Xingyu Zhou | Engineering | Research Excellence Award

Published on by Research Scientist

Prof. Xingyu Zhou | Engineering | Research Excellence Award

Assistant Professor | Beijing Institute of Technology | China

Prof. Xingyu Zhou is an Associate Professor at the School of Mechanical Engineering, Beijing Institute of Technology, China, specializing in renewable energy systems and electric vehicle (EV) powertrain optimization. His research integrates deep learning, stochastic modeling, and co-optimization techniques to enhance EV efficiency, safety, and ecological performance. He has authored 30 publications, cited over 494 Citations, and maintains collaborations with 36 co-authors, contributing to high-impact journals such as Applied Energy and Journal of Power Sources. Prof. Zhou’s work on predictive speed planning, powertrain energy management, and renewable energy integration advances sustainable transportation solutions, promoting environmentally responsible and technologically innovative mobility systems globally.

Citation Metrics (Scopus)

800

600

400

200

0

Citations
494

Documents
30

h-index
11

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile

Featured Publications

Sabitha Vemula | Engineering | Best Researcher Award

Published on by Research Scientist

Mrs. Sabitha Vemula | Engineering | Best Researcher Award

Associate Professor | Vaagdevi College Of Engineering | India

Mrs. Sabitha Vemula  is an Associate Professor at Vaagdevi College of Engineering with expertise in signal and image processing, medical image analysis, machine learning, and VLSI-related systems. Her research is strongly focused on MRI-based brain tumor detection and classification, integrating kernel methods, support vector machines, convolutional neural networks, and transformer architectures. She has authored over 7 peer-reviewed publications in international journals, IEEE/Elsevier conference proceedings, and edited volumes, accumulating more than 37 citations. Her work reflects sustained interdisciplinary collaboration and contributes to improved diagnostic accuracy, healthcare decision support, and intelligent imaging systems with clear societal and clinical relevance.

Citation Metrics (Google Scholar)

80

60

40

20

0

Citations
37

Documents
7

h-index
2

🟦 Citations 🟥 Documents 🟩 h-index

View Google Scholar Profile

Featured Publications


MRI brain tumor detection and classification using KPCA and KSVM.

– Materials Today: Proceedings. (2021). Citations: 21

Transformer-enhanced MRI analysis for brain tumor detection with kernel-based PCA and support vector techniques.

– International Conference on Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC) . (2024). 

Ei Mehdi Chakour | Computer Science | Research Excellence Award

Published on by Research Scientist

Dr. Ei Mehdi Chakour | Computer Science | Research Excellence Award

Research Postdoc | Université Sidi Mohamed Ben Abdellah | Morocco

Dr. Ei Mehdi Chakour is a researcher at Université Sidi Mohamed Ben Abdellah, Fez, Morocco, specializing in medical image analysis and deep learning applications for ophthalmology, particularly diabetic retinopathy detection. With four peer-reviewed publications and 16 citations, Dr. Chakour has contributed to advancements in retinal image segmentation, enhancement, and severity classification using dynamic preprocessing, mathematical morphology, and transfer learning techniques. His collaborative work involves 11 co-authors across international conferences and journals, reflecting a strong commitment to interdisciplinary research. Through the development of mobile-based deep learning systems, his work demonstrates significant societal impact by enabling earlier, accessible, and accurate diabetic retinopathy screening.

Citation Metrics (Scopus)

80

60

40

20

0

Citations
16

Documents
4

h-index
2

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile
             View ORCID Profile

Featured Publications


Mobile‑based deep learning system for early detection of diabetic retinopathy.

– Intelligence‑Based Medicine. Advance online publication. (2025). 

Transfer learning for severity and stages detection of diabetic retinopathy.

-Embedded Systems and Artificial Intelligence (ESAI) . (2024).

Blood vessel segmentation of retinal fundus images using dynamic preprocessing and mathematical morphology.

– International Conference on Control, Decision and Information Technologies (CoDIT). (2022). 

Yirga Munaye | Computer Science | Best Researcher Award

Published on by Research Scientist

Assoc. Prof. Dr. Yirga Munaye | Computer Science | Best Researcher Award

Postdoctoral Researcher | Bahir Dar University | Ethiopia

Dr. Yirga Munaye is a distinguished researcher at Bahir Dar University, Ethiopia, specializing in wireless communication, UAV systems, artificial intelligence, IoT, and cybersecurity. With 36 publications and 599 citations, he has made significant contributions to UAV positioning, indoor/outdoor localization, deep learning for resource management, and secure network infrastructures. His collaborations span 36 international co-authors, reflecting strong interdisciplinary and global engagement. Dr. Munaye’s research addresses critical societal challenges, including smart connectivity, network optimization, and AI-driven security solutions, advancing both theoretical knowledge and practical applications in emerging technologies worldwide.

Citation Metrics (Scopus)

800

600

400

200

0

Citations
599

Documents
36

h-index
11

🟦 Citations 🟥 Documents 🟩 h-index

View Scopus Profile
             View Google Scholar Profile

Featured Publications


Cyber security: State of the art, challenges and future directions.


– Cyber Security and Applications, 2024 · 354 citations

 

Ahmed El-Sherbeeny | Human Factors Engineering | Best Researcher Award

Published on by Research Scientist

Dr. Ahmed El-Sherbeeny | Human Factors Engineering | Best Researcher Award

Assistant Professor | King Saud University  | Saudi Arabia

Dr. Ahmed M. El-Sherbeeny is an Assistant Professor of Industrial Engineering at King Saud University, specializing in safety engineering, human factors and ergonomics, and environmental engineering. With an extensive interdisciplinary research portfolio, he has contributed significantly to high-impact domains including public health, intelligent systems, machine learning, environmental remediation, and industrial sustainability. His scholarly impact is reflected in 456 publications 7,784 citations, an h-index of 44, and an i10-index of 135, positioning him as a highly influential researcher within and beyond his primary discipline.Dr. El-Sherbeeny has collaborated on several landmark global studies most notably the Recovery trials published in The Lancet, which transformed clinical decision-making for COVID-19 treatment. His co-authored works on Tocilizumab Casirivimab/Imdevimab Azithromycin Baricitinib and corticosteroid therapy collectively account for thousands of citations underscoring his role in research that shaped international health policies. Beyond clinical research he has made notable contributions to intelligent security frameworks for electric vehicles machine-learning-driven medical diagnostics and blockchain-based IoT access control demonstrating strong engagement with emerging technological frontiers.In the environmental and materials engineering domains his publications address photocatalytic degradation nanocomposites for pollutant removal drug-delivery materials groundwater quality assessment and energy-efficient systems reflecting an impressive breadth of problem-solving across sustainability and environmental protection. His collaborative works span multiple continents involving international teams from engineering medical sciences environmental chemistry and computational intelligence.Dr. El-Sherbeeny’s research outputs appearing in top-tier journals such as The Lancet Chemical Engineering Journal Journal of Hazardous Materials ACS Omega and IEEE Access highlight both methodological rigor and societal impact. His contributions advance safer industrial systems enhance public health resilience support data-driven environmental management and promote technologically integrated solutions for global sustainability challenges.

Profiles : ORCID | Scopus | Google Scholar

Featured Publications

1.Avatefipour, O., Al-Sumaiti, A. S., El-Sherbeeny, A. M., Awwad, E. M., & Elmeligy, M. A. (2019). An intelligent secured framework for cyberattack detection in electric vehicles’ CAN bus using machine learning. Cited By : 185

2.Nadeem, A., Ahmad, S. F., Al-Harbi, N. O., Fardan, A. S., & El-Sherbeeny, A. M. (2017). IL-17A causes depression-like symptoms via NFÎşB and p38MAPK signaling pathways in mice: Implications for psoriasis associated depression.
Cited By : 172

3.Nadeem, A., Al-Harbi, N. O., Al-Harbi, M. M., El-Sherbeeny, A. M., & Ahmad, S. F. (2015). Imiquimod-induced psoriasis-like skin inflammation is suppressed by BET bromodomain inhibitor in mice through RORC/IL-17A pathway modulation. Pharmacological Research, 99, 248–257.  Cited By : 132

4.Mahum, R., Rehman, S. U., Meraj, T., Rauf, H. T., Irtaza, A., & El-Sherbeeny, A. M. (2021). A novel hybrid approach based on deep CNN features to detect knee osteoarthritis. Cited By : 117

5.Yang, X., Wang, J., El-Sherbeeny, A. M., AlHammadi, A. A., & Park, W. H. (2022). Insight into the adsorption and oxidation activity of a ZnO/piezoelectric quartz core-shell for enhanced decontamination of ibuprofen: Steric, energetic, and oxidation studies. Cited By : 98

Dr. Ahmed M. El-Sherbeeny’s interdisciplinary research integrates industrial engineering, machine learning, and environmental sciences to advance public health, industrial safety, and sustainable technologies. His work informs global healthcare strategies, enhances intelligent systems, and drives innovative solutions with broad societal and industrial impact.

Tzu Wen Kuo | Engineering | Editorial Board Member

Published on by Research Scientist

Assist. Prof. Dr Tzu Wen Kuo | Engineering | Editorial Board Member

Architect | Private Chinese Culture University  | Taiwan

Assist. Prof. Dr Tzu Wen Kuo is a dedicated scholar and practitioner in the field of technological disaster prevention, currently serving as a Lecturer in the Department of Architecture and Urban Design at the Chinese Culture University in Taipei, Taiwan. He is also a practicing architect and an active instructor for architectural license examination preparation, demonstrating a strong commitment to bridging academic knowledge with professional practice. Kuo is presently pursuing his PhD in the Department of Architecture at the National Taiwan University of Science and Technology, where his doctoral research focuses on enhancing safety, resilience, and emergency response mechanisms in built environments.Kuo’s research centers on integrating advanced technologies into disaster prevention frameworks, particularly with respect to fire safety, emergency evacuation, and smart building systems. His scholarly contributions reflect a strong emphasis on simulation-based analysis, digital tools, and mobile-assisted evacuation strategies. He has authored multiple peer-reviewed journal articles, including recent works published in Fire and the International Journal of Environmental Research and Public Health. His studies ranging from QR code-enabled fire rescue notification systems to smartphone-based evacuation guidance and stadium evacuation efficiency—highlight his interdisciplinary approach that combines architecture, information technology, and public safety engineering.Through collaborations with academic and industry experts, Kuo contributes to practical solutions that strengthen building safety management and emergency preparedness across various public infrastructures. His work provides empirical insights that support policymakers, architects, and safety professionals in developing more efficient, technology-enhanced disaster response strategies. With growing citations and recognition in the field, Kuo’s research continues to advance the integration of smart technologies into architectural planning and urban safety systems.

Profile : ORCID 

Featured Publications

1.Yang, C.-H., Lin, C.-Y., & Kuo, T.-W. (2025). Simulation-based assessment of evacuation efficiency in sports stadiums: Insights from case studies. Fire, 8(6), 210.

2.Kuo, T.-W., & Lin, C.-Y. (2025). Smart building technologies for fire rescue: A QR code-enabled notification system. Fire, 8(3), 114..

3.Kuo, T.-W., Lin, C.-Y., Chuang, Y.-J., & Hsiao, G. L.-K. (2022). Using smartphones for indoor fire evacuation. International Journal of Environmental Research and Public Health, 19(10), 6061.

Hadi Gokcen | Engineering | Best Researcher Award

Published on by Research Scientist

Prof. Hadi Gokcen | Engineering | Best Researcher Award

Professor | Gazi University Industrial Engineering Department | Turkey

Dr. Hadi Gökçen, affiliated with Gazi University, Ankara, Turkey, is a distinguished researcher recognized for his influential contributions to industrial engineering, operations research, and computational intelligence. With 51 published documents, an h-index of 23, and more than 1,920 citations from 1,367 citing documents, his scholarly impact spans data-driven decision systems, intelligent manufacturing, and applied artificial intelligence. His recent works reflect a strong integration of machine learning, optimization, and sustainability in solving real-world industrial and economic problems. In Computational Economics , he introduced a hybrid machine learning model that combines clustering and stacking ensemble approaches for improved real estate price prediction. His research published in Applied Sciences Switzerland, proposed a dynamic scheduling method for identical parallel-machine environments through a multi-purpose intelligent utility framework. In Flexible Services and Manufacturing Journal, he presented innovative balancing and sequencing strategies for mixed-model parallel robotic assembly lines, emphasizing energy-efficient production. Further, his Survey Review paper applied hybrid unsupervised learning to identify sub-real estate markets, enhancing prediction accuracy and market segmentation. His contribution to developing a Digital Transformation Perception Scale underscores his focus on organizational innovation and industrial adaptation within the Industry paradigm. Dr. Gökçen’s interdisciplinary research bridges artificial intelligence, optimization, and digital transformation, advancing the understanding and implementation of intelligent, sustainable, and adaptive systems in engineering and economic domains.

Profiles : ORCID | Scopus | Google Scholar 

Featured Publications

1. Demirel, N. Ö., & Gökçen, H. (2008). A mixed integer programming model for remanufacturing in reverse logistics environment. The International Journal of Advanced Manufacturing Technology, 39(11), 1197–1206.
Cited By : 258

2. Demirel, E., Demirel, N., & Gökçen, H. (2016). A mixed integer linear programming model to optimize reverse logistics activities of end-of-life vehicles in Turkey. Journal of Cleaner Production, 112, 2101–2113.
Cited By : 247

3. Gökçen, H., Ağpak, K., & Benzer, R. (2006). Balancing of parallel assembly lines. International Journal of Production Economics, 103(2), 600–609.
Cited By : 226

4. Gökçen, H. (2007). Yönetim bilgi sistemleri. Ankara: Palme Yayıncılık.
Cited By : 217

5. Erel, E., & Gökçen, H. (1999). Shortest-route formulation of mixed-model assembly line balancing problem. European Journal of Operational Research, 116(1), 194–204.
Cited By : 189

Mohammad Taghilou | Engineering | Best Researcher Award

Published on by Research Scientist

Assoc. Prof. Dr. Mohammad Taghilou | Engineering | Best Researcher Award

Associate professor | University of Zanjan | Iran

Dr. Mohammad Taghilou is an Associate Professor in the Department of Mechanical Engineering at the University of Zanjan, Iran. His research expertise lies in heat transfer, phase change problems, energy storage, and porous media, with an emphasis on the lattice Boltzmann method (LBM) and its applications in thermal systems. Over his academic career, he has authored impactful studies published in leading journals such as Applied Thermal Engineering, Computers & Mathematics with Applications, and the International Journal of Thermal Sciences. His most cited works explore PCM solidification, nanofluid-based heat exchangers, and the thermal behavior of energy storage systems. Dr. Taghilou’s studies significantly contribute to advancing thermal management technologies, including applications in lithium-ion batteries, heat sinks, and double-pipe exchangers, with an aim to enhance energy efficiency and system reliability. His collaborations with international scholars from institutions such as the University of Tehran, Aalto University (Finland), and the University of Technology Sydney have expanded the interdisciplinary reach of his research. With 541 total citations, an h-index of 15, and an i10-index of 16, his work demonstrates both academic impact and global relevance. Through innovative numerical modeling and experimental approaches, Dr. Taghilou continues to advance understanding in phase-change thermal systems and nanomaterial-enhanced heat transfer, fostering sustainable energy applications and modern engineering solutions.

Profiles: ORCID | Scopus | Google Scholar | ResearchGate | LinkedIn

Featured Publications

1.Sajedi, R., Osanloo, B., Talati, F., & Taghilou, M. (2016). Splitter plate application on the circular and square pin fin heat sinks.  Cited by : 70

2.Taghilou, M., & Rahimian, M. H. (2014). Investigation of two-phase flow in porous media using lattice Boltzmann method. Cited By : 49

3.Talati, F., & Taghilou, M. (2015). Lattice Boltzmann application on the PCM solidification within a rectangular finned container. Cited By : 44

4.Taheri, A. A., Abdali, A., Taghilou, M., Alhelou, H. H., & Mazlumi, K. (2021). Investigation of mineral oil-based nanofluids effect on oil temperature reduction and loading capacity increment of distribution transformers.
Cited By : 40

5.Taghilou, M., & Khavasi, E. (2020). Thermal behavior of a PCM filled heat sink: The contrast between ambient heat convection and heat thermal storage. Cited By : 36

Sakshi Dua | Engineering | Best Researcher Award

Published on by Research Scientist

Assoc. Prof. Dr. Sakshi Dua | Engineering | Best Researcher Award

Associate Professor | Lovely Professional University | India

Dr. Sakshi Dua is an accomplished academic and researcher currently serving as Associate Professor at the School of Computer Applications, Lovely Professional University, Jalandhar-Phagwara, Punjab, India. She holds a Ph.D. in Computer Science and has over 14 years of professional experience as Assistant Professor before her current role. Her research interests span artificial intelligence, Internet of Things, Arduino, machine learning, fuzzy systems, network operating systems, and database management systems. She has contributed as Guest Editor for reputed ABDC and Scopus-indexed journals, authored book chapters with CRC Press, Taylor & Francis, and IGI Global, and is actively involved in book editorial projects with CRC Press and Emerald. She has published widely in SCIE, Scopus, ABDC, and UGC-indexed journals, as well as in IEEE and Springer conferences, and has presented her research internationally. Her contributions extend to applied innovation with patents and copyrights in diverse areas such as smart healthcare, ICT, and IoT-based solutions. She has chaired sessions at IEEE conferences, delivered workshops and FDPs, and guided students through impactful academic and research projects. Her skills include advanced data analysis, algorithm design, applied AI and IoT development, research writing, and academic leadership. Dr. Sakshi Dua has earned recognition through her impactful scholarly work, editorial leadership, and strong community engagement. She has received 71 citations by 9 documents with an h-index of 1.

Profile :  Scopus

Featured Publication

1. Dua, S. (2025). Blockchain-based node authentication algorithm for securing electronic health record data transmission.

Yu Cheng Wang | Engineering | Best Research Article Award

Published on by Research Scientist

Assoc. Prof. Dr. Yu Cheng Wang | Engineering | Best Research Article Award

Aeronautical Engineering Of Chair at Chaoyang University of Technology | Taiwan

Prof. Dr. Yu Cheng Wang  is a distinguished academic and researcher, currently serving as Associate Professor and Chair of the Department of Aeronautical Engineering at Chaoyang University of Technology, Taiwan. He holds a Ph.D. in Industrial Engineering from Feng Chia University and has built a reputation for advancing research at the intersection of aeronautical systems, intelligent manufacturing, and explainable artificial intelligence. With more than publications in SCI and Scopus-indexed journals, his contributions have made significant impact in manufacturing optimization and decision-support systems. He has an h-index of  with over citations, reflecting the scholarly influence of his work. Prof. Wang also collaborates extensively with colleagues across Taiwan and internationally, bridging academic research and industry practice. His work on Industry 4.0 applications in semiconductor manufacturing showcases his commitment to developing transparent and human-centered AI systems that directly address real-world industrial challenges.

Professional Profile

ORCID Profile | Scopus Profile

Education 

Prof. Dr. Yu Cheng Wang pursued his academic journey with a focus on engineering, systems, and innovation. He earned his Ph.D. in Industrial Engineering from Feng Chia University, Taiwan, where his doctoral research laid the foundation for his expertise in intelligent systems and complex manufacturing processes. His educational background reflects a strong balance between theoretical modeling and applied problem-solving. Dr. Wang’s training emphasized operations research, production systems, and the integration of artificial intelligence into industrial applications, which later expanded into explainable AI frameworks for decision support. His solid grounding in industrial engineering principles has allowed him to extend his research into aeronautical systems and semiconductor manufacturing. With this interdisciplinary academic foundation, he has successfully bridged domains such as fuzzy theory, optimization, and smart manufacturing, enabling him to pursue pioneering research in Industry 4.0. His educational journey demonstrates a commitment to combining engineering rigor with innovative technological applications.

Experience 

Prof. Dr. Yu-Cheng Wang has extensive academic and professional experience that combines leadership, research, and industry collaboration. As Department Chair and Associate Professor at Chaoyang University of Technology, he oversees curriculum development, research strategy, and faculty mentorship in aeronautical engineering. His leadership extends to managing cross-disciplinary projects that integrate aeronautical engineering with intelligent manufacturing and artificial intelligence applications. He has spearheaded major research initiatives, including the Industry 4.0 XAI project for wafer-fab output forecasting, a groundbreaking effort that combines machine learning with interpretability for industrial decision-making. His experience also spans consultancy projects that provide practical solutions for semiconductor manufacturing, aligning academic research with industry needs. Prof. Wang’s editorial contributions over appointments demonstrate his recognition as a peer reviewer and thought leader in his field. Through collaborations with colleagues such as Tin-Chih Toly Chen and Chi-Wei Lin, he has broadened his international research presence and strengthened academia-industry knowledge exchange.

Research Interest

Prof. Dr. Yu-Cheng Wang’s research interests lie at the intersection of aeronautical engineering, smart manufacturing, and artificial intelligence. His primary focus is on explainable AI (XAI), where he develops models that not only achieve predictive accuracy but also provide transparency and interpretability for industrial decision-makers. He applies these methods to semiconductor manufacturing, Industry 4.0 environments, and production planning, ensuring that complex systems are optimized while remaining human-understandable. His work extends to fuzzy theory and decision analytics, particularly in contexts where uncertainty and complexity are critical, such as aerospace systems and large-scale industrial operations. Beyond manufacturing, Dr. Wang also explores applications of XAI in training and maintenance, including VR-based approaches for sustainable engineering education. By linking advanced computational models with practical engineering needs, his research contributes to both academic advancement and industry transformation, ensuring technological innovation supports efficiency, sustainability, and human factors integration.

Award and Honor

Prof. Dr. Yu-Cheng Wang has earned recognition for his scholarly contributions and leadership in the fields of aeronautical engineering and artificial intelligence. His publications in high-impact international journals such as The International Journal of Advanced Manufacturing Technology, Complex & Intelligent Systems, and Decision Analytics Journal highlight his academic influence and earned him strong citation metrics, with an h-index of 15 and more than citations. These achievements reflect his standing in the research community. His editorial appointments  across SCI and Scopus-indexed journals demonstrate the trust placed in him as a global reviewer and evaluator of cutting-edge research. He has also been actively involved in industry-driven projects, bridging academia and practical innovation, which further highlights his leadership. Recognition through research funding, collaborations, and invitations to contribute to international projects underscores his role as a thought leader. Collectively, these honors validate his impact as a forward-looking scientist and educator.

Research Skill

Prof. Dr. Yu Cheng Wang possesses a robust set of research skills that combine technical depth with interdisciplinary application. He is proficient in developing explainable AI frameworks, integrating advanced machine learning models with interpretability methods such as SHAP and rule-based surrogates to improve transparency in industrial decision systems. His expertise extends to fuzzy theory, production planning, and smart manufacturing analytics, making him adept at tackling complex and uncertain problems in both aeronautical and industrial domains. He has successfully applied these skills to semiconductor manufacturing, leading research on wafer-fab output forecasting that directly supports industry needs. In addition to computational modeling, Dr. Wang demonstrates strong skills in data analytics, simulation, and optimization, enabling him to bridge theory with real-world application. His experience with large-scale collaborations and consultancy projects further reflects his ability to integrate technical innovation with industry practices, positioning him as both a problem solver and research leader.

Publication Top Notes

Title: An explainable decision model for selecting facility locations in supply chain networks
Authors: Tin-Chih Toly Chen; Yu-Cheng Wang; Yi-Chi Wang
Journal: Supply Chain Analytics
Year: 2025

Title: Enhancing the effectiveness of output projection in wafer fabrication using an Industry 4.0 and XAI approach
Authors: Tin-Chih Toly Chen; Yu-Cheng Wang; Chi-Wei Lin
Journal: The International Journal of Advanced Manufacturing Technology
Year: 2024

Title: Adapted techniques of explainable artificial intelligence for explaining genetic algorithms on the example of job scheduling
Authors: Yu-Cheng Wang; Toly Chen
Journal: Expert Systems with Applications
Year: 2024

Title: Evaluating innovative future robotic applications in manufacturing using a fuzzy collaborative intelligence approach
Authors: Tin-Chih Toly Chen; Yu-Cheng Wang
Journal: The International Journal of Advanced Manufacturing Technology
Year: 2024

Title: A heterogeneous fuzzy collaborative intelligence approach: Air quality monitor selection study
Authors: Tin-Chih Toly Chen; Yu-Cheng Lin; Yu-Cheng Wang
Journal: Applied Soft Computing
Year: 2023

Title: Prediction of engine failure time using principal component analysis, categorical regression tree, and back propagation network
Authors: Yu-Cheng Wang
Journal: Journal of Ambient Intelligence and Humanized Computing
Year: 2023

Title: Improving people’s health by burning low-pollution coal to improve air quality for thermal power generation
Authors: Tin-Chih Toly Chen; Teng Chieh Chang; Yu-Cheng Wang
Journal: Digital Health
Year: 2023

Title: A selectively calibrated derivation technique and generalized fuzzy TOPSIS for semiconductor supply chain localization assessment
Authors: Toly Chen; Yu-Cheng Wang; Pin-Hsien Jiang
Journal: Decision Analytics Journal
Year: 2023

Title: New XAI tools for selecting suitable 3D printing facilities in ubiquitous manufacturing
Authors: Yu-Cheng Wang; Toly Chen
Journal: Complex & Intelligent Systems
Year: 2023

Title: A modified random forest incremental interpretation method for explaining artificial and deep neural networks in cycle time prediction
Authors: Toly Chen; Yu-Cheng Wang
Journal: Decision Analytics Journal
Year: 2023

Title: 3D Printer Selection for Aircraft Component Manufacturing Using a Nonlinear FGM and Dependency-Considered Fuzzy VIKOR Approach
Authors: Yu-Cheng Wang; Tin-Chih Toly Chen; Yu-Cheng Lin
Journal: Aerospace
Year: 2023

Title: An efficient approximating alpha-cut operations approach for deriving fuzzy priorities in fuzzy multi-criterion decision-making
Authors: Tin-Chih Toly Chen; Yu-Cheng Wang; Min-Chi Chiu
Journal: Applied Soft Computing
Year: 2023

Title: A novel auto-weighting deep-learning fuzzy collaborative intelligence approach
Authors: Yu-Cheng Wang; Tin-Chih Toly Chen; Hsin-Chieh Wu
Journal: Decision Analytics Journal
Year: 2023

Title: An explainable deep-learning approach for job cycle time prediction
Authors: Yu-Cheng Wang; Toly Chen; Min-Chi Chiu
Journal: Decision Analytics Journal
Year: 2023

Conclusion

Dr. Yu-Cheng Wang has consistently demonstrated academic excellence and research innovation across aeronautical engineering, explainable AI, and smart manufacturing systems. In publications in leading SCI/Scopus-indexed journals, an h-index of , and more than  citations, his work bridges theory with impactful industrial applications, particularly in semiconductor manufacturing and Industry 4.0 transformations. His leadership as Department Chair, coupled with collaborations with renowned scholars, highlights his influence on both research and education. Recognized for advancing interpretable AI for real-world adoption, Dr. Wang’s contributions embody the spirit of innovation, making him a strong and deserving candidate for the Best Researcher Award.