Vignesh D | Machine Learning | Best Researcher Award

Published on by Technology Scientists

Best Researcher Award

Vignesh D
Chennai Institute of Technology, India

                               Vignesh D
Affiliation Chennai Institute of Technology
Country India
Scopus ID 57369745400
Documents 22
Citations 289
h-index 10
Subject Area Machine Learning
Event Technology Scientists Awards
ORCID 0000-0003-2681-9551

Vignesh D is an academic researcher affiliated with Chennai Institute of Technology, India, whose scholarly work spans machine learning, computational modeling, advanced materials, sensor technologies, and sustainable engineering applications. His publication record demonstrates interdisciplinary engagement with theoretical and experimental methodologies, contributing to scientific understanding and technological innovation in emerging research domains.[1]

Abstract

Vignesh D has established a research profile characterized by interdisciplinary investigations in machine learning, advanced functional materials, sensing technologies, photocatalysis, and thermoelectric systems. His scholarly output integrates experimental techniques with computational approaches, including density functional theory, to address scientific and engineering challenges. Through publications in recognized journals, he has contributed to material design, energy-efficient technologies, environmental remediation, and intelligent analytical methodologies. The combination of academic productivity, citation impact, and collaborative research activity reflects sustained engagement with contemporary technological advancements and demonstrates meaningful contributions to applied scientific research and innovation.[1]

Keywords

Machine Learning, Advanced Materials, Density Functional Theory, Photocatalysis, Gas Sensors, Thermoelectric Devices, Nanotechnology, Computational Modeling, Environmental Engineering, Energy Materials, Material Characterization, Sensor Technology.

Introduction

The research activities of Vignesh D focus on integrating computational analysis, material engineering, and intelligent technologies to solve practical scientific problems. His work demonstrates a balanced approach between theoretical investigation and experimental validation, contributing to advancements in sensing systems, energy materials, environmental applications, and technology-driven innovation across multidisciplinary research environments.[2]

Research Profile

With 22 indexed publications, 289 citations, and an h-index of 10, Vignesh D has developed a research profile reflecting consistent scholarly productivity. His investigations encompass machine learning applications, nanostructured materials, photocatalytic systems, gas sensing technologies, and thermoelectric materials, demonstrating expertise in both computational modeling and experimental scientific methodologies.[1]

Research Contributions

His contributions include developing advanced material systems for ethanol sensing, investigating thermoelectric compounds for low-temperature energy applications, and enhancing photocatalytic degradation processes through graphene-based nanostructures. By combining density functional theory with laboratory experimentation, his studies provide insights into material behavior, performance optimization, and technological applicability across diverse engineering domains.[2][3]

Publications

Notable publications authored or co-authored by Vignesh D include investigations on mesoporous niobium-doped vanadium oxide sensors, thermoelectric homojunction materials based on silver bismuth selenide compounds, and graphene-decorated cobalt ferrite photocatalysts. These studies demonstrate interdisciplinary engagement with materials science, computational chemistry, environmental engineering, and emerging technology applications.[2][3][4]

Research Impact

The impact of his research is reflected through citation performance, academic visibility, and relevance to current technological challenges. His studies contribute to improving sensor efficiency, sustainable energy technologies, and environmental remediation strategies. The integration of theoretical simulations with experimental validation enhances the reliability and practical significance of his scientific findings.[1]

Award Suitability

Vignesh D demonstrates attributes commonly associated with recipients of academic research recognition, including sustained publication output, measurable citation impact, interdisciplinary collaboration, and contributions to technology-oriented scientific advancement. His work aligns with the objectives of the Technology Scientists Awards by promoting innovation, knowledge generation, and practical applications supporting scientific and societal development.[1]

Conclusion

The academic achievements of Vignesh D illustrate a commitment to advancing scientific understanding through interdisciplinary research and technological innovation. His contributions to sensing technologies, energy materials, computational studies, and environmental applications have strengthened his scholarly profile. Continued research activity is expected to further support developments in emerging scientific and engineering fields.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Vignesh D, Author ID 57369745400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57369745400
  2. Vignesh, D., et al. (2024). Synthesis of mesoporous Nb-doped V2O5 for ethanol detection – experimental and DFT studies. Surface Interfaces.
    https://www.scopus.com/pages/publications/105017778091
  3. Vignesh, D., et al. (2023). Efficient low temperature homojunction exploring into Ag(Bi,X)Se2 (X=Sn, Sb and Pb) compounds for thermoelectric devices. Materials Science Publication.
    https://www.scopus.com/pages/publications/85215577683
  4. Vignesh, D., et al. (2024). Graphene-decorated magnetic cobalt ferrite for effective UV-accelerated photocatalytic degradation of methylene blue: experimental and theoretical insights by DFT. Advanced Materials Research.
    https://www.scopus.com/pages/publications/105001073658

Annarosa Scalcione | Machine learning | Research Excellence Award

Published on by Technology Scientists

Dr. Annarosa Scalcione | Machine learning | Research Excellence Award

Politecnico di Torino | Italy

Dr. Annarosa Scalcione is an early-career biomedical engineer at Politecnico di Torino with research expertise in medical image analysis, radiomics, and artificial intelligence–based diagnostic support systems. Her work focuses on end-to-end radiomic frameworks for the automated classification and three-dimensional visualization of vertebral lesions, aiming to enhance accuracy, reproducibility, and clinical interpretability in spinal and musculoskeletal imaging. She is the co-author of a peer-reviewed journal article published in Engineering (MDPI), reflecting her contribution to interdisciplinary research at the intersection of biomedical engineering, computer vision, and clinical imaging. Her research activities involve collaboration with multidisciplinary teams of engineers, clinicians, and imaging experts, underscoring a strong capacity for cooperative scientific work. While at an early stage of her academic career, her research demonstrates clear translational and societal impact by supporting improved diagnostic workflows, facilitating data-driven clinical decision-making, and contributing to the advancement of intelligent healthcare technologies with potential benefits for patient outcomes.


View ORCID Profile

Featured Publication

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.

Mohammad Amin | Gradient boosting | Young Scientist Award

Published on by Technology Scientists

Mr. Mohammad Amin | Gradient boosting | Young Scientist Award

Mohammad Amin | RWTH Aachen University | Germany

Mr. Mohammad Yazdi is a distinguished researcher and academic with expertise in information technology, e-learning systems, and research data management. He holds advanced degrees in information systems and has built a robust academic and professional profile through his work in integrating IT solutions for collaborative research, developing interactive e-learning media, and enabling process mining for research data life cycles. Professionally, he has contributed to several high-impact projects, including the implementation of web services for integrated government systems, evaluation of FAIR data management architectures, and the orchestration of research cluster collaborations, demonstrating strong technical and leadership skills. His research focuses on e-learning as an interactive IT-based learning medium, process mining, IT resource management in research projects, and operational support systems, with a publication record spanning reputable conferences and journals. His works, including “E-learning sebagai media pembelajaran interaktif berbasis teknologi informasi,” “How to Manage IT Resources in Research Projects? Towards a Collaborative Scientific Integration Environment,” and “Event Log Abstraction in Client-Server Applications,” have collectively garnered significant citations, underscoring their academic impact. He has been recognized for his scholarly contributions and actively participates in academic dissemination through editorial roles and conference presentations. With a commitment to advancing digital transformation in education and research, Mohammad Yazdi stands out as a thought leader and innovator in his field, with 29 citations across 23 documents, 10 publications, and an h-index of 4

Profile: Google Scholar | Scopus | ORCID

Featured Publications

1. M. Yazdi*, E-learning sebagai media pembelajaran interaktif berbasis teknologi informasi. Foristek, 2012, 2(1), 680.

2. M. Yazdi*, Implementasi Web-Service pada Sistem Pelayanan Perijinan Terpadu Satu Atap di Pemerintah Kota Palu. Seminar Nasional Teknologi Informasi & Komunikasi Terapan, 2012, 450–457, 24.

3. M. Politze, F. Claus, B. Brenger, M.A. Yazdi*, B. Heinrichs, A. Schwarz, How to Manage IT Resources in Research Projects? Towards a Collaborative Scientific Integration Environment. 2020, 22.

4. M.A. Yazdi*, P. Farhadi Ghalati, B. Heinrichs, Event Log Abstraction in Client-Server Applications. 13th Int. Conf. Knowledge Discovery and Information Systems, 2021, 13.

5. M.A. Yazdi*, Enabling Operational Support in the Research Data Life Cycle. Proc. 1st Int. Conf. Process Mining – Doctoral Consortium, 2019, 13.