Sheng Xiang | AI | Research Excellence Award

Published on by Technology Scientists

Assoc. Prof. Dr. Sheng Xiang | AI | Research Excellence Award

Chongqing University of Posts and Telecommunications | China

Assoc. Prof. Dr. Sheng Xiang is an Associate Researcher at Chongqing University, China, specializing in intelligent battery management systems, energy storage analytics, and data-driven prognostics for lithium-ion batteries. His research expertise lies at the intersection of artificial intelligence, machine learning, and electrochemical energy systems, with a particular focus on remaining useful life prediction, state-of-charge estimation, and lightweight deep learning models for real-world battery applications. He has authored 30 peer-reviewed publications, which have collectively received 1,655 citations, reflecting strong international recognition and an h-index of 17. His recent contributions in high-impact journals such as Energy and Journal of Energy Storage demonstrate methodological innovation and practical relevance, especially for electric vehicles and smart energy systems. With an active collaboration network involving 57 co-authors, his work supports interdisciplinary research and global knowledge exchange. The societal impact of his research is evident in its potential to enhance battery safety, efficiency, sustainability, and lifecycle management in next-generation energy technologies.

Citation Metrics (Scopus)

1655
1000
500
100
0

Citations

1655

Documents

30

h-index

17

Citations

Documents

h-index


View Scopus Profile
 View ORCID Profile
 View Google Scholar Profile

Top 5 Featured Publications

Bin Zhang | Artificial Intelligence | Research Excellence Award

Published on by Technology Scientists

Prof. Bin Zhang | Artificial Intelligence | Research Excellence Award

City University of Hong Kong | China

Prof. Bin Zhang is a Professor and senior engineering expert specializing in computer science and intelligent perception, with a strong focus on brain-inspired intelligence, generative models, small object detection, and deep learning–based visual understanding. His research integrates theory and real-world applications in areas such as infrared tiny object detection, intelligent transportation, panoramic vision, spiking neural networks, and natural language processing. He has authored 6 peer-reviewed publications in reputable international journals and conferences, including Sensors and the International Journal of Pattern Recognition and Artificial Intelligence, with his work attracting growing academic citations and visibility. Zhang Bin has led and contributed to multiple nationally recognized innovation initiatives in China and maintains active collaborations with researchers across academia and industry. His research has demonstrated clear social and industrial impact, particularly in smart cities, intelligent sensing, and decision-support systems, advancing practical AI deployment aligned with national and global technological priorities.


View ORCID Profile

Top 5 Featured Publications

Karim El Moutaouakil | Neural network | Research Excellence Award

Published on by Technology Scientists

Prof. Dr. Karim El Moutaouakil | Neural network | Research Excellence Award

Université Sidi Mohamed Ben Abdellah | Morocco

Prof. Dr. Karim El Moutaouakil is an active researcher in artificial intelligence, computational intelligence, and applied optimization, with a strong emphasis on machine learning for data-driven decision systems. His work spans deep learning architectures (LSTM, CNNs, HRNNs), metaheuristic and fractional optimization algorithms, fuzzy systems, and their applications in financial forecasting, big data analytics, healthcare prediction, energy systems, and smart decision support. He has authored 98 peer-reviewed publications, receiving 650 citations with an h-index of 15, reflecting consistent scholarly impact. His research demonstrates methodological innovation, notably the integration of Ito-based optimizers, genetic algorithms, fuzzy logic, and hybrid AI frameworks to enhance model accuracy and robustness. With extensive international collaboration involving 90+ co-authors, his work contributes to interdisciplinary knowledge exchange. The societal relevance of his research is evident in applications addressing economic forecasting, personalized tourism, medical risk prediction, and energy optimization, supporting data-informed policy and sustainable technological development at a global level.

Citation Metrics (Scopus)

650
450
300
150
0

Citations

650

Documents

98

h-index

15

Citations

Documents

h-index


View Scopus Profile
 View ORCID Profile
 View Google Scholar Profile

Top 5 Featured Publications

Abhilash Kancharla | Deep Learning | Editorial Board Member

Published on by Technology Scientists

Dr. Abhilash Kancharla | Deep Learning | Editorial Board Member

The University of Tampa | United States

Dr. Abhilash Kancharla is a researcher at the University of Tampa specializing in advanced computing and next-generation digital technologies, with expertise in edge computing, 6G networks, blockchain-based security and privacy, quantum machine learning, neural-inspired algorithms, and computational modeling of nanomaterials. He has published 23 peer-reviewed research articles, which have received 50 citations, and holds an h-index of 4, reflecting consistent academic impact in emerging interdisciplinary fields. His work is particularly notable for integrating intelligent learning models with secure communication architectures for future wireless networks, as well as applying computational intelligence to the analysis of self-healing materials. Through collaborations with 14 co-authors, he actively contributes to international research networks, fostering cross-disciplinary knowledge exchange. The broader social and technological impact of his research supports the development of secure, intelligent, and sustainable digital infrastructures, with relevance to future communication systems, smart technologies, and advanced material design.

Citation Metrics (Scopus)

50
40
30
20
10
0

Citations

50

Documents

23

h-index

4

Citations

Documents

h-index


View Scopus Profile
 View ORCID Profile
 View Google Scholar Profile

Top 5 Featured Publications

Muhammad Firoz Mridha | Machine Learning | Best Researcher Award

Published on by Technology Scientists

Prof. Dr. Muhammad Firoz Mridha | Machine Learning | Best Researcher Award

Professor | American International University | Bangladesh

Prof. Dr. Muhammad Firoz Mridha, a researcher at the American International University–Bangladesh (AIUB), has established a strong scholarly profile in computer science with notable contributions to machine learning, data analytics, cybersecurity, IoT, and applied artificial intelligence. With 319 publications, over 4,629 citations, and an h-index of 33, his work demonstrates sustained academic productivity and global research impact. His studies often address practical and emerging challenges—such as intelligent decision-support systems, secure digital infrastructures, and data-driven solutions for healthcare and smart environments—positioning his contributions at the intersection of theoretical advancement and real-world application. Collaboration is a defining feature of his career, reflected in partnerships with 575 co-authors, enabling multidisciplinary knowledge exchange and strengthening international research networks. His work has supported technological development, digital inclusion, and innovation-oriented problem-solving, particularly in contexts where data-centric technologies can improve societal outcomes.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Mridha, M. F., Keya, A. J., Hamid, M. A., Monowar, M. M., & Rahman, M. S. (2021). A comprehensive review on fake news detection with deep learning. IEEE Access, 9, 156151–156170.

Cited by: 297

2. Mridha, M. F., Das, S. C., Kabir, M. M., Lima, A. A., Islam, M. R., & Watanobe, Y. (2021). Brain–computer interface: Advancement and challenges. Sensors, 21(17), 5746.

Cited by: 296

3. Jim, J. R., Talukder, M. A. R., Malakar, P., Kabir, M. M., Nur, K., & Mridha, M. F. (2024). Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review. Natural Language Processing Journal, 6, 100059.

Cited by: 271

4. Rayed, M. E., Islam, S. M. S., Niha, S. I., Jim, J. R., Kabir, M. M., & Mridha, M. F. (2024). Deep learning for medical image segmentation: State-of-the-art advancements and challenges. Informatics in Medicine Unlocked, 47, 101504.

Cited by: 227

5. Mridha, M. F., Lima, A. A., Nur, K., Das, S. C., Hasan, M., & Kabir, M. M. (2021). A survey of automatic text summarization: Progress, process and challenges. IEEE Access, 9, 156043–156070.

Cited by: 197

Prof. Dr. Muhammad Firoz Mridha’s research advances data-driven intelligence and secure digital systems, contributing to global technological innovation and societal problem-solving. His work supports scalable, real-world applications—particularly in developing regions—promoting inclusive, ethical, and sustainable digital transformation.

Tianyuan Liu | Machine Learning | Best Researcher Award

Published on by Technology Scientists

Assoc. Prof. Dr. Tianyuan Liu | Machine Learning | Best Researcher Award

Master’s Supervisor | Donghua University | China

Assoc. Prof. Dr. Tianyuan Liu, affiliated with Donghua University, Shanghai, China, is a distinguished researcher specializing in industrial intelligence, human-centric manufacturing, and vision-based quality inspection. With 43 publications, 1,103 citations, and an h-index of 17, Dr. Liu’s work reflects significant academic impact and steady scholarly growth in intelligent industrial systems. His research integrates cognitive computing, deep learning, and large language models to enhance manufacturing precision, reliability, and adaptability. Notably, his 2025 article “Analysis of causes of welding defects in bridge weathering steel based on large language models” in the Journal of Industrial Information Integration demonstrates his pioneering approach to applying AI-driven diagnostic systems in structural materials engineering. Another major contribution, “Causal deep learning for explainable vision-based quality inspection under visual interference” published in Journal of Intelligent Manufacturing, advances explainable AI (XAI) frameworks for real-time industrial inspection, ensuring transparency and accuracy in automated decision-making. His review, “Towards cognition-augmented human-centric assembly: A visual computation perspective”, underscores his vision for augmenting human intelligence with computational cognition to achieve collaborative, efficient, and sustainable manufacturing systems. Furthermore, his book chapter “Industrial Intelligence: Methods and Applications” provides a comprehensive view of the synergy between AI and industrial processes, shaping the academic and applied discourse in smart factories. Assoc. Prof. Dr. Liu’s contributions collectively enhance the fusion of AI, cognition, and industrial engineering, driving forward the next generation of intelligent, explainable, and human-oriented manufacturing ecosystems.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Zhang, R., Lv, Q., Li, J., Bao, J., Liu, T., & Liu, S. (2022). A reinforcement learning method for human-robot collaboration in assembly tasks. Robotics and Computer-Integrated Manufacturing, 73, 102227.
Cited by: 182.

2. Zhou, B., Bao, J., Li, J., Lu, Y., Liu, T., & Zhang, Q. (2021). A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops. Robotics and Computer-Integrated Manufacturing, 71, 102160.
Cited by: 152.

3. Zhou, B., Shen, X., Lu, Y., Li, X., Hua, B., Liu, T., & Bao, J. (2023). Semantic-aware event link reasoning over industrial knowledge graph embedding time series data. International Journal of Production Research, 61(12), 4117–4134.
Cited by: 123.

4. Zhou, B., Li, X., Liu, T., Xu, K., Liu, W., & Bao, J. (2024). CausalKGPT: Industrial structure causal knowledge-enhanced large language model for cause analysis of quality problems in aerospace product manufacturing. Advanced Engineering Informatics, 59, 102333.
Cited by: 114.

5. Liu, T., Bao, J., Wang, J., & Zhang, Y. (2018). A hybrid CNN–LSTM algorithm for online defect recognition of CO₂ welding. Sensors, 18(12), 4369.
Cited by: 105.

Assoc. Prof. Dr. Tianyuan Liu’s research bridges artificial intelligence and industrial engineering, advancing smart, explainable, and human-centric manufacturing solutions that empower global industry transformation.

Minoru Sasaki | Artificial Intelligence | Best Researcher Award

Published on by Technology Scientists

Prof. Dr. Minoru Sasaki | Artificial Intelligence | Best Researcher Award

Organizing Committee | Gifu University | Japan

Prof. Dr. Minoru Sasaki, a distinguished Emeritus Professor at Gifu University, has made significant contributions to the fields of mechanical engineering, control systems, and mechatronics throughout his academic and professional career. With a Ph.D. in Mechanical Engineering from Tohoku University (1985), he has held various academic positions in Japan and internationally, including visiting professorships at UCLA, Georgia Institute of Technology, and King Mongkut’s University of Technology Thonburi. He has also served in numerous leadership roles at Gifu University, such as Department Chair, Assistant President, and Director of the Career Center. His professional affiliations include IEEE Life Senior Member, ASME, JSME, SICE (Fellow), RSJ, JSASS, and others. He has actively contributed to global academic and research communities through editorial roles in prestigious journals and program committees of international conferences. His involvement extends to advisory roles and leadership positions within key engineering societies in Japan and abroad. A prolific researcher, Dr. Sasaki has authored 202 publications, which have been cited by 966, reflecting a strong academic impact with an h-index of 14. These metrics highlight the depth and relevance of his research in intelligent mechanical systems and applied electromagnetics.

Profiles: Scopus | Google Scholar | ORCID

Featured Publications

1. Taheri, S. M., Matsushita, K., & Sasaki, M. (2017). Virtual reality driving simulation for measuring driver behavior and characteristics. Journal of Transportation Technologies, 7(02), 123.
Cited by 84.

2. Takayama, K., & Sasaki, M. (1983). Effects of radius of curvature and initial angle on the shock transition over concave and convex walls. Report of the Institute of High Speed Mechanics, 46, 1–30.
Cited by 66.

3. Yoshida, T., Sasaki, M., Ikeda, K., Mochizuki, M., Nogami, Y., & Inokuchi, K. (2002). Prediction of coal liquefaction reactivity by solid state 13C NMR spectral data. Fuel, 81(11-12), 1533–1539.
Cited by 64.

4. Endo, T., Sasaki, M., Matsuno, F., & Jia, Y. (2016). Contact-force control of a flexible Timoshenko arm in rigid/soft environment. IEEE Transactions on Automatic Control, 62(5), 2546–2553.
Cited by 61.

5. Takeda, K., Sasaki, M., Kieda, N., Katayama, K., Kako, T., Hashimoto, K., … (2001). Preparation of transparent super-hydrophobic polymer film with brightness enhancement property. Journal of Materials Science Letters, 20(23), 2131–2133.
Cited by 56.

Faten AlQaifi | Artificial Intelligence | Best Researcher Award

Published on by Technology Scientists

Dr. Faten AlQaifi | Artificial Intelligence | Best Researcher Award

Atilim University, Turkey.

Dr. Faten Musaed Alqaifi is a multidisciplinary researcher currently pursuing her Master’s in Healthcare Management at Atılım University, Turkey. She brings a unique blend of expertise in dental surgery, healthcare management, and artificial intelligence, having earned her MBA in Healthcare Management from UTM, Malaysia. With academic excellence reflected in her top CGPAs, she has contributed to both clinical and psychosocial research domains. Her recent studies explore AI’s role in improving oral healthcare outcomes and the psychological dimensions of international student life. She has worked as a general dentist, educator, and volunteer in diverse cultural settings, underscoring her global adaptability and social commitment. Fluent in Arabic, English, and intermediate Turkish, Dr. Alqaifi exemplifies the qualities of a globally engaged researcher.

🌐Author Profiles

Strengths for the Award

  1. Interdisciplinary Research in Healthcare and AI
    Faten Alqaifi has shown a strong interdisciplinary approach, particularly in integrating artificial intelligence into healthcare systems. Her 2024 publication titled “Artificial intelligence’s impact on oral healthcare in terms of clinical outcomes: a bibliometric analysis” reflects a forward-thinking research agenda. By analyzing AI’s role in improving clinical outcomes, she contributes meaningfully to a high-impact and emerging area in health sciences and management.

  2. Empirical Work in Mental Health and Social Integration
    Her 2025 paper, “International students’ adaptation in Ankara: The mediating roles of anxiety and self-esteem”, published in the International Journal of Intercultural Relations, indicates her engagement with global psychological and sociocultural issues. This work not only highlights her concern for vulnerable populations but also showcases the use of rigorous methodologies in behavioral and intercultural research.

  3. Diverse Experience and Multilingual Skills
    Faten has a well-rounded professional background as a dentist, academic tutor, volunteer, and general manager. She speaks Arabic natively and is proficient in English and Turkish, enabling her to conduct research and collaboration across regions. Her use of advanced research tools (e.g., SPSS, SmartPLS, bibliometric software) further enhances her research capacity.

🔹 Education 

Dr. Alqaifi holds a Bachelor’s in Dental Surgery from the Yemeni University of Science and Technology, where she ranked fifth in her class. She earned her first Master’s degree—an MBA in Healthcare Management—from Universiti Teknologi Malaysia (UTM) with a stellar GPA of 3.94/4. Building on her academic and clinical background, she is now completing a second Master’s in Healthcare Management at Atılım University, Turkey, where she maintains a GPA of 3.93/4. Her ongoing thesis investigates the adoption of artificial intelligence in dentistry, combining technology, healthcare policy, and clinical practice. This educational trajectory highlights her commitment to interdisciplinary learning and research excellence.

🔹 Research Focus on Artificial Intelligence

Dr. Faten Alqaifi’s research lies at the intersection of healthcare innovation, artificial intelligence, and behavioral science. Her ongoing thesis on AI integration in dentistry demonstrates a forward-looking approach to digital transformation in healthcare. Her research agenda emphasizes both technological effectiveness and human-centered outcomes, as evidenced by her bibliometric analysis on AI’s clinical impact in oral healthcare. Simultaneously, she investigates psychological dimensions of social adaptation, particularly anxiety and self-esteem among international students—adding depth to her interdisciplinary reach. She is proficient in tools like SPSS, SmartPLS, and bibliometric analysis platforms, enabling her to conduct statistically robust, data-driven research. Faten’s aim is to bridge the digital and human aspects of healthcare policy and practice.

📚 Publication Top Notes

1. International Students’ Adaptation in Ankara: The Mediating Roles of Anxiety and Self-Esteem

Author: Faten Alqaifi
Journal: International Journal of Intercultural Relations, Vol. 108, Article 102249, 2025
Summary:
This study explores the psychological adjustment process among international students in Ankara, focusing on the mediating impact of anxiety and self-esteem on their adaptation levels. Using structural equation modeling, Alqaifi identifies critical pathways by which mental health factors influence sociocultural integration. The research provides insights for universities and policymakers aiming to improve the student experience in multicultural settings.

2. Artificial Intelligence’s Impact on Oral Healthcare in Terms of Clinical Outcomes: A Bibliometric Analysis

Authors: Faten Alqaifi, D. Tengilimoglu, I. Arslan Aras
Journal: Journal of Health Organization and Management, 2024
Summary:
This bibliometric study investigates global research trends on AI applications in oral healthcare. Analyzing data from Scopus and Web of Science, the authors assess publication patterns, key contributors, emerging keywords, and citation landscapes. The study concludes that AI is increasingly driving diagnostic precision, treatment planning, and clinical outcome optimization in dentistry. It positions AI as a transformative force and sets the foundation for future strategic investments in digital dentistry.

Conclusion

Dr. Faten Musaed Alqaifi shows strong potential for recognition as an emerging interdisciplinary researcher in healthcare, AI, and social sciences. Her academic excellence, early contributions in high-relevance areas, and diverse experience make her a promising candidate for the Best Researcher Award. However, to fully meet the expectations of this award at a global competitive level, she should aim to expand her publication portfolio, lead independent research projects, and pursue collaborative or funded initiatives. With her current trajectory and dedication, she is on a strong path to achieving these milestones.