Vijay Sangolgi | Deep Learning | Young Researcher Award

Published-on by Indian Scientist Awards

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

Dattaguru Kamath | Circuits | Innovative Research Award

Published-on by Indian Scientist Awards

Innovative Research Award

Researcher Information
Affiliation Manipal Institute of Technology
Country India
Scopus ID 26423069300
Documents 39
Citations 387
h-index 11
Subject Area Circuits
Event Indian Scientist Awards

Dattaguru Kamath

Manipal Institute of Technology

Dattaguru Kamath, affiliated with Manipal Institute of Technology, India. The profile evaluates scholarly output, citation performance, research influence, and academic engagement within the field of Circuits and related electronic engineering domains. The assessment is intended to provide a structured academic perspective on the researcher’s suitability for recognition under the Indian Scientist Awards framework.[1]

Abstract

This article provides an academic overview of the research profile of Dattaguru Kamath, focusing on contributions to the field of circuits, electronic systems, and associated engineering applications. The researcher has established a documented record of scholarly productivity through indexed publications and citation activity. Bibliometric indicators, including publication count, citation volume, and h-index performance, demonstrate consistent engagement with scientific inquiry and knowledge dissemination. The profile further examines the relevance of these contributions within the context of innovation-oriented academic recognition programs.[1][2]

Keywords

Circuits, Electronic Engineering, Research Excellence, Scholarly Impact, Citation Analysis, Engineering Innovation, Scientific Publications, Circuit Design, Academic Recognition, Indian Scientist Awards.

Introduction

The field of circuits forms a foundational component of modern electrical and electronic engineering. Research in this area contributes to advancements in communication systems, embedded technologies, signal processing, semiconductor devices, and intelligent electronic applications. Continuous innovation within circuit design and analysis supports technological development across industrial, commercial, and academic sectors.[3]

Researchers working in this domain play an essential role in improving system performance, reliability, efficiency, and scalability. Through publication, collaboration, and technical dissemination, scholarly contributions support both theoretical advancement and practical implementation within engineering disciplines.[4]

Research Profile

Dattaguru Kamath is associated with Manipal Institute of Technology and has developed a research portfolio characterized by sustained scholarly activity and measurable academic impact. The available bibliometric indicators indicate significant engagement with research, publication, and scientific communication within circuits and related engineering specializations.[1]

  • Scopus Author ID: 26423069300
  • Indexed Documents: 39
  • Total Citations: 387
  • h-index: 11
  • Primary Research Area: Circuits
  • Recognition Platform: Indian Scientist Awards

Research Contributions

The researcher’s scholarly contributions encompass areas associated with circuit design, electronic systems, engineering optimization, and technological innovation. Publications and collaborative studies contribute to the understanding and advancement of engineering methodologies applicable across a variety of electronic and computational environments.[1]

  • Development and analysis of circuit architectures and electronic systems.
  • Contribution to engineering research through peer-reviewed publications.
  • Application of analytical and experimental methodologies to technological challenges.
  • Support for interdisciplinary engineering research and innovation initiatives.
  • Dissemination of scientific knowledge through indexed scholarly literature.

Publications

The publication portfolio demonstrates a sustained commitment to scientific research and academic communication. Indexed publications contribute to the visibility of research findings and support broader engagement with the international engineering community. Citation activity associated with these works reflects their relevance within scholarly discussions and ongoing technological investigations.[1]

  1. Peer-reviewed journal articles in circuit design and electronic engineering.
  2. Conference publications addressing emerging technological developments.
  3. Collaborative engineering research studies published in indexed sources.
  4. Technical contributions supporting innovation in electronic systems.

Research Impact

Research impact is commonly evaluated through scholarly productivity, citation influence, and contributions to scientific advancement. With thirty-nine indexed documents, three hundred eighty-seven citations, and an h-index of eleven, the researcher demonstrates a substantial level of academic engagement and measurable influence within the engineering research community.[1]

Beyond numerical indicators, the broader significance of the research lies in its contribution to engineering knowledge, technological development, and the advancement of circuit-related applications. Such outcomes support continued innovation and facilitate knowledge transfer between academia and industry.[3][4]

Award Suitability

The objectives of the Innovative Research Award emphasize originality, scholarly quality, research productivity, and contribution to scientific progress. Based on documented publication output, citation performance, and sustained engagement within the field of circuits, Dattaguru Kamath demonstrates characteristics that align with the evaluation criteria commonly associated with academic recognition programs.[1]

The combination of indexed publications, measurable citation impact, and continued contribution to engineering research supports consideration for recognition through the Indian Scientist Awards. The researcher’s profile reflects academic dedication and a commitment to advancing knowledge within a technically significant field.[2][4]

Conclusion

Dattaguru Kamath has established a research profile characterized by sustained scholarly productivity, notable citation performance, and contributions to the advancement of circuit engineering research. The available bibliometric indicators demonstrate meaningful academic influence, while the body of published work contributes to ongoing scientific and technological development. These achievements collectively support the researcher’s suitability for consideration under the Innovative Research Award category of the Indian Scientist Awards.[1]

References

  1. Elsevier. (n.d.). Scopus author details: Dattaguru Kamath, Author ID 26423069300. Scopus. https://www.scopus.com/authid/detail.uri?authorId=26423069300
  2. Complementary Metal-Oxide Semiconductor (CMOS) Circuit Realization of Elliptic Low-Pass Filter of Order (1 + α).
    https://www.mdpi.com/2504-3110/10/1/31
  3. Electric vehicle speed tracking control using an ANFIS-based fractional order PID controller. https://www.sciencedirect.com/science/article/pii/S1018363922000010
  4. Novel first-order and second-order current-mode filters using multiple-output operational transconductance amplifiers. https://link.springer.com/article/10.1007/s00034-010-9163-y

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

Published-on by Indian Scientist Awards

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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Banupriya N | Computer Science | Young Researcher Award

Published-on by Indian Scientist Awards

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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Ayshika Kapoor | Computer Science | Women Researcher Award

Published-on by Indian Scientist Awards

Dr. Ayshika Kapoor | Computer Science | Women Researcher Award

Indian Institute of Technology Roorkee,  India

Dr. Ayshika Kapoor is a researcher in Electronics and Communication Engineering with a Ph.D. specializing in privacy-preserving and communication-efficient federated learning. Her work focuses on secure AI, urban sensing systems, and domain-adaptive learning. She has experience as a research scientist, contributing to intelligent mobility and autonomous systems. With multiple high-impact publications, conference presentations, and a granted copyright, she has earned prestigious fellowships and research recognition. Her contributions advance scalable, secure, and efficient AI solutions for real-world applications.

Citation Metrics (Google Scholar)

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15
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Featured Publications


A resource adaptive secure aggregation protocol for federated learning based urban sensing systems


– Joint International Conference on Data Science (Cited by 6 · Year: 2023)


Optimization of user resources in federated learning for urban sensing applications


– Federated Learning for Distributed Data Mining Workshop (Cited by 3 · Year: 2023)

ADRIJIT GOSWAMI | Computer Science | Research Excellence Award

Published-on by Indian Scientist Awards

ADRIJIT GOSWAMI | Computer Science | Research Excellence Award

Indian Institute of Technology Kharagpur India, India

Prof. Dr. Adrijit Goswami is a distinguished academic in Mathematics at the Indian Institute of Technology Kharagpur, specializing in Operations Research and Optimization. He earned his doctorate from Jadavpur University and has extensive teaching and research experience. His interests include supply chain management, fuzzy systems, vehicle routing, cryptography, and data analytics. A recipient of multiple academic honors, he has supervised numerous doctoral scholars and published widely. His work significantly advances mathematical modeling, optimization science, and impactful decision-making research.

Research Metrics (Google Scholar)

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Featured Publications


A secure biometrics-based multi-server authentication protocol using smart cards

V Odelu, AK Das, A Goswami – IEEE Transactions on Information Forensics and Security
Cited by: 489 · Year: 2015


An EOQ model for deteriorating items with linear time-dependent demand rate and shortages under inflation and time discounting

S Bose, A Goswami, KS Chaudhuri – Journal of the Operational Research Society
Cited by: 354 · Year: 1995


An EOQ model for deteriorating items with shortages and a linear trend in demand

A Goswami, KS Chaudhuri – Journal of the Operational Research Society
Cited by: 343 · Year: 1991


Multiobjective transportation problem with interval cost, source and destination parameters

SK Das, A Goswami, SS Alam – European Journal of Operational Research
Cited by: 281 · Year: 1999


A deterministic inventory model for deteriorating items with stock-dependent demand rate

S Pal, A Goswami, KS Chaudhuri – International Journal of Production Economics
Cited by: 269 · Year: 1993

Jeba Sonia J | Artificial Intelligence | Chemistry | Best Academic Researcher Award

Published-on by Indian Scientist Awards

Dr. Jeba Sonia J | Artificial Intelligence | Chemistry | Best Academic Researcher Award

SRM Institute of Science and Technology, India

Dr. Jeba Sonia J is an accomplished academician and researcher in Computer Science and Engineering with extensive experience in teaching, mentoring, and curriculum development across undergraduate, postgraduate, and professional programs. Her academic background includes doctoral and postgraduate training in computer science. Her research interests span artificial intelligence, machine learning, deep learning, data science, computer networks, and wireless communications, with notable contributions through high-impact publications and patents. She has received prestigious fellowships, best paper recognition, and faculty excellence awards, demonstrating sustained excellence in research, teaching, and academic leadership.

Citation Metrics (Google Scholar)

220
160
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209

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Featured Publications


Authentication of biometric system using fingerprint recognition with euclidean distance and neural network classifier


– International Journal of Innovative Technology and Exploring Engineering · Cited by 35


K-means clustering and SVM for plant leaf disease detection and classification


– International Conference on Recent Advances in Energy-efficient Technologies · Cited by 29


Green buildings and sustainable engineering


– Springer Transactions in Civil and Environmental Engineering · Cited by 15

Sajja Tulasi Krishna | Computer Science | Research Excellence Award

Published-on by Indian Scientist Awards

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.

Priyanka Upadhyay | Robust Navigation | Best Researcher Award

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Ms. Priyanka Upadhyay | Robust Navigation | Best Researcher Award

Netaji Subhas University of Technology | India

Priyanka Upadhyay is a researcher in Electrical Engineering with growing contributions in autonomous robotics, intelligent navigation, and control systems. She is pursuing a doctoral degree focused on robust navigation of autonomous robots and holds postgraduate and undergraduate qualifications in Electrical Power Systems and Electrical & Electronics Engineering. Her research record includes publications on hybrid path-planning algorithms, enhanced Bezier-based trajectory modeling, autonomous mobile robot navigation, and control strategies for multi-link manipulators. Her work has gained academic attention, reflected in 16 citations, an h-index of 2, and i10-index of 0, demonstrating steadily increasing scholarly visibility with 8 citations since 2020. She has prior teaching experience as a faculty member in Electrical Engineering and has participated in several conferences, faculty development programs, and short-term courses related to artificial intelligence, optimization, robotics, renewable integration, and power electronics. Her technical expertise is strengthened by hands-on training in tools such as MATLAB and experience in industrial settings. She has earned recognition including a Silver Medal for an NPTEL Robotics certification and reviewer certification from an international robotics journal. Her research interests include electrical machines, robotic navigation, path planning, and intelligent control. She continues to advance her academic profile with a commitment to innovation, learning, and collaborative research.

Profile: Google Scholar

Featured Publications

Upadhyay, P., Rajesh, N., Garg, N., & Singh, A. (2015). Evaluating seed germination monitoring system by application of wireless sensor networks: A survey. In Computational Intelligence in Data Mining—Volume 2: Proceedings of the … (Cited by: 3).

Upadhyay, P., Singh, A., & Garg, N. (2014). Modeling software maintainability and quality assurance in the agile environment. International Journal of Database Theory and Application, 7(3), 83–90. (Cited by: 3).

Singh, P., Upadhyay, P., & Singh, S. K. (2025). Unlocking the economic potential of potato cultivars for mini-tuber production under aeroponic culture. Potato Research, 1–24. (Cited by: 2).

Upadhyay, P., Singh, R., & Rani, A. (2023). Reliable autonomous navigation of mobile robot in unconstrained environment. In 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 531–537). IEEE. (Cited by: 2).

Upadhyay, P., Patil, K. U., Kuber, R., Kulkarni, V., & Singh, A. (2014). Evaluation of hypoxic-ischaemic events in preterm neonates using trans cranial ultrasound. International Journal of Healthcare and Biomedical Research, 3, 67–72. (Cited by: 2).

Subhodeep Moitra | Artificial Intelligence | Young Researcher Award

Published-on by Indian Scientist Awards

Mr. Subhodeep Moitra | Artificial Intelligence | Young Researcher Award

Techno College Hooghly | India

Subhodeep Moitra is a computer science researcher focused on advancing artificial intelligence through the fusion of human-like visual perception and cognition. His academic foundation spans computer applications at both undergraduate and postgraduate levels, where he built strong expertise in machine learning, deep learning, computer vision, neural networks, adversarial robustness, and cognitive modeling. His research explores self-supervised reconstruction, adversarial recovery, AGI-oriented theoretical computing, medical prediction systems, and environmental forecasting, with publications in journals, conferences, preprint platforms, and book chapters. He has contributed to projects ranging from temperature forecasting and brain-stroke detection to adversarially robust autoencoders and AGI theory. His professional experience includes serving as a visiting faculty member, teaching programming, mentoring research projects, and engaging in active collaborative work. His technical skills extend across Python, deep learning frameworks, MERN stack development, and cloud-based AI tools, supported by multiple certifications from NASA, NVIDIA, CERN, IBM, Oracle, and Coursera. He has presented papers at international conferences and earned best paper presentation awards for his contributions in machine learning–driven forecasting and adversarial perception. His long-term research interest lies in building unified AI systems capable of perceiving, reasoning, and adapting with human-inspired intelligence, aiming to push the boundaries of next-generation cognitive AI.

Profile: Google Scholar

Featured Publications

Moitra, S., & Banerjee, D. (n.d.). Robustness as Latent Symmetry: A Theoretical Framework for Semantic Recovery in Deep Learning. OSF.

Moitra, S., & Banerjee, D. (n.d.). Are We Even on the Right Track? A Theoretical Framework for AGI Beyond Classical Computation. Authorea Preprints.

Moitra, S., & Banerjee, D. (n.d.). Skip the Chaos: A Self-Supervised Learning-Powered Autoencoder for Adversarial Recovery. OSF.

Pintu, P., Subhodeep, M., & Deblina, B. (n.d.). The mystery of Neural Network: Linked with quantum mechanics and universe. ResearchGate.

Pal, P., Moitra, S., & Banerjee, D. (n.d.). The mystery of Neural Network: Linked with quantum mechanics and universe.