Pawan Kumar Patidar | Machine Learning | Young Researcher Award

Young Researcher Award

Pawan Kumar Patidar
Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur

Pawan Kumar Patidar
Researcher Pawan Kumar Patidar
Affiliation Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur
Country India
Scopus ID 58175373800
Documents 9
Citations 109
h-index 3
Subject Area Machine Learning
Event International Young Scientist Award
Pawan Kumar Patidar is a researcher affiliated with Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, India, whose research profile is associated with the field of Machine Learning. His scholarly record includes nine documents, 109 citations, and an h-index of 3 according to the supplied research-profile information. The researcher is associated with the International Young Scientist Award, recognizing research activity and contributions within the relevant academic field.[1]

Abstract

Pawan Kumar Patidar is a Machine Learning researcher affiliated with Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur. The supplied scholarly profile records nine documents, 109 citations, and an h-index of 3. These indicators provide a bibliometric overview of research activity and scholarly visibility. The profile is considered in connection with the International Young Scientist Award and its recognition of research-oriented academic contributions.

Keywords

Machine Learning; Artificial Intelligence; Researcher; Scholarly Publications; Bibliometrics; Research Impact; Young Researcher Award; Academic Research.

Introduction

Machine Learning is a major area of contemporary computer science and artificial intelligence, concerned with computational methods that enable systems to identify patterns and improve performance from data. Research in this field spans supervised and unsupervised learning, classification, prediction, optimization, and data-driven decision-making.[2] Within this context, Pawan Kumar Patidar’s profile is associated with Machine Learning research and scholarly publication activity.

Research Profile

The supplied research information identifies Pawan Kumar Patidar with Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, India. The reported Scopus record contains nine documents and 109 citations, with an h-index of 3. These metrics are bibliometric indicators and should be interpreted alongside publication quality, research relevance, originality, and broader scholarly contribution.[1]

Research Contributions

The research profile is positioned within Machine Learning, a field that supports computational approaches to pattern recognition, prediction, classification, and automated analysis. Based on the supplied information, the documented scholarly output demonstrates participation in academic research and dissemination through indexed publications. Specific research contributions should be evaluated from the individual publications and their methodological and application-specific findings rather than from bibliometric measures alone.

Publications

The supplied profile reports nine scholarly documents. Publication records provide evidence of research dissemination and form an important component of academic evaluation. Citation counts can provide an indication of subsequent scholarly use, although citation practices vary considerably among disciplines, publication types, and research communities.[1]

Research Impact

The reported 109 citations and h-index of 3 indicate measurable scholarly visibility within the supplied bibliometric record. Research impact, however, is multidimensional and may also include methodological contribution, reproducibility, practical applications, collaboration, and influence on subsequent research. Bibliometric indicators therefore provide one component of an overall assessment rather than a complete measure of scientific significance.

Award Suitability

The documented research profile is relevant to consideration for the International Young Scientist Award in the Machine Learning area. The combination of an identified institutional affiliation, indexed scholarly documents, citations, and an established h-index provides a research-oriented basis for consideration. Final award suitability should be determined through the applicable nomination, verification, and evaluation procedures of the award program.

Conclusion

Pawan Kumar Patidar is associated with Machine Learning research at Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur. The supplied scholarly indicators comprise nine documents, 109 citations, and an h-index of 3. Collectively, these data establish a documented research profile suitable for academic consideration while emphasizing the importance of evaluating individual research contributions, publication quality, and research relevance alongside quantitative metrics.

References

  1. Elsevier. (n.d.). Scopus author details: Pawan Kumar Patidar, Author ID 58175373800. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=58175373800
  2. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
    https://doi.org/10.1126/science.aaa8415
  3. Google Scholar. (n.d.). Scholar profile associated with Pawan Kumar Patidar.
    https://scholar.google.com/citations?user=kW4v5uIAAAAJ&hl=en&oi=sra

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Nazarbayev University | Kazakhstan

Mr. Yerassyl Saparbekov is a researcher in computer science specializing in deep learning, natural language processing, and computer vision. His work focuses on embedding models, clustering techniques, and retrieval-augmented generation systems for evidence-based analysis. He has contributed to developing advanced AI solutions for student feedback interpretation and decision-support systems. His research also spans medical visual question answering, scene text super-resolution, and multimodal learning approaches. He is actively engaged in advancing applied artificial intelligence for real-world problem solving and intelligent data-driven insights.


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King Fahd University of Petroleum and Minerals | Saudi Arabia

Assist. Prof. Dr. Saad Ezzini is a Computer Science researcher specializing in software engineering, artificial intelligence, large language models, and natural language processing, with a strong focus on AI-driven automation and intelligent software systems. His research addresses key challenges in requirements engineering, ambiguity handling, and data-centric software analysis, with applications across multiple interdisciplinary domains. He has published extensively in leading international journals and top-tier conferences, demonstrating high research quality and relevance. His scholarly work has received 655 citations overall and 616 citations, reflecting strong and sustained research visibility. He holds an h-index of 12, indicating consistent academic impact. He also maintains an i10-index of 16, highlighting the breadth of his influential publications. Overall, his research profile demonstrates significance, impact, and leadership in contemporary AI and software engineering research.

Citation Metrics (Google Scholar)

800

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400

200

0

Citations
655

Documents
12

h-index
12

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Dr. Sharmila More is an accomplished academician and researcher, currently serving as Assistant Professor in the Department of Science and Computer Science at MIT ACSC, Alandi (D), Pune. With over nine years of teaching and administrative experience, along with eight years of dedicated research expertise, she has significantly contributed to the fields of computer science, data science, cyber security, and artificial intelligence. She holds a Ph.D. in Computer Science from MATS University, Raipur, an MCA from Pune University, and a Postgraduate Diploma in Core Competency from Shivaji University. Dr. More has published 18 research papers in reputed journals, presented 20 papers at national and international conferences, and authored a book titled Solving Security Issues in Personal Identification using Fuzzy Approach and Multimodal Images. She has also guided several students in research and academic projects. Her innovations are reflected in multiple patents, including an Indian patent at the FER stage, one design patent, and granted patents in Australia, Germany, and the UK. She is actively engaged in academic committees, curriculum development under the NEP framework, and serves as an editorial board member for journals. Her professional memberships include the Computer Society of India, Indian Science Congress Association, and Soft Computing Research Society. Recognized for her excellence, she has received prestigious honors such as the International Research Excellence Awards for Outstanding Researcher and Distinguished Researcher, Best Teacher Award, and multiple prizes at research competitions. A sought-after resource person and reviewer, she has delivered expert lectures and contributed as co-supervisor and examiner for Ph.D. programs. Her citation index stands at 67, reflecting her impactful scholarship. Dr. More continues to advance interdisciplinary research in computer science, focusing on biometric systems, machine learning, cryptography, and emerging technologies, while inspiring future scholars through teaching, mentorship, and innovation.

Profile: Google Scholar

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More, S. S. (2025). Revolutionizing military operations: The role of deep learning with YOLO v7 in the evolution of drones. Advancements in Intelligent Systems, 33–43. Computing & Intelligent Systems.

More, S. S. (2024). Pythonic learning: Advancements and innovations in machine intelligence. International Journal of Advanced Research in Science, Communication and Technology.

More, S. S. (2023). Statistical and fuzzy inference system analysis of multimodal. Solovyov Studies ISPU.

More, S. S. (2023). Statistical and fuzzy inference system analysis of multimodal images using NMRSA. Solovyov Studies ISPU, 71(11), 122–127.

More, S., Narain, B., & Jadhav, B. T. (2023). Privacy conserving using fuzzy approach and blowfish algorithm for malicious personal identification. In Institute of Engineering (pp. xx–xx). Springer.