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

S.S.Subashka Ramesh | Medicinal Chemistry | Best Researcher Award-duplicate-1

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Dr. S.S. Subashka Ramesh | Data Science and Machine learning | Best Researcher Award

SRM Institute of Science and Technology india, India

Dr. S.S. Subashka Ramesh is an accomplished academic and researcher in Computer Science and Engineering with a strong foundation in advanced computing and data-driven technologies. Holding doctoral and postgraduate qualifications, she has extensive teaching and research experience, mentoring scholars and contributing to impactful innovations. Her research interests include machine learning, artificial intelligence, edge computing, and medical imaging. With notable publications, patents, and academic leadership, she has earned professional recognition and continues to advance knowledge through research, collaboration, and technology-driven solutions for real-world challenges.

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Featured Publications

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)

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Featured Publications

Sajja Tulasi Krishna | Computer Science | Research Excellence Award

Published-on by Indian Scientist

Dr. Sajja Tulasi Krishna | Computer Science | Research Excellence Award

Koneru Lakshmaiah Education Foundation | India

Dr. Sajja Tulasi Krishna is a distinguished researcher and academician in Computer Science and Engineering, currently serving as an Assistant Professor at Koneru Lakshmaiah Education Foundation. She has extensive teaching experience in areas including CI/CD, Cloud DevOps, Python Full Stack Development, MERN Stack Web Development, Deep Learning, and Data Structures. Dr. Krishna earned her Ph.D. in Computer Science and Engineering and holds advanced degrees in M.Tech and B.Tech, reflecting a strong academic foundation. Her research focuses on deep learning, machine learning, biomedical image processing, and intelligent systems, with contributions in multi-omics integration, lung cancer detection, COVID-19 diagnosis, and medicinal plant classification. She has published 18 research articles in SCIE, Scopus, and IEEE journals, achieving a total of 488 citations with an h-index of 5 and an i10-index of 5. Dr. Krishna has presented her work at multiple national and international conferences, serving as a reviewer for reputed journals and conferences. She has received multiple awards recognizing her excellence in teaching, research, and technical contributions, including Best Teacher, International Excellence, and Young Researcher Awards. With her expertise, she continues to advance innovative research while mentoring students and contributing to the academic community globally.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

Krishna, S. T., & Kalluri, H. K. (2019). Deep learning and transfer learning approaches for image classification. International Journal of Recent Technology and Engineering (IJRTE), 7(5S4), 427–432.

Sajja, T. K., Devarapalli, R. M., & Kalluri, H. K. (2019). Lung cancer detection based on CT scan images by using deep transfer learning. Traitement du Signal, 36(4), 339–344.

Sajja, T. K., & Kalluri, H. K. (2020). A deep learning method for prediction of cardiovascular disease using convolutional neural network. Revue d’Intelligence Artificielle, 34(5), 601–606.

Sajja, T. K., & Kalluri, H. K. (2021). Image classification using regularized convolutional neural network design with dimensionality reduction modules: RCNN–DRM. Journal of Ambient Intelligence and Humanized Computing, 12(10), 9423–9434.

Sajja, T. K., & Kalluri, H. K. (2019). Gender classification based on face images of local binary pattern using support vector machine and back propagation neural networks. Advances in Modelling and Analysis B, 62(1), 31–35.

Satyamvada Maurya | Computational Biology | Best Researcher Award

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Mrs. Satyamvada Maurya | Computational Biology | Best Researcher Award

ICAR-National Bureau of Fish Genetic Resources | India

Dr. Satyamvada Maurya is a bioinformatics researcher with extensive experience in genomics, computational biology, and drug discovery. She holds a Ph.D. in Bioinformatics, focusing on the design and development of novel anti-tuberculosis agents through computational approaches. Her academic foundation includes a Master’s in Bioinformatics and a Bachelor’s in Biotechnology, which laid the groundwork for her multidisciplinary research. She has worked as a Project Assistant and Young Professional in premier Indian research institutes, contributing to projects in genome analysis, database development, SNP characterization, and functional genomics of aquatic organisms, particularly Clarias magur. Her research interests span next-generation sequencing, genome analysis, protein structure-function prediction, drug design, homology modeling, phylogenetic analysis, and database creation for biological resources. She has authored multiple research articles, book chapters, training manuals, and popular science articles, accumulating 27 citations with an h-index of 3 and i10-index of 1, reflecting the impact of her work. She has received awards including the TEQIP fellowship and best presentation accolades at national and international conferences. Dr. Maurya is committed to advancing bioinformatics applications in both healthcare and aquaculture, bridging computational tools with experimental biology to address complex biological challenges. Her work continues to contribute to the development of innovative solutions in genomics and drug discovery.

Profile: Google Scholar

Featured Publications

Verma, S., Maurya, R., & Maurya, S. (2016). Prediction of gene action and combining ability for yield and quality traits in F1 and F2 generations of wheat (Triticum aestivum L.). Tropical Plant Research, 3(2), 449–459.

Maurya, S., Jain, A., Singh, V., Haque, S., & Mishra, B. N. (2023). Evaluation of Saraca asoca for its anti-tubercular potential via molecular docking and molecular dynamics simulation studies. ChemistrySelect, 8(12), e202204899.

Maurya, S., Alhazmi, A., Vidyarthi, A. S., Jain, A., Singh, V., Khan, F., Haque, S., & Mishra, B. N. (2022). In-silico study reveals potential antitubercular drug targets unique to Mycobacterium tuberculosis H37Rv. Minerva Biotechnology & Biomolecular Research, 34(2), 71–79.

Maurya, S., Kumar, M. S., Kumar, R., & Kushwaha, B. (2024). Role of machine learning and artificial intelligence in transforming aquaculture and fisheries sector. Indian Farming, 74(8), 24–27.

Maurya, S., Jain, A., Rehman, M. T., Hakamy, A., Bantun, F., AlAjmi, M. F., Singh, V., … Mishra, B. N. (2023). In silico identification of novel derivatives of rifampicin targeting Ribonuclease VapC2 of M. tuberculosis H37Rv: Rifampicin derivatives target VapC2 of Mtb H37Rv. Molecules, 28(4), 1652.

Supriya M H | Electronics | Research Excellence Award

Published-on by Indian Scientist

Prof. Dr. Supriya M H | Electronics | Research Excellence Award

Cochin University of Science and Technology | India

Dr. Supriya M.H. is a distinguished Professor in the Department of Electronics at Cochin University of Science and Technology with extensive experience at both undergraduate and postgraduate levels as well as in industry. She holds a B.Tech in Electrical Engineering, M.Tech and Ph.D. in Electronics, a Diploma in Unix & C, and an MBA in Information Technology. With over two decades of teaching experience and active involvement in research, Dr. Supriya has guided multiple Ph.D. students and has been recognized with numerous awards and recognitions for her contributions. Her research interests include underwater acoustics, target identification, underwater communication, signal processing, computer technology, and machine learning, leading to significant outputs in publications and patents. She has led several nationally and internationally funded projects with substantial total outlay, demonstrating her capability in research management and innovation. Dr. Supriya has authored over 178 publications, holds multiple patents, and has been cited over 1,210 times, with an h-index of 15 and i10-index of 25. She has also played key roles in organizing and coordinating symposia, workshops, and training programs, contributing to academic development. Her leadership in academic and professional bodies reflects her commitment to advancing electronics research and mentoring the next generation of researchers.

Profile: Google Scholar

Featured Publications

Pranav, E., Kamal, S., Chandran, C. S., & Supriya, M. H. (2020). Facial emotion recognition using deep convolutional neural network. In Proceedings of the 6th International Conference on Advanced Computing and Communication (pp. …).

Jose, E., Greeshma, M., Haridas, M. T. P., & Supriya, M. H. (2019). Face recognition based surveillance system using FaceNet and MTCNN on Jetson TX2. In Proceedings of the 5th International Conference on Advanced Computing & Communication (pp. …).

Kamal, S., Mohammed, S. K., Pillai, P. R. S., & Supriya, M. H. (2013). Deep learning architectures for underwater target recognition. In Proceedings of Ocean Electronics (SYMPOL) (pp. 48–54).

Nezla, N. A., Haridas, T. P. M., & Supriya, M. H. (2021). Semantic segmentation of underwater images using U-Net architecture based deep convolutional encoder-decoder model. In Proceedings of the 7th International Conference on Advanced Computing and Communication (pp. …).

Sherin, B. M., & Supriya, M. H. (2015). Selection and parameter optimization of SVM kernel function for underwater target classification. In IEEE Underwater Technology (UT) (pp. 1–5).

Vaijayanthimala Jayavel | Artificial Intelligence | Best Researcher Award

Published-on by Indian Scientist

Dr. Vaijayanthimala Jayavel | Artificial Intelligence | Best Researcher Award

Dhirajlal Gandhi College of Technology| India

Dr. J. Vaijayanthimala is a dynamic academic and researcher recognized for her extensive contributions in computer science and engineering, particularly in artificial intelligence, image processing, sensor networks, and intelligent computing systems. Her Google Scholar profile records 16 total citations with an h-index of 2 and i10-index of 0, reflecting her growing scholarly influence across interdisciplinary domains. She has published widely in reputed journals including the ECS Journal of Solid State Science and Technology and Journal of The Electrochemical Society, with research spanning photonic biosensors, AI-based news aggregation, virtual reality accessibility, and smart agriculture. She has co-authored and authored multiple technical books on AI, machine learning, database systems, and data structures, demonstrating her commitment to quality education and knowledge dissemination. Her innovations include patents in automated voice recognition and eco-friendly 3D printing technology. A recipient of the “Innovative Technologist and Dedicated Teaching Professional Award,” she actively contributes as a reviewer for Springer Nature and Elsevier journals. With research interests that merge intelligence, automation, and sustainable technology, Dr. Vaijayanthimala continues to advance computational research and inspire the next generation of scholars.

Profile: Google Scholar

Featured Publications

Vaijayanthimala, J., Pon Bharathi, A., Ramkumar Raja, M., & Arun Kumar, U. (2024). Enhanced sensing of diseased blood samples through one-dimensional MgO-SiO2 photonic crystal sensor. Journal of The Electrochemical Society, 171(10), 107505.

V.M. Manish, J. Vaijayanthimala. (2014). Diminution of packet drop by efficient selection of network route in MANET. International Journal of Computer Science Information Technology (IJCSIT), 5, 1852–1855.

Vaijayanthimala, J., Vaishnavi, K., & Arun Kumar, U. (2025). High-sensitivity terahertz metasensor for cervical cancer diagnosis: Graphene modulation and XGBoost-assisted optimization. Sensors International, 2666–3511, Article 2666.

Vaijayanthimala, J., Alam, M.K., Shqaidef, A., & Mahmoud, O. (2024). Performance evaluation of refractive index biosensor in THz regime for clinical applications: A simulation approach. ECS Journal of Solid State Science and Technology, 13(10), 107005.

Vaijayanthimala, J., & Padma, T. (2019). Synthesis score level fusion based multifarious classifier for multi-biometrics applications. Journal of Medical Imaging and Health Informatics, 9(8), 1673–1680.