Subash Senthil Mohanvel | Predictive Maintenance | Best Researcher Award

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Mr. Subash Senthil Mohanvel | Predictive Maintenance | Best Researcher Award

Subash Senthil Mohanvel at West Pharmaceuticals, United States.

Subash Senthil Mohanvel is a digital manufacturing expert and technology strategist with over two decades of experience in SAP, MES, and Industry 4.0 implementations. As Director of Digital Manufacturing at West Pharmaceutical, he leads enterprise-wide digital transformation initiatives, integrating SAP S/4HANA, IoT, and automation technologies to enhance manufacturing and quality processes. His expertise in solution architecture, predictive maintenance, and shop floor digitization has contributed to significant efficiency improvements and cost savings in industrial operations. Subash is also a thought leader in manufacturing innovation, with research contributions in high-performance teams and predictive maintenance applications.

Publication Profile

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Educational Details

Subash Senthil Mohanvel holds a Master of Engineering in Information Technology from the Royal Melbourne Institute of Technology, Australia, and a Bachelor of Engineering in Computer Science and Engineering from Mepco Schlenk Engineering College, India. He has also obtained multiple certifications, including SAP development on HANA and various industry-recognized credentials from Open SAP.

Professional Experience

With over 22 years of experience, Subash Senthil Mohanvel is a seasoned expert in digital manufacturing, plant maintenance, and enterprise resource planning (ERP) solutions. He currently serves as Director of Digital Manufacturing at West Pharmaceutical, leading the digital transformation of manufacturing and quality operations using SAP S/4HANA, MES, and IoT. Previously, he worked at CertainTeed – Saint-Gobain (2013–2023) as a Senior Manager, where he played a key role in implementing Industry 4.0 solutions, MES systems, SAP MII, and digital shop floor operations. His expertise spans SAP Logistics, Supply Chain, Procurement, and Manufacturing Execution Systems (MES), with a focus on system architecture, solution innovation, and process optimization.

Subash has architected and managed several large-scale SAP and MES implementation projects, driving efficiency through IoT integration, real-time shop floor analytics, predictive maintenance, and digital transformation strategies. He has successfully developed and deployed automated guided vehicles (AGVs), plant maintenance mobile solutions, and energy management systems, contributing to cost savings and operational excellence. His leadership extends to offshore and onshore team management, solution advisory for CIOs and directors, and business case development, including ROI calculation and capital approvals.

Research Interest

Subash’s research interests lie in Industry 4.0, digital manufacturing, predictive maintenance, IoT-enabled shop floor operations, and SAP-driven enterprise solutions. His work focuses on optimizing manufacturing processes, reducing operational costs, and enhancing plant reliability through digital transformation strategies. He has also published papers on high-performance team building and practical applications of predictive maintenance.

Author Metrics

  • Publications: 2 research papers on high-performance team building and predictive maintenance applications

  • Key Contributions: Industry 4.0 applications in predictive maintenance, MES, IoT-enabled manufacturing, and SAP-driven shop floor transformation

  • Recognitions: Awarded for Act Like an Entrepreneur (2021), Best Designer (2016), Professional Commitment (2018), and Cultivate Customer Intimacy (2019) at CertainTeed

Top Noted Publication

  • “Making Predictive Maintenance a Reality”

    • Author: Subash Senthil Mohanvel​

    • Journal: Intelligent Control and Automation​

    • Volume: 16​

    • Issue: 1​

    • Year: 2025

    • DOI: 10.4236/ica.2025.161001​

    • Abstract: This publication aims to bridge the gap between AI advancements and maintenance, specifically focusing on making predictive maintenance a practical application.

  • “How to Build a High-Performance Team”

    • Author: Subash Senthil Mohanvel​

    • Journal: American Journal of Industrial and Business Management​

    • Volume: 14​

    • Issue: 9

    • Pages: 1181-1188​

    • Year: 2024​

    • DOI: 10.4236/ajibm.2024.149061​

    • Abstract: This article aims to help individuals, teams, or anyone in understanding the fundamentals on how to strategize in building a high-performance team.

Conclusion

Mr. Subash Senthil Mohanvel is a strong candidate for the Best Researcher Award due to his extensive industry experience, transformative digital manufacturing innovations, and impactful research in predictive maintenance and high-performance teams. His work bridges theoretical advancements with practical applications, making it highly valuable for industrial and academic progress.

To strengthen his candidacy, he should increase his research output in high-impact journals, engage more in academic collaborations, and expand data-driven empirical studies. However, his deep expertise in Industry 4.0, leadership in digital transformation, and innovative contributions to predictive maintenance make him a deserving nominee for this award.

 

 

Arti Singh | Machine learning | Best Researcher Award

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Mrs. Arti Singh | Machine learning | Best Researcher Award

Assistant Professor at DYPIEMR, India

Mrs. Arti Singh is an accomplished academician and researcher with a robust background in Computer Science, Artificial Intelligence, and Data Science. She is currently serving as an Assistant Professor at Dr. D Y Patil Institute of Engineering Management and Research. With a passion for teaching and research, her expertise lies in machine learning, sentiment analysis, data science, and computational intelligence. Mrs. Singh has presented and published several research papers at national and international conferences. She is committed to continuous learning, having completed various industry-relevant certifications and training programs.

Publication Profile

Google Scholar

Educational Details

  • M.Tech in Computer Technology and Applications from National Institute of Technical Teachers’ Training and Research (RGPV, Bhopal) – 2016 (CGPA: 8.69)
  • B.E. in Computer Science Engineering from Sagar Institute of Research Technology and Science (RGPV, Bhopal) – 2014 (CGPA: 8.35)

Professional Experience

  • Assistant Professor in the Department of Artificial Intelligence and Data Science at Dr. D Y Patil Institute of Engineering Management and Research since July 1, 2022.
  • Lecturer in the Computer Department at Marathwada Mitra Mandal Polytechnic College.
  • Assistant Professor at Sri Sai Shail Manglam College, Singrauli (June 1, 2019, to June 30, 2021).
  • Resource Person for the B.C.A Vocational course at Babasaheb Bhimrao Ambedkar Bihar University, Muzaffarpur (May 30, 2017, to May 27, 2019).

Research Interest

  • Data Science
  • Machine Learning
  • Software Engineering
  • Operating Systems
  • Quantum Artificial Intelligence
  • Pattern Recognition
  • Computational Intelligence

Top Noted Publication

An Opinion Mining for Indian Premier League Using Machine Learning Techniques

  • Authors: KP Dubey, S Agrawal
  • Conference: 2019 4th International Conference on Internet of Things: Smart Innovation, Usage, and Application
  • Pages: 25
  • Year: 2019
  • Summary: This paper presents a sentiment analysis model for social media data related to the Indian Premier League (IPL). The authors employed machine learning techniques to classify public opinions, enabling better understanding of audience engagement and predicting trends in sports sentiment.

Comparing Classification and Regression Tree and Support Vector Machine for Analyzing Sentiments for IPL

  • Author: Arti Singh
  • Journal: International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC)
  • Volume: 4
  • Issue: 6
  • Pages: 172-175
  • Year: 2016
  • ISSN: 2321-8169
  • Summary: This study compares the performance of two machine learning algorithms, Classification and Regression Tree (CART) and Support Vector Machine (SVM), for sentiment analysis on IPL data. The research evaluates the accuracy and effectiveness of both approaches for sports sentiment analysis.

AI Application in Production

  • Author: Arti Singh
  • Publisher: Taylor & Francis
  • Book Title: Industry 4.0: Enabling Technologies and Applications
  • Chapter: AI Application in Production
  • Year: 2024
  • URL: Link to book
  • Summary: This book chapter explores the integration of Artificial Intelligence (AI) in manufacturing and production processes. It highlights AI-driven innovations, predictive maintenance, process optimization, and intelligent automation in modern industrial setups.

Automated Invoice Data Extraction: Advancements and Challenges in OCR-Based Approaches

  • Authors: Arti Singh, Sneha Kanwade, Siddhant Shendge, Amoksh Layane, Kohsheen Tikoo
  • Journal: International Journal of Scientific Research in Engineering and Management (IJSREM)
  • Volume: 8
  • Pages: 1-6
  • ISSN: 2582-3930
  • Year: 2024
  • Summary: This paper addresses the growing need for automated invoice data extraction using Optical Character Recognition (OCR) technologies. It discusses the latest advancements, the challenges faced, and potential solutions to enhance accuracy in invoice processing systems.

An In-Depth Analysis of Sentiment Polarity Using Various Machine Learning Algorithms

  • Author: Arti Singh
  • Conference: 8th International Conference on ISDIA 2024
  • Volume: 1107
  • Pages: 157–167
  • Year: 2024
  • Summary: This research investigates the effectiveness of different machine learning algorithms for sentiment polarity detection. The study evaluates models such as SVM, Random Forest, and Extremely Randomized Trees to improve sentiment classification accuracy in social media data.

Comparative Study of Machine Learning Algorithms for Sentiment Polarity

  • Author: Arti Singh
  • Conference: IRF International Conference
  • Pages: 1-5
  • Year: 2017
  • Summary: The paper compares several machine learning techniques, including Naive Bayes, Decision Trees, and SVM, for sentiment polarity classification. It emphasizes the importance of selecting the appropriate algorithm for accurate sentiment detection in online text data.

Conclusion:

Mrs. Arti Singh is a strong candidate for the Best Researcher Award, given her consistent research output in machine learning and applied AI domains, industry-relevant research contributions, and dedication to academic excellence. Her work bridges the gap between theory and practice, making her a valuable contributor to the field of computational intelligence and Industry 4.0 applications.

With increased focus on high-impact journals, research funding, and industry collaborations, she has the potential to emerge as a leading figure in her field. Therefore, she is highly deserving of recognition through the Best Researcher Award.

 

 

Yousry AbdulAzeem | Artificial Intelligence | Best Researcher Award

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Assoc. Prof. Dr. Yousry AbdulAzeem | Artificial Intelligence | Best Researcher Award

Associate Professor at School of Computational Sciences and Artificial Intelligence (CSAI), Zewail City of Science and Technology, Egypt

Assoc. Prof. Dr. Yousry AbdulAzeem is a computer science researcher and educator specializing in distributed database systems, artificial intelligence, and software performance engineering. With over two decades of academic and industry experience, he has contributed to database optimization, software modeling, and AI-driven data analysis. He is currently affiliated with Misr Higher Institute for Engineering and Technology, University of Science and Technology (Zewail City, Giza, Egypt), and The Higher Institute of Engineering, El-Shorouk Academy, Cairo, Egypt.

Publication Profile

Scopus

Google Scholar

Educational Details

  • Ph.D. in Automatic Control Engineering (2014) – Mansoura University, Egypt
    Thesis: Ranking of Distributed Uncertain Database Systems
  • M.Sc. in Automatic Control Engineering (2009) – Mansoura University, Egypt
    Thesis: Performance Evaluation of UML Software Models Based on LQN
  • B.Sc. in Computers and Systems Engineering (2004) – Mansoura University, Egypt (Very Good)

Professional Experience

Dr. Yousry AbdulAzeem is an Associate Professor of Computer Science and Engineering with extensive academic and industry experience. He is currently a full-time Associate Professor at the Misr Higher Institute for Engineering and Technology, Mansoura, Egypt, and also serves as an Adjunct Associate Professor at both the University of Science and Technology (Zewail City, Giza) and The Higher Institute of Engineering (El-Shorouk Academy, Cairo, Egypt).

From 2015 to 2022, he worked as an Assistant Professor at Taibah University, Saudi Arabia, where he taught courses in network security, programming, database systems, and artificial intelligence. Before this, he held academic roles at Mansoura University and Misr Higher Institute for Computers and Commerce.

In addition to his academic career, Dr. AbdulAzeem has industry experience as a Technical Director and Executive Manager at Trust IT (2007–2009) and a Senior Programmer & Project Administrator at Aflak Masr for Information Technology (2004–2006). He also has significant experience in academic mentoring and quality assurance, having served as the Academic Mentoring Manager at both Taibah University (2017–2022) and Misr Higher Institute for Engineering and Technology (2023–present).

Research Interest

  • Distributed Database Systems
  • Software Performance Engineering
  • Artificial Intelligence & Knowledge Representation
  • Data Governance & Security
  • Cloud Computing & Distributed Systems

Top Noted Publication

“A CNN-based framework for classification of Alzheimer’s disease” – Neural Computing and Applications (2021)

  • Citations: 127
  • Developed a deep learning framework for Alzheimer’s disease classification using CNNs.

“A congestion-aware clustering and routing (CCR) protocol for mitigating congestion in WSN” – IEEE Access (2019)

  • Citations: 61
  • Proposed a congestion-aware routing protocol for wireless sensor networks.

“Human action recognition based on transfer learning approach” – IEEE Access (2021)

  • Citations: 53
  • Applied deep transfer learning for human activity recognition in videos.

“An optimized transfer learning-based approach for automatic diagnosis of COVID-19 from chest X-ray images” – PeerJ Computer Science (2021)

  • Citations: 39
  • Introduced an optimized AI model for COVID-19 detection using X-ray images.

“Classification of breast cancer using a manta-ray foraging optimized transfer learning framework” – PeerJ Computer Science (2022)

  • Citations: 27
  • Developed a bio-inspired optimization technique for breast cancer classification.

Conclusion

Assoc. Prof. Dr. Yousry AbdulAzeem is a highly qualified and impactful researcher in Artificial Intelligence and Distributed Systems. His strong publication record, interdisciplinary expertise, and leadership roles make him a top contender for the Best Researcher Award.

To further strengthen his case, he could increase grant acquisitions, expand international collaborations, and focus on AI deployment in real-world applications. Given his high citation impact, research relevance, and academic leadership, he is a strong candidate deserving recognition.