Vijay Sangolgi | Deep Learning | Young Researcher Award

Published-on by Indian Scientist

Young Researcher Award

Vijay Sangolgi
Affiliation N. K. Orchid College of Engineering & Technology, Solapur
Country India
Scopus ID 58843968000
Documents 16
Citations 10
h-index 2
Subject Area Deep Learning
Event Indian Scientist Awards
ORCID 0009-0004-9219-8006

Vijay Sangolgi

N. K. Orchid College of Engineering & Technology, Solapur,

 

Vijay Sangolgi is affiliated with N. K. Orchid College of Engineering & Technology, Solapur, India. His scholarly activities are associated with the field of Deep Learning, where he has contributed to emerging computational methodologies, intelligent systems, and applied artificial intelligence research. Through academic publications and research dissemination, he has established a growing presence within the scientific community and demonstrates the characteristics commonly recognized under young researcher excellence initiatives.[1]

Abstract

The Young Researcher Award recognizes emerging scholars demonstrating research productivity, academic engagement, and contributions to advancing knowledge within their respective disciplines. Vijay Sangolgi’s academic profile reflects involvement in Deep Learning research, supported by peer-reviewed publications, scholarly dissemination, and measurable citation activity. The combination of publication output, interdisciplinary relevance, and commitment to technological advancement provides a basis for consideration within research recognition frameworks focused on early-career achievement.[1][2]

Keywords

Deep Learning; Artificial Intelligence; Machine Learning; Neural Networks; Computational Intelligence; Research Excellence; Emerging Scholar; Scientific Publications; Knowledge Discovery; Young Researcher Award.

Introduction

Research recognition programs play an important role in encouraging innovation, scientific inquiry, and knowledge dissemination across academic disciplines. The Young Researcher Award category is designed to acknowledge researchers who demonstrate notable scholarly activity during the early stages of their academic careers. Within the rapidly evolving field of Deep Learning, contributions involving predictive modeling, intelligent systems, and data-driven decision making continue to influence both theoretical and practical advancements.[2]

Research Profile

Vijay Sangolgi is associated with N. K. Orchid College of Engineering & Technology, Solapur, India. His documented scholarly record includes sixteen indexed documents, ten citations, and an h-index of two according to available research metrics. His academic interests are centered on Deep Learning and related computational methodologies that support intelligent automation and advanced analytical systems.[1]

  • Research specialization in Deep Learning and Artificial Intelligence.
  • Participation in scholarly publication and dissemination activities.
  • Contribution to computational and intelligent system research.
  • Engagement with interdisciplinary technological applications.

Research Contributions

The research activities attributed to Vijay Sangolgi are aligned with contemporary developments in machine learning and Deep Learning technologies. Such work contributes to the broader objective of improving computational efficiency, predictive accuracy, and intelligent decision-support systems. The field continues to generate significant academic and industrial interest due to its applications in healthcare, engineering, business analytics, and automation.[3]

  • Application of machine learning methodologies to complex datasets.
  • Exploration of intelligent computational models.
  • Support for innovation through algorithmic research.
  • Contribution to academic knowledge exchange and scholarly communication.

Publications

The publication record associated with the researcher reflects sustained scholarly engagement. Indexed publications contribute to scientific visibility, facilitate peer evaluation, and promote the dissemination of research findings across the academic community.[1]

  1. Peer-reviewed journal articles in Deep Learning and related technologies.
  2. Conference proceedings and technical research presentations.
  3. Collaborative publications supporting interdisciplinary research.
  4. Scholarly outputs contributing to emerging AI applications.

Research Impact

Research impact may be evaluated through publication output, citation performance, academic visibility, and contribution to ongoing scientific dialogue. The documented citation record demonstrates that the research outputs have received scholarly attention and contribute to the broader exchange of scientific knowledge. Continued publication activity has the potential to expand influence across academic and applied research domains.[1]

Award Suitability

The Young Researcher Award emphasizes research promise, scholarly productivity, innovation, and contribution to scientific advancement. Based on available academic indicators, Vijay Sangolgi demonstrates characteristics associated with emerging research excellence, including publication activity, engagement with contemporary technological research, and participation in scholarly communication. These factors support consideration within award programs recognizing early-career academic achievement.[1][4]

Conclusion

Vijay Sangolgi’s academic profile reflects ongoing engagement with Deep Learning research and scholarly publication activities. Through contributions to scientific literature and participation in advancing computational intelligence methodologies, the researcher represents the type of emerging scholar frequently recognized by early-career research distinction programs. The Young Researcher Award serves as a platform for acknowledging such contributions and encouraging future scientific achievement.[1]

References

  1. Scopus author details: Vijay Sangolgi, Author ID 58843968000. Scopus. https://www.scopus.com/authid/detail.uri?authorId=58843968000
  2. Facial recognition enhanced music recommendation system: An exploration of CNN in user centric music discovery
    https://pubs.aip.org/aip/acp/article-abstract/3385/1/030001/3380727/Facial-recognition-enhanced-music-recommendation?redirectedFrom=fulltext
  3. Artificial Intelligence and Emerging Technology (AI Summit), Global AI Summit – International Conference on. https://ieeexplore.ieee.org/xpl/conhome/11410557/proceeding
  4. Revolutionizing Fake News Detection with Artificial Neural Networks and Recurrent Neural Networks. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5091615

Banupriya N | Computer Science | Young Researcher Award

Published-on by Indian Scientist

Dr. Banupriya N | Computer Science | Young Researcher Award

R.M.K. Engineering College, India

Dr. N. Banupriya is an accomplished academician and researcher in Computer Science and Engineering, currently serving as an Assistant Professor at R.M.K. Engineering College. She holds advanced degrees in Computer Science and is pursuing doctoral research, reflecting her commitment to academic excellence. With extensive teaching experience, she has contributed significantly to research in Artificial Intelligence, Machine Learning, Data Analytics, and Brain-Computer Interface. She has published in reputed journals and conferences and received recognitions including Infosys Campus Connect certifications. Her work demonstrates dedication to innovation, impactful research, and academic leadership.

Citation Metrics (Scopus)

15
10
5
2
0

Citations
4

Documents
12

h-index
1

Citations

Documents

h-index

Featured Publications

Sivakamasundari | Computer Science | Best Researcher Award

Published-on by Indian Scientist

Ms. Sivakamasundari | Computer Science | Best Researcher Award

SRM Institute of Science and Technology | India

Ms. P. Sivakamasundari is a dedicated academic and researcher in Computer Science and Engineering, recognized for her contributions to deep learning-based medical image analysis. With qualifications spanning Diploma, Bachelor’s, and Master’s degrees in Computer Science and Engineering, she is currently pursuing her Ph.D. at SRM Institute of Science and Technology. She has extensive teaching experience as an Assistant Professor for more than a decade, during which she has guided students in core computing subjects including algorithms, computation theory, compiler design, and image classification. Her research focuses on advanced deep learning frameworks for healthcare applications, particularly diabetic retinopathy and diabetic foot ulcer detection, resulting in book chapters, conference publications, and journal manuscripts under review. She has published and filed patents related to medical imaging and automated disease detection systems, demonstrating her innovation-driven approach. Her scholarly presence includes 1 citation, 1 h-index, and 0 i10-index, indicating emerging research visibility. She has completed multiple professional certifications and participated in workshops, FDPs, and internships in machine learning, biometrics, accelerated computing, and high-performance healthcare analytics. Her work reflects strong commitment toward applying AI for societal benefit, and she continues to advance her expertise through active research and academic contributions.

Profile: Google Scholar

Featured Publications

Sivakamasundari, P., Anandhi, S., Kumaran, A. A., Vijayakumar, K., Birnica, Y. J., & others. (2024). Early detection of glaucoma utilizing retinal nerve fiber layer (RNFL) investigation. International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems.

Sivakamasundari, P., & Niranjana, G. (2025). An automatic detection and classification of diabetic foot ulcers using Chebyshev chaotic ladybug beetle optimized extended Swin Transformer–InceptionV3 model. Biomedical Signal Processing and Control, 110, 108268.

Gomathi, G., Sumathy, V., Sivakamasundari, P., & Deepa, R. (2024). A various approaches of machine learning algorithms for kidney disease prediction. International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems.

Sivakamasundari, P., & Niranjana, G. (2024). Diabetic foot ulcer classification using deep learning approach. International Conference on Computer, Communication and Signal Processing (ICCCSP).

Sivakamasundari, P., & Niranjana, G. (2023). A critique on deep learning methodologies employed for the identification of diabetic retinopathy using fundus images. Intelligent Computing and Control for Engineering and Business Systems (ICCEBS).

Sunil Datt Sharma | Computer Science | Best Researcher Award

Published-on by Indian Scientist

Dr. Sunil Datt Sharma | Computer Science | Best Researcher Award

Central University of Jammu | India

Dr. Sunil Datt Sharma is a distinguished researcher in the fields of Digital Signal Processing, Adaptive Signal Processing, and Machine Learning Applications, recognized for his contributions across computational biology, biomedical signal analysis, and intelligent imaging systems. With 289 citations, an h-index of 9, and 9 indexed documents, his research is widely acknowledged for its technical depth and interdisciplinary impact. He has authored numerous journal articles, conference papers, and book chapters covering areas such as CpG island detection, promoter identification using deep learning, image de-noising, transfer learning for fault diagnosis, micro-Doppler signature analysis, anisotropic diffusion models, and advanced frequency-domain algorithms. His academic background encompasses strong training in electronics, computing, and signal processing, complemented by extensive experience in teaching, research, and scholarly reviewing for reputed international journals. His research interests span computational genomics, machine learning-based biomedical systems, pattern recognition, and intelligent signal analysis. He has been actively engaged in professional peer-review activities for more than twenty journals, reflecting his standing within the global research community. His work integrates innovative algorithms with real-world applications, contributing to both theoretical advancement and practical solutions. Dr. Sharma continues to advance cutting-edge research aimed at addressing complex challenges across science and engineering.

Profile: Google Scholar

Featured Publications

Sharma, S. D., Shakya, K., & Sharma, S. N. (2011). Evaluation of DNA mapping schemes for exon detection. In 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET). (Cited by: 42).

Sharma, S., Sharma, S. N., & Saxena, R. (2020). Identification of short exons disunited by a short intron in eukaryotic DNA regions. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(5). (Cited by: 33).

Sharma, S. D., Saxena, R., & Sharma, S. N. (2015). Identification of microsatellites in DNA using adaptive S-transform. IEEE Journal of Biomedical and Health Informatics, 19(3), 1097–1105. (Cited by: 23).

Garg, P., & Sharma, S. (2020). Identification of CpG islands in DNA sequences using short-time Fourier transform. Interdisciplinary Sciences: Computational Life Sciences, 12(3), 355–367. (Cited by: 19).

Sharma, S. D., Saxena, R., Sharma, S. N., & Singh, A. K. (2015). Short tandem repeats detection in DNA sequences using modified S-transform. International Journal of Advances in Engineering and Technology, 8(2). (Cited by: 16).