Jianjun Yuan | Network Resilience and Robustness | Research Excellence Award

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Prof. Jianjun Yuan | Network Resilience and Robustness | Research Excellence Award

Southwest University | China

Prof. Jianjun Yuan is an established researcher whose work spans intelligent systems, deep learning, and secure networked applications, with strong contributions to intrusion detection, image understanding, and robust optimization models. His research emphasizes lightweight and resilient learning architectures for complex real-world environments, including in-vehicle networks, medical imaging, and high-resolution remote sensing. He has authored 39 scholarly documents, receiving 215 citations across 205 citing publications, and holds an h-index of 9, reflecting sustained research impact and influence. Recent publications include RL-IDS, a robust reinforcement learning–based intrusion detection system for in-vehicle networks (Journal of Information Security and Applications, 2026), IMFF, a dual-space optimization framework for remote sensing scene classification (Expert Systems with Applications, 2026), and MLFD, a multi-level feature disentanglement network for medical image recognition (Expert Systems with Applications, 2025). His work advances robust, interpretable, and application-driven artificial intelligence research.

Citation Metrics (Scopus)

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215

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Featured Publications

Meihui Li | Information Entropy | Excellence in Research Award

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Dr. Meihui Li | Information Entropy | Excellence in Research Award

Yanshan University | China

Dr. Meihui Li is an interdisciplinary researcher whose work bridges music psychology, music therapy, neurorehabilitation, and human–machine interaction, with a strong emphasis on evidence-based intervention and experimental methods. With 7 published documents, 11 citations, and an h-index of 2, her research spans systematic reviews, psychophysiological experiments, and biomimetic design. She has contributed to high-impact journals including Biomimetics, Frontiers in Psychology, Frontiers in Neurology, and PLOS ONE (SCI/SSCI Q1–Q2), covering topics such as piano music therapy for stroke rehabilitation, prosocial music cognition using ERP, sleep-related music therapy, and psychometric assessment in music populations. Her work also advances wearable and robotic exoskeleton systems for piano practice through multi-domain mapping and top-down process models. Dr. Li is a registered PROSPERO systematic reviewer and completed a doctoral dissertation focused on deliberate-practice-based teaching models in music education, highlighting motivation and performance state optimization.

Citation Metrics (Scopus)

12

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11

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2

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7

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Featured Publications

The Impact of Songs with Prosocial Lyrics on Implicit Cognition and Prosocial Behavior:
An Event-Related Brain Potentials Study
– Frontiers in Psychology (SSCI Q1), 2025 | Second Author

Meta-Narrative Review: The Impact of Music Therapy on Sleep and Future Research Directions
– Frontiers in Neurology (SCI Q2), 2024 | Co–First Author

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)

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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.

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📝🔬

Sulyman Abdulkareem – Network Intrusion Detection – Best Researcher Award

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Sulyman Abdulkareem – Network Intrusion Detection – Best Researcher Award

Assoc Prof Dr. Sulyman Abdulkareem  distinguished academic and researcher in the field Network Intrusion Detection. He is a performance-driven Project Manager with an extensive academic background in Management and Information Systems and over 10 years of experience managing cross-functional teams to drive cost-effective technology solutions and execute key business projects in a technical environment. He has a proven ability to effectively manage knowledge, communicate, collaborate, and coordinate core IT functions, third parties, and vendors on initiatives to ensure project integration and alignment with overall requirements, security, compliance, standards, and quality assurance. Adept at managing all aspects of solution delivery, he excels in research, analysis, scope definition, resource planning and allocation, budget management, document development, status reporting, risk management, and change control.

🌐 Professional Profile

Educations📚📚📚

He holds a Doctor of Philosophy in Information and Communication Systems from the University of Surrey and a Master of Science in Management Information Systems with Distinction from Coventry University. Additionally, he has earned several professional certifications, including PRINCE 2 Foundation, CMI Level 7 in Professional Consulting, CMI Level 7 in Strategic Management and Leadership, and a certification in Security Policy Development.

 

PROFESSIONAL EXPERIENCE

Since October 2019, he has been serving as a Project Manager at IMBIL Consultancy Services Limited in London. In this role, he implemented a new Case Management System (CMS) tailored to the department’s specific needs, leading to a 40% increase in workflow efficiency and overall productivity of the legal department. He oversaw the development and implementation of a comprehensive enterprise resource planning (ERP) system for a major client, coordinating cross-functional teams, managing timelines and budgets, and ensuring the project met all specified requirements. He led the integration of a machine learning model into a client solution to predict customer behavior for e-commerce activities, enhancing business intelligence and personalized marketing strategies. Additionally, he implemented a data security system for a financial client in compliance with GDPR, ensuring all project activities adhered to relevant industry standards. He conducted daily stand-ups, regular project retrospectives, and feedback sessions, identifying areas for improvement and implementing process optimizations. By refining development processes, improving communication within the project team, and adopting new tools, he enhanced team efficiency and project quality. He standardized the filing system and promoted the use of cloud documentation and storage via SharePoint. Efficiently organizing and coordinating meetings with stakeholders, he scheduled, sent out invitations, prepared meeting agendas, took minutes, and distributed them promptly to ensure clear communication and accountability. He successfully optimized project processes by conducting gap analysis, gathering data on current processes, identifying associated KPIs, and redesigning these processes to improve alignment with desired outcomes, achieving a 65% improvement in these processes.

 

Doctoral Researcher –University of Surrey

He coordinated the DEDICAT 6G project in collaboration with industry giants like Airbus, Nokia, and Orange under the EU’s Horizon 2020 program, securing over €6 million in funding to enhance network efficiency and security. He led the design and implementation of an advanced Network Intrusion Detection Classifier for IoT networks, achieving superior efficiency and effectiveness compared to existing solutions. His groundbreaking research efforts culminated in the publication of a paper titled “IoT Network Intrusion Detection with Ensemble Learners,” detailing the development and success of an innovative network intrusion detection classifier. He collaborated effectively with project team members and security consultants at Mafic Ltd, contributing significantly to the successful completion of IoT solutions by documenting crucial research findings. He demonstrated proficiency in stakeholder management, and agile, waterfall, lean, PRINCE 2, SAFe, and SDLC methodologies. His expertise spans service management, risk and business analysis, process optimization, planning and budgeting, user acceptance testing (UAT), system administration, change management, requirements analysis, and documentation and reporting. He secured smooth laboratory operations and maintained up-to-date standardized operating procedures, resulting in a remarkable 90% success rate in meeting project deadlines. His planning and organizational skills ensured smooth operations by designing a comprehensive weekly schedule, maximizing productivity and efficiency.

As a lecturer at the University of Ilorin from February 2018 to September 2019, he collaborated with a software firm to develop new cybersecurity protocols, providing students with practical experience and creating innovative solutions that benefited the company. He established numerous partnerships with tech companies to collaborate on applied research projects. He led a pioneering research project on renewable energy technologies, engaging undergraduate and graduate students, yielding sustainable energy solutions and publishable research findings that significantly advanced the academic community’s understanding and the industry’s capabilities. He led annual research conferences and regular workshops at the university, where students and faculty could present their project findings, focusing on trending topics such as AI and inviting industry experts to provide networking opportunities for students. He supervised undergraduate projects, guiding more than 20 students through their research endeavors, achieving a remarkable 100% pass rate and witnessing a notable 20% increase in student engagement and active participation in various research and development initiatives. He successfully managed undergraduate class assignments and assessments, resulting in a high 95% completion rate and a notable 15% grade improvement by implementing strategies such as ensuring 95% lecture attendance and enhancing lecture materials, which reduced failure rates by 5% and fostered increased student engagement.

IT Project Manager

• Successfully led the Lifestalia Loan app project delivery within budget and ahead of schedule; Partnered with Apple and Google to deploy
on the App Store and Google Play store. Collaborated with cross-functional teams to uphold the Agile methodology in delivering this project,
resulting in a 30% improvement in project delivery time and a 15% increase in team productivity.
• Implemented UX enhancements based on findings, conducted comprehensive user research and analysis, and identified pain points and
areas for improvement within the app interface. Achieved a 25% increase in user satisfaction and a 20% decrease in user churn rate.
• Collaborated with the Power BI team to create real-time dashboards accessible to cross-functional teams, providing actionable insights into
current user trends, app usage metrics, and the impact of new and already-existing processes, improving decision-making by 60%.
• Effectively managed a high caseload of tasks while maintaining standards and adhering to established protocols. Prioritised tasks based on
urgency, efficiently allocating time and resources to assist clients and attend to other essential duties.
• Responsible for developing and maintaining project documentation. Utilised Trello, Jira, SharePoint, MS Project, and Visio in managing
multiple projects simultaneously within tight deadlines, ensuring seamless communication among stakeholders by preparing project status
& progress reports. Oversaw project timelines, budget tracking, and risk management to ensure the timely delivery of high-quality solutions
that meet client expectations and drive business growth.
• Oversaw transitioning on-premises servers, applications, and data to cloud-based platforms, AWS specifically. Elicited and documented
requirements for the change and developed UAT scripts and comprehensive migration steps.
• Established a robust risk management framework using the RAID log to identify, assess, and mitigate risks across project operations.
Conducted risk assessments, developed risk registers, and implemented risk mitigation strategies, resulting in a proactive approach to risk
management and a 15% reduction in compliance-related incidents.
• Prepared business and financial cases that contained forecasts, methods, assumptions, adopted metrics and sensitive analysis to help
management and other stakeholders make informed decisions before, during, and after any project.

📝🔬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

Published on by Network Science

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📝🔬

Hedieh Sajedi – Machine learning – Best Researcher Award

Published on by Network Science

Hedieh Sajedi – Machine learning – Best Researcher Award

Dr. Hedieh Sajedi  distinguished academic and researcher in the field Machine learning.  Her research interests encompass a wide range of advanced topics, including deep learning and machine learning, where she delves into the development and refinement of algorithms that enable computers to learn from and make decisions based on data. She is also deeply involved in multimedia processing, exploring techniques to enhance and manipulate various forms of media, such as images, videos, and audio. Additionally, her work in data mining and information retrieval focuses on extracting meaningful patterns and insights from large datasets, improving the efficiency and accuracy of information retrieval systems. Furthermore, she investigates bio-inspired algorithms, drawing inspiration from natural processes to create innovative computational methods that solve complex problems.

🌐 Professional Profile

Educations📚📚📚

She completed her Ph.D. in Artificial Intelligence and Robotics at Sharif University of Technology in May 2010, following her M.Sc. in the same field from the same institution, which she earned in August 2005. Prior to her postgraduate studies, she obtained her B.Sc. in Computer Software Engineering from Amir Kabir University of Technology in September 2002.

Work Experience:

She has delivered several invited talks on various topics, including “Computer vision and machine learning for medical image analysis” at the Children’s International Research Center in Washington DC, USA, in July 2022, and “Age Prediction based on brain MRI images” at Pompeu Fabra University in Barcelona, Spain, in June 2022. Additionally, she discussed a “Blind Spot Warning System based on Vehicle Analysis in Stream Images” at the same university and “Brain Age Estimation based on Brain MRI Images” at Sehir University of Istanbul, Turkey, in March 2018. Earlier, in March 2014, she presented on the “Application of Steganography and Steganalysis Methods in Medical and Healthcare Systems” at the University of Pavia, Italy. Her executive activities include serving as the Scientific Chair of the International Conference on Pattern Recognition and Image Analysis (IPRIA) in 2023, head of the Computer Science Department from 2018 to 2022, and Scientific Chair of the 6th International Conference on Pattern Recognition and Image Analysis at the University of Tehran in 2022. She also held the position of Head of Computer Services and Information Technology in the College of Science from 2018 to 2020 and served as Inspector of the Image Processing and Machine Vision Society in Tehran, Iran, in 2015 and 2017. Her funded projects include research on the “Detection and Classification of Circular Objects on the Basis of Convolutional Neural Network (CNN)” funded by the Iran National Science Foundation (INSF) from 2021 to the present, “Investigating Brain Health from Brain MRI Images Using Machine Learning Methods,” partially funded by the Institute for Research in Fundamental Sciences (IPM) from 2018 to 2019, “Brain Age Estimation with Mathematical Modeling” funded by INSF from 2017 to 2018, and the development of “A High-Security and High-Capacity Steganography System” funded by INSF from 2011 to 2014.

Honors & Awards

She was recognized as a member of the University of Tehran Top Researchers Club in 2022 and received the Erasmus Mobility Award from the European Union in the same year. Additionally, she was honored with the Honors Program Graduate Award from Sharif University of Technology for the period from 2006 to 2010. Since 2009, she has been an active member of the Scientific Society for Image Processing and Machine Vision.

She has been an instructor at the University of Tehran since 2013, teaching courses such as “Machine Learning,” “Artificial Intelligence,” “Data Mining,” and “Digital Image Processing” in the Department of Computer Science. She has also instructed “Advanced Topics in Artificial Intelligence” since 2020 and “Advanced Information Retrieval” from 2017 to 2020. Additionally, she taught “Advances in AI” from 2013 to 2020 and “Machine Learning in Physics” from 2018 to 2019. Her teaching portfolio includes courses for Ph.D. students at the Institute of Biochemistry and Biophysics, such as “Advanced Data Structure” in 2018-2019. At AmirKabir University of Technology, she instructed “Machine Learning” from 2010 to 2011 and “Artificial Intelligence” in 2010-2011. She also co-instructed “Machine Vision” at Pompeu Fabra University in Barcelona, Spain, in May 2022. Her experience in bio-inspired computing includes teaching “Evolutionary Computing” at the University of Tehran from 2013 to 2016.

Furthermore, she has taught “Distributed Systems” at Azad University, Qazvin, from 2011 to 2013, and courses such as “Computer Networks,” “Compiler Design and Principles,” and “Introduction to Programming” at the University of Tehran. She also taught “Operating Systems,” “Introduction to Programming,” and other foundational courses at Tarbiat Moallem University from 2006 to 2008. Her early teaching roles include instructing “Introduction to Programming” at Sharif University of Technology in 2006-2007 and several technical and scientific presentation courses at AmirKabir University of Technology from 2009 to 2011.

📝🔬Publications📝🔬