Zhuming Cao | Engineering | Research Excellence Award

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Mr. Zhuming Cao | Engineering | Research Excellence Award

Xiamen Institute of Technology | China

Mr. Zhuming Cao, affiliated with Xiamen Institute of Technology, China, is an accomplished researcher in mechanical engineering with expertise in intelligent manufacturing, CNC systems, and digital twin technologies. His recent publication in Machines (2026) highlights advanced predictive maintenance using innovation-adaptive sensor fusion and uncertainty-aware prognostics. With over 60 academic publications, multiple patents, and active leadership in funded research projects, his work significantly advances smart manufacturing and industrial digitalization. He has established strong industry collaborations, contributing to technology transfer and practical innovation. His research demonstrates measurable societal impact through enhanced manufacturing efficiency, workforce skill development, and the integration of academia with industry applications.

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Featured Publications

Ardy Arsyad | Engineering | Research Excellence Award

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Assoc. Prof. Dr. Ardy Arsyad | Engineering | Research Excellence Award

Universitas Hasanuddin | Indonesia

Assoc. Prof. Dr. Ardy Arsyad is an Associate Professor at Hasanuddin University, Indonesia, with expertise in geotechnical earthquake engineering, soil stabilization, and landslide hazard assessment. He has authored numerous Web of Science and Scopus-indexed publications, contributing over 221 citations with an h-index of 7. His research spans liquefaction analysis, sustainable road materials, and advanced modeling of geohazards, including landmark studies on the 2018 Sulawesi earthquake. He collaborates internationally with scholars such as Yasuhiro Mitani. His work supports resilient infrastructure development, environmental sustainability, and disaster risk reduction, delivering significant societal impact in hazard-prone regions.

Citation Metrics (Scopus)

400

300

200

100

0

Citations
221

Documents
58

h-index
7

๐ŸŸฆ Citations ๐ŸŸฅ Documents ๐ŸŸฉ h-index

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 ย ย  ย ย  ย View Google Scholar Profile

Featured Publications

Large distance flow-slide at Jono-Oge due to the 2018 Sulawesi Earthquake, Indonesia.

โ€“ Soils and Foundations, 61(1), 239โ€“255. (2021). Cited By: 65

Large-scale flowslide in Sibalaya caused by the 2018 Sulawesi earthquake.

– Soils and Foundations, 60(4), 1050โ€“1063. (2020). Cited By: 52

Bioremediation of coal contaminated soil as the road foundations layer.

โ€“ International Journal of GEOMATE, 21(84), 76โ€“84. (2021). Cited By: 21

Landslide susceptibility mapping along road corridors in west Sulawesi using GIS-AHP models.

โ€“ IOP Conference Series: Earth and Environmental Science, 419(1), 012080. (2020). Cited By; 18

 

Hadi Gokcen | Engineering | Best Researcher Award

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Prof. Hadi Gokcen | Engineering | Best Researcher Award

Professor | Gazi University Industrial Engineering Department | Turkey

Dr. Hadi Gรถkรงen, 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. Gรถkรงenโ€™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 Scholarย 

Featured Publications

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

2. Demirel, E., Demirel, N., & Gรถkรงen, 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, 2101โ€“2113.
Cited By : 247

3. Gรถkรงen, H., AฤŸpak, K., & Benzer, R. (2006). Balancing of parallel assembly lines. International Journal of Production Economics, 103(2), 600โ€“609.
Cited By : 226

4. Gรถkรงen, H. (2007). Yรถnetim bilgi sistemleri. Ankara: Palme Yayฤฑncฤฑlฤฑk.
Cited By : 217

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

Sakshi Dua | Engineering | Best Researcher Award

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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 :ย  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

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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 Wangย  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 ofย  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

Educationย 

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. Wangโ€™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.

Experienceย 

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. Wangโ€™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 Wangโ€™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 appointmentsย  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 thanย  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. Wangโ€™s contributions embody the spirit of innovation, making him a strong and deserving candidate for the Best Researcher Award.

Ms. Somaye Mohammadi | Vibration Analysis | Best Researcher Award

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Ms. Somaye Mohammadi | Vibration Analysis | Best Researcher Award

Assistant Professor , Sharif University of Technology, Best Researcher Award

Dr. S. Mohammadi is an accomplished mechanical engineer with a strong focus on vibration analysis, acoustics, and machine condition monitoring ๐Ÿ› ๏ธ๐Ÿ”. He earned his Ph.D. from Amirkabir University of Technology, where he specialized in tire/road noise prediction and reduction ๐Ÿ”Š๐Ÿ›ฃ๏ธ. His research spans intelligent fault diagnosis, dynamic balancing, and advanced signal processing ๐Ÿ“Š๐Ÿค–. With a deep commitment to industrial problem-solving and academic excellence, Dr. Mohammadi has published extensively in top-tier journals and conferences ๐Ÿง ๐Ÿ“š. His collaborative work with leading automotive and petrochemical industries demonstrates his practical impact in engineering innovation ๐Ÿš—๐Ÿญ.

Professional Profile

ORCID

Education and Experience

Dr. Mohammadi holds a Ph.D. (2016โ€“2021), M.Sc. (2014โ€“2016), and B.Sc. (2010โ€“2014) in Mechanical Engineering from Amirkabir University of Technology ๐ŸŽ“๐Ÿ‡ฎ๐Ÿ‡ท. His doctoral research focused on modeling and predicting tire/road noise using semi-analytical and statistical methods ๐Ÿ”Š๐Ÿ“ˆ. He has extensive experience in academia and industry, collaborating with IPCO and other companies on dynamic balancing, machine reliability, and condition monitoring โš™๏ธ๐Ÿ—๏ธ. He has published over 25 journal and conference papers and actively participates in technical events and applied engineering research, bridging theory and practice effectively ๐Ÿ“š๐Ÿ› ๏ธ.

Professional Development

Dr. Mohammadi has significantly contributed to professional development in mechanical engineering through active involvement in research, publications, and conferences ๐ŸŽค๐Ÿ“„. He has attended numerous national and international events such as CMFD, ISAV, and IRNDT, presenting cutting-edge research on condition monitoring, acoustic diagnostics, and vibration analysis ๐Ÿ”๐Ÿง . He continuously updates his skills in AI, machine learning, and signal processing for predictive maintenance and fault detection ๐Ÿค–๐Ÿ“Š. His multidisciplinary approach enables practical solutions for complex industrial problems, making him a valuable contributor to academic and engineering communities ๐ŸŒ๐Ÿ”ง.

Research Focus

Dr. Mohammadi’sย  research centers on mechanical vibrations, acoustics, and intelligent fault detection using AI and signal processing ๐Ÿง ๐Ÿ”Š. His work addresses real-world engineering challenges like tire noise reduction, gearbox diagnostics, and turbine reliability โš™๏ธ๐Ÿญ. He combines statistical methods with machine learning to predict failures and optimize performance in rotating machinery, engines, and industrial systems ๐Ÿค–๐Ÿ”ง. His interdisciplinary expertise bridges mechanical design, acoustics, and data analytics to improve machinery health monitoring and performance efficiency ๐Ÿ“‰๐Ÿ“ˆ. His research supports sustainable and cost-effective engineering operations ๐Ÿ”„๐Ÿ’ก.

Awards and Honors

Dr. Mohammadi has received multiple recognitions for his research excellence and technical contributions ๐ŸŽ–๏ธ๐Ÿ“š. He has been invited to present at prestigious conferences like CMFD, ISAV, and IRNDT and collaborated with top engineers and institutions on vibration and fault diagnosis projects ๐Ÿค๐Ÿ”. His publications in high-impact journals such as Applied Acoustics, Journal of Vibration and Control, and Machines have earned critical acclaim from the academic community ๐ŸŒŸ๐Ÿ“ฐ. He was also involved in award-supported industrial collaborations, including projects with IPCO and petrochemical companies, showcasing practical impact and innovation ๐Ÿญ๐Ÿ….

Publication Top Notes

1.๐Ÿ” Intelligent Diagnosis of Rolling Element Bearings Under Various Operating Conditions Using an Enhanced Envelope Technique and Transfer Learning
๐Ÿ“… Published: April 2025 โ€“ Machines

๐Ÿ“Œ DOI: 10.3390/machines13050351

๐Ÿ‘ฅ Co-authors: Ali Davoodabadi, Mehdi Behzad, Hesam Addin Arghand, Len Gelman

๐Ÿง  Key Contribution: Developed an innovative technique combining advanced signal processing (enhanced envelope detection) with transfer learning, significantly improving fault diagnosis accuracy across variable operating conditions in rolling bearings. This paper bridges AI and mechanical reliability โ€“ a cutting-edge intersection in engineering diagnostics.

2.๐Ÿ“Š A Comprehensive Study on Statistical Prediction and Reduction of Tire/Road Noise
๐Ÿ“… Published: October 2022 โ€“ Journal of Vibration and Control

๐Ÿ“Œ DOI: 10.1177/10775463211013184

๐Ÿ‘ฅ Co-authors: Abdolreza Ohadi, Mostafa Irannejad-Parizi

๐Ÿง  Key Contribution: Offers a data-driven, statistical framework for predicting and mitigating tire/road interaction noise, addressing environmental and comfort challenges in vehicle design. The study integrates modeling, statistical methods, and experimental validation, making it valuable for the automotive industry.

3.๐Ÿ”‰ Effect of Modeling Sidewalls on Tire Vibration and Noise

๐Ÿ“… Published: September 2022 โ€“ Journal of Automobile Engineering (IMechE Part D)

๐Ÿ“Œ DOI: 10.1177/09544070211052368

๐Ÿ‘ฅ Co-author: Abdolreza Ohad

๐Ÿง  Key Contribution: Investigated how sidewall modeling precision influences vibrational behavior and noise in tires. The research advanced numerical tire modeling techniques, which are essential for designing quieter, more stable vehicles.

Conclusion

Dr. Mohammadi’s blend of deep theoretical knowledge, strong publication output, practical industrial applications, and multidisciplinary research makes him a standout researcher. His work addresses real-world engineering challenges with smart solutions, reinforcing his eligibility for the Best Researcher Award. He not only contributes to advancing scientific understanding but also to improving industrial reliability and performance โ€” hallmarks of a truly impactful researcher ๐Ÿ…๐Ÿš€.