Engineering at Harbin University of Science and Technology in 2010, where his thesis was titled “Pressure Data Acquisition and Control System Based on ARM,” supervised by Professor Junshan Gao. He began his academic journey with a Bachelor of Engineering in Automation from Jiamusi University, Jiamusi, China, graduating in 2007.

 Experience:

He began his academic career as a Lecturer in the School of Automation at Harbin University of Science and Technology, serving from October 2014 to August 2018. Following this, he was promoted to Associate Professor within the same school, a role he held from August 2018 to August 2023. In September 2023, he was further promoted to the position of Professor at the School of Automation. Additionally, since May 2023, he has been serving as a Doctoral Supervisor in the School of Computer Science and Technology at Harbin University of Science and Technology.

Projects 

He has been actively involved in various research projects over the years. From 2021 to 2024, he led the Key R&D Program Guidance Projects of Heilongjiang Province (Grant No. GZ20210065), focusing on the development of intelligent winding equipment for hydrogen cylinders based on robots. As the Principal Investigator for the National Science Foundation for Young Scientists of China (Grant No. 61806060) from 2019 to 2021, he researched diabetic retinal image analysis algorithms using fractional order differential and deep learning. During the same period, he also served as Principal Investigator for a project funded by the Natural Science Foundation of Heilongjiang Province, China, which focused on the key technology of defect detection of wind turbine blades based on images taken by unmanned aerial vehicles. From 2017 to 2020, he was the Principal Investigator for the Harbin Program of Science and Technology (Grant No. 2017RAQXJ006), which aimed to develop key technologies for the automatic production line for rubber bridge bearing members. Additionally, he played significant roles in several other projects, including being a Principal Participant in the National Natural Science Foundation of China project (Grant No. 61174037) on distributed motion autonomous coordination control of on-orbit multi-spacecraft expectation mode (2011-2015), a Participant in the State Key Program of National Natural Science of China (Grant No. 61333003) on coordination control technology for networked satellite clusters (2012-2016), and a Principal Participant in a project funded by the Natural Science Foundation of Heilongjiang Province, China (Grant No. F030101) on the dynamic morphology of hyperchaotic attractors (2013-2016).

📝🔬Publications📝🔬

    • Deng, L., Zou, Y., Yang, X. et al. L2NLF: a novel linear-to-nonlinear framework for multi-modal medical image registration[J]. Biomed. Eng. Lett.(2024).（WOS:001139763600001）
    • Deng, L., Lan, Q., Zhi, Q. et al. Deep learning-based 3D brain multimodal medical image registration[J]. Med Biol EngComput .2024,62: 505–（WOS:001098686900001）
    • Biao Zhang, Sheng Wang, Liwei Deng, et al.Ship motion attitude prediction model based on IWOA-TCN-Attention[J].Ocean Engineering,2023,272:113911
    • Liwei Deng, Henan Sun, Jing Wang, et al.A Novel Unsupervised MRI Synthetic CT Image Generation Framework with Registration Network[J].CMC-Computers, Materials & Continua,2023,77(2):2271–（WOS:0011269599000 24）
    • Liwei Deng, Zhen Liu,Jiandong Wang et al.ATT-YOLOv5-Ghost: Water surface object detection in complex scenes[J].Journal of Real-Time Image Processing,2023,20:97.（WOS:001050358900001）
    • LiweiDeng,ShanshanLiu,WeiShi,et al.Defect Detection and Classification of Offshore Wind Turbine Rotor Blades[J].Nondestructive Testing and Evaluation,2023.（WOS:001025791800001）
    • Yuheng Yin, Yangang Guo, Liwei Deng, et al.Improved Pspnet-Based Water Shoreline Detection in Complex Inland River Scenarios[J]. Complex & Intelligent Systems, 2022,9(1): 233-245.（WOS:000816995100001）
    • Liwei Deng, Yue Wang, Qi Lan,etal.Remote sensing image building change detection based on efficient-UNet++[J].Journal of Applied Remote Sensing,2023,17(3):034501.（WOS:00177860300006）
    • Liwei Deng, He Cao,QingboDong,etal.Semi-Supervised Lane Detection for Continuous Traffic Scenes[J].Traffic Injury Prevention.2023,24(6):452-457.（WOS:001008469300001）
    • Liwei Deng, Yanchao Zou, SijuanHuang,et al. Deformable 3D Medical Image Registration with ConvolutionalNeural Network and Transformer[J].Journal of Instrumentation.2023,(18):P04029（WOS:000986658100 001）
    • Rui Zhang, Cong Xie, Liwei Deng. A Fine-Grained Object Detection Model for Aerial Images Based on Yolov5 Deep Neural Network[J]. Chinese Journal of Electronics, 2023, 32(1): 51-63.（WOS:000932261100001）
    • Liwei Deng, Yufei Ji, Sijuan Huang, et al.Synthetic CT generation from CBCT using Double-Chain-CycleGAN[J].Computers in Biology and Medicine.2023,106889.（WOS:001006917800001）
    • Liwei Deng, JiandongWang, Zhen Liu. Cascaded Network Based on EfficientNet and Transformer for Deepfake Video Detection[J].Neural Processing Letters.2023.（WOS:000961765200002）
    • Liwei Deng, QiangZhi, Sijuan Huang, et al. A Deformable Patch-based Transformer for 3D Medical Image Registration[J].International Journal of Computer Assisted Radiology and Surgery.2023.（WOS:000990481800001）
    • Liwei Deng, Yuanzhi Zhang, Xin Yang, et al. Meta-learning Multi-scale Radiology Medical Image Superresolution[J].CMC-Computers, Materials & Continua.2023,75(2) :2671-2684.（WOS:000980836000018）
    • Liwei Deng, Yuanzhi Zhang, Jing Wang, et al. Improving Performance of Medical Image Alignment through Super-resolution[J].Biomedical Engineering Letters.2023.（WOS:000934806000001）
    • LiweiDeng ,Mingxing Zhang , JingWang, et al.Improving Cone-Beam Ct Quality Using a Cycle-Residual Connection with a Dilated Convolution-Consistent Generative Adversarial Network[J]. Physics in Medicine & Biology, 2022,67(14): 145010.（WOS:000822102200001）
    • Liwei Deng, Jie Hu, Jing Wang, et al.Synthetic CT Generation Based on CBCT Using Respath-cycleGAN[J]. Medical Physics, 2022,49(8): 5317-5329.（WOS:000792418600001）
    • Zhe Yan, Shuchun Chu, Liwei Deng. Visual SLAM Based on Instance Segmentation in Dynamic Scenes[J]. Measurement Science and Technology.2021,32(9):095113.（WOS:000662668300001）

    Conference Papers

    • Liwei Deng*, Yuanzhi Zhang, Xiaofei Wang. High-definition Processing of Remote Sensing Images Based on CUT-CycleGAN[C], The 40th Chinese Control Conference, CCC 2021.( EI：20214311045982).
    • Liwei Deng*, YueWang, Jing Han. Optical Disc Location Based on Similarity toImproved Harris Algorithm[C], The 40th Chinese Control Conference, CCC 2021.( EI：20214311046402).
    • Liwei Deng*, Hongfei Suo, Haonan Ren. Research on Tension Control System of Winding Machine Based on Fuzzy PID Algorithm[C], The 40th Chinese Control Conference, CCC 2021.( EI：20214311045650).
    • Yuheng Yin, Yangang Guo, Liwei Deng*. Research on Image Processing Application of Improved Adaptive Filter Based on LPSO Algorithm[C], The 40th Chinese Control Conference, CCC 2021.( EI：20214311045948).
    • Yanshu Jiang, Mingqi Jia, Biao Zhang, Liwei Deng. Malicious Domain Name Detection Model Based on CNN-GRU-Attention[C], The 33rd Chinese Control and Decision Conference, CCDC 2021.
    • Liwei Deng, Xiaofei Wang.A New Fracture Image Segmentation Method Based on MSA-k Clustering Algorithm[C], The 39th Chinese Control Conference, CCC 2020.( EI：20203909242340).
    • Liwei Deng,Jie Hu,Jingjing Qi. Super-Resolution Reconstruction of Finger Vein Image Based on Regression Tree Model[C],The 32nd Chinese Control and Decision Conference, CCDC 2020.( EI：20204009254996).

Pavithra sekar – Deep Learning – Best Researcher Award

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Pavithra sekar -Deep Learning – Best Researcher Award

Dr. Pavithra sekar distinguished academic and researcher in the field Deep Learning.

🌐 Professional Profile

Educations📚📚📚

She holds a Bachelor of Engineering (B.E.) in Computer Science Engineering, which she completed in April 2006 with a percentage of 75.6%, earning a first-class with distinction from Vel Tech Engineering College, affiliated with Anna University. She further advanced her education by obtaining a Master of Engineering (M.Tech) in Information Technology in June 2011, achieving a percentage of 8.43 and graduating with first-class honors from Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, also affiliated with Anna University. She completed her academic journey with a Ph.D. in Information Technology, awarded on January 24, 2020, from St. Peter’s Institute of Higher Education and Research.

PROFESSIONAL EXPERIENCE:

She possesses about 17 years of experience in the field of education, encompassing teaching, administration, and research. Currently, she holds the position of Assistant Professor Sr in the School of Computer Science and Engineering at Vellore Institute of Technology, Chennai. Her career includes roles such as Assistant Professor (Sr) in the Department of Computer Science and Engineering at VIT Chennai since December 15, 2023. Previously, she served as Assistant Professor (SG) and IPR coordinator in the Department of Information Technology from March 31, 2021, to December 7, 2022, at Rajalakshmi Engineering College. Prior to that, she was Assistant Professor & Assistant HOD in the Computer Science & Engineering Department at VelTech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College for 9.6 years, from July 6, 2011, to January 25, 2021. She began her career as a lecturer in the Department of Information Technology at VelTech MultiTech Dr. Rangarajan Dr. Sakunthala Engineering College from August 24, 2006, to September 25, 2009.

 

DEVELOPMENT ACTIVITIES

She possesses a thorough understanding of her subject area, demonstrating an exceptional ability to effectively communicate complex concepts to her students. Her strong communication and comprehension skills enhance her role in internal administrative tasks within educational institutions. She has a proven track record in coordinating various activities such as symposiums, student projects, and conferences, where she provides guidance and support to ensure successful outcomes. Her extensive experience includes roles as Class Coordinator, Conference Coordinator, Project Coordinator for contests, and organizing events like the MOTOROLA FAER EVENT. Additionally, she has served as ISO coordinator, handled NBA coordination, and participated actively in autonomous file activities. She is involved in setting question papers for autonomous colleges and universities and has excelled in roles such as Class In-Charge, Student Mentor, and AICTE CII-Survey participant. She diligently maintains semester-wise result analysis reports, prepares weekly schedules, and curates course materials for effective teaching. Her commitment to excellence is evident in achieving over 90% results across all subjects and receiving a publication award of 16,000. Notably, she achieved a perfect 100% result in key subjects like Computer Programming, Operating System, Computer Architecture, Advanced Computer Architecture, and Problem Solving And Python Programming.

📝🔬Publications📝🔬

1. S. Pavithra and K. Venkata Vikas, “Detecting Unbalanced Network Traffic Intrusions With Deep
Learning,” in IEEE Access, vol. 12, pp. 74096-74107, 2024, doi: 10.1109/ACCESS.2024.3405187.
2. . Pavithra, T. Veeramani, S. Sree Subha, J.P. Sathish Kumar, S. Shanmugan, Ammar H.
Elsheikh, F.A. Essa, “Revealing prediction of Perched Cum Off-Centered Wick Solar Still
Performance using network based on Optimizer algorithm” Process Safety and
Environmental Protection,Volume 161,2022, Pages 188-200,ISSN 0957-
5820,https://doi.org/10.1016/j.psep.2022.03.0092022, .(SCI, Scopus).)(Impact factor: 7.92).
3. Meena, M., Kavitha, A., Karthick, Pavithra.S “Effect of decorated photoanode of
https://doi.org/10.1007/s12034-022-02828-9 .(SCI, Scopus).)(Impact factor: 1.92).
4. S.Pavithra, P.M Anu “An Efficient Data Aggregation with Optimal Recharging in Wireless
Rechargeable Sensor Networks” (Submission code: IJAIP-221302) for the International
Journal of Advanced Intelligence Paradigms (IJAIP) Inderscience
DOI: 10.1504/IJAIP.2022.10040244,2022 (Scopus). Impact factor ( 0.63)
5. S.Pavithra Assistive Chatbot device to support Visually Impaired Person to access
Transport Mode Status Using Deep Learning Model ARPN Journal of Engineering and
Applied Sciences waiting for Publication 2022. (Scopus).
6. S.Pavithra, R.Karthikeyan P.M Anu “Detection and classification of 2D and 3D Hyper
Spectral Image Using Enhanced Harris Corner Detector” “Scalable Computing: Practice and
Experience, ISSN 1895-1767, Volume 21, Issue 1, pp. 93–100, DOI