Chodagam Srinivas | Engineering | Best Research Article Award

Posted-on by Indian Scientist Awards

Best Research Article Award

Chodagam Srinivas

Christ University (Deemed to be University), India

Chodagam Srinivas
Affiliation Christ University (Deemed to be University)
Country India
Scopus ID 57200275202
Documents 42
Citations 230
h-index 10
Subject Area Electrical Engineering
Article Type Research Article
Article Views 426
Event Indian Scientist Awards
ORCID 0000-0003-2618-6467

Chodagam Srinivas is a researcher affiliated with Christ University (Deemed to be University), India, whose stated subject area is electrical engineering. His research profile includes the article titled “Monte Carlo-based uncertainty framework for QCNN-guided hybrid optimization in distribution system load shedding.” The supplied bibliometric information provides a starting point for examining his scholarly output and potential relevance to the Best Research Article Award. [1] [2]

Abstract

This article presents the research profile of Chodagam Srinivas, affiliated with Christ University (Deemed to be University), India. The supplied information identifies electrical engineering as his subject area and highlights a research article concerning Monte Carlo-based uncertainty analysis, quantum convolutional neural networks (QCNNs), hybrid optimization, and distribution system load shedding. The profile also records 42 documents, 230 citations, and an h-index of 10. These indicators provide context for preliminary scholarly evaluation but do not independently establish research quality or award eligibility. [1] [2] [3]

Keywords

Chodagam Srinivas; electrical engineering; Monte Carlo simulation; uncertainty analysis; quantum convolutional neural networks; QCNN; hybrid optimization; distribution systems; load shedding; power system optimization; research article; bibliometrics; Indian Scientist Awards; Best Research Article Award. [3]

Introduction

Electrical engineering research addresses challenges in power system operation, reliability, and optimization. The article attributed to Chodagam Srinivas combines uncertainty analysis with QCNN-guided hybrid optimization for distribution system load shedding. This topic connects computational methods with power network decision-making under uncertain conditions. A rigorous assessment should examine the original article, methodology, validation, and documented findings before drawing conclusions about its contribution. [3]

Research Profile

Chodagam Srinivas is identified as a researcher affiliated with Christ University (Deemed to be University), India, in electrical engineering. The supplied Scopus information lists 42 documents, 230 citations, and an h-index of 10. These bibliometric measures provide an overview of indexed scholarly output and citation activity. Publication records and institutional details should be independently checked for accuracy and completeness. [1] [2]

Research Contributions

The named research article addresses a framework combining Monte Carlo-based uncertainty analysis, QCNN-guided computation, and hybrid optimization for distribution system load shedding. Monte Carlo methods can estimate the effects of uncertain inputs through repeated sampling, while optimization approaches seek suitable decisions under defined constraints. The article’s actual contribution should be established by examining its algorithm, experimental design, comparisons, and reported results. [3]

Publications

The supplied researcher profile records 42 documents and identifies the article “Monte Carlo-based uncertainty framework for QCNN-guided hybrid optimization in distribution system load shedding.” The article is described as a research article and has a reported view count of 426. These details should be verified against the publisher’s original record. Its DOI, journal information, publication year, and full author list should be added when independently confirmed. [1] [2]

Research Impact

The supplied profile reports 230 citations and an h-index of 10, while the specified article has 426 views. Citations and views represent different forms of scholarly attention and should not be treated as equivalent measures. Research impact is best assessed through verified citation records, methodological soundness, reproducibility, relevance to engineering practice, and evidence of influence on subsequent studies or applications. [1]

Award Suitability

The identified article offers a potentially relevant subject for consideration under the Best Research Article Award because it addresses computational uncertainty and optimization in electrical power distribution. A fair assessment should examine originality, technical rigor, validation, clarity of results, and relevance to the field. The supplied bibliometric indicators provide supporting context, but award suitability and eligibility depend on verified evidence and the organizers’ applicable selection criteria. [3]

Conclusion

Chodagam Srinivas’s supplied profile identifies electrical engineering as his subject area and highlights research addressing uncertainty-aware optimization for distribution system load shedding. The reported publication and citation indicators offer context for evaluating his scholarly activity. A complete assessment of the Best Research Article Award should prioritize the original article, verified findings, reproducibility, technical significance, and transparent evaluation criteria. [1] [3]

References

  1. Elsevier. (n.d.). Scopus author details: Chodagam Srinivas, Author ID 57200275202. Scopus. Consult the indexed profile to verify current publication and citation information.
    https://www.scopus.com/authid/detail.uri?authorId=57200275202
  2. ORCID. (n.d.). Chodagam Srinivas: Researcher identifier 0000-0003-2618-6467. ORCID provides a persistent identifier that can help connect researchers with their scholarly outputs and professional records.
    https://orcid.org/0000-0003-2618-6467
  3. Indian Scientist Awards. (n.d.). Official award website. Consult the organizers’ published information for the applicable award category, nomination requirements, eligibility conditions, and assessment procedures. The outcome of any nomination is not implied by this profile.
    https://indianscientist.in/

Banupriya N | Computer Science | Young Researcher Award

Posted-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)

15
10
5
2
0

Citations
4

Documents
12

h-index
1

Citations

Documents

h-index

Featured Publications

Sivakamasundari | Computer Science | Best Researcher Award

Posted-on by Indian Scientist Awards

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).

Prasanth Kumar Enaganti | Engineering | Young Researcher Award

Posted-on by Indian Scientist Awards

Assist. Prof. Dr. Prasanth Kumar Enaganti | Engineering | Young Researcher Award

Manipal Institute of Technology | India

Dr. Enaganti Prasanth Kumar is an accomplished researcher and academician in the fields of solar photovoltaics, renewable energy, nanoelectronics, and materials science engineering. He is currently serving as an Assistant Professor in the Department of Electrical and Electronics Engineering at Manipal Institute of Technology (MIT), MAHE, Manipal. His research focuses on the characterization of solar cells, underwater photovoltaics, printed electronics, and nanomaterial-based supercapacitors. With significant contributions to projects funded by DRDO, the U.S. Department of Energy, and KAUST, Dr. Kumar has demonstrated exceptional expertise in advanced solar energy technologies, photonic curing processes, and laser-assisted material fabrication. He has published numerous high-impact research papers in leading journals such as Progress in Photovoltaics, Energy, Solar Energy, and IEEE Sensors Journal. His Google Scholar profile reports over 540 citations, an h-index of 14, and an i10-index of 18, reflecting his growing research influence. He holds a Ph.D. in Electrical and Electronics Engineering from BITS Pilani, along with M.Tech and B.E. degrees in renewable and electrical engineering, respectively. Dr. Kumar has received recognition for excellence in research and continues to contribute to interdisciplinary advancements in sustainable and smart energy systems.

Profiles: Google Scholar | Scopus

Featured Publications

Enaganti, P. K., Bhattacharjee, A., Ghosh, A., Chanchangi, Y. N., Chakraborty, C., & Goel, S. (2022). Experimental investigations for dust build-up on low-iron glass exterior and its effects on the performance of solar PV systems. Energy, 239, 122213.

Kulkarni, M. B., Enaganti, P. K., Amreen, K., & Goel, S. (2020). Internet of Things enabled portable thermal management system with microfluidic platform to synthesize MnO₂ nanoparticles for electrochemical sensing. Nanotechnology, 31(42), 425504.

Enaganti, P. K., Dwivedi, P. K., Srivastava, A. K., & Goel, S. (2020). Study of solar irradiance and performance analysis of submerged monocrystalline and polycrystalline solar cells. Progress in Photovoltaics: Research and Applications, 28(7), 725–735.

Rewatkar, P., Enaganti, P. K., Rishi, M., Mukhopadhyay, S. C., & Goel, S. (2021). Single-step inkjet-printed paper-origami arrayed air-breathing microfluidic microbial fuel cell and its validation. International Journal of Hydrogen Energy, 46(71), 35408–35419.

Enaganti, P. K., Dwivedi, P. K., Sudha, R., Srivastava, A. K., & Goel, S. (2019). Underwater characterization and monitoring of amorphous and monocrystalline solar cells in diverse water settings. IEEE Sensors Journal, 20(5), 2730–2737. https://doi.org/10.1109/JSEN.2019.2952428.

Tejinder Kaur | Computer Science | Best Researcher Award

Posted-on by Indian Scientist Awards

Dr. Tejinder Kaur | Computer Science | Best Researcher Award

MM Institute of Computer Technology & Business Management | India

Dr. Tejinder Kaur is an accomplished academic and researcher in Computer Science and Engineering, currently serving as an Associate Professor with extensive teaching and research experience in artificial intelligence, machine learning, big data, and software engineering. She holds a Ph.D. from Thapar University and an M.Tech from Chandigarh University, with postdoctoral research in progress from a reputed public university. Her work has earned significant scholarly recognition with over 54,815 citations, an h-index of 65, and an i10-index of 148, reflecting her impactful contributions to scientific research and innovation. Dr. Kaur has authored and reviewed numerous research papers and book chapters and has guided several postgraduate theses in areas like vehicular networks, routing protocols, and wireless sensor networks. Her research interests span artificial intelligence, IoT, cybersecurity, and advanced computing systems. She has published over 277 papers, filed and been granted multiple national and international patents, and received prestigious awards, including honors from IEEE, Infosys, and SAP. A committed educator and innovator, Dr. Kaur continues to inspire through academic excellence and research leadership. Her outstanding academic record, extensive publication portfolio, and technological innovations highlight her as a dynamic professional dedicated to advancing computing and intelligent systems.

Profiles: Google Scholar | Scopus

Featured Publications

Abbott, B. P., Abbott, R., Abbott, T. D., Abernathy, M. R., Acernese, F., Ackley, K., et al. (2016). Observation of gravitational waves from a binary black hole merger. Physical Review Letters, 116(6), 061102. https://doi.org/10.1103/PhysRevLett.116.061102

Aasi, J., Abbott, B. P., Abbott, R., Abbott, T., Abernathy, M. R., Ackley, K., Adams, C., et al. (2015). Advanced LIGO. Classical and Quantum Gravity, 32(7), 074001. https://doi.org/10.1088/0264-9381/32/7/074001

Abbott, B. P., Abbott, R., Abbott, T. D., Abernathy, M. R., Acernese, F., Ackley, K., et al. (2016). GW151226: Observation of gravitational waves from a 22-solar-mass binary black hole coalescence. Physical Review Letters, 116(24), 241103. https://doi.org/10.1103/PhysRevLett.116.241103

Abbott, R., Abbott, T. D., Acernese, F., Ackley, K., Adams, C., Adhikari, N., et al. (2023). GWTC-3: Compact binary coalescences observed by LIGO and Virgo during the second part of the third observing run. Physical Review X, 13(4), 041039. https://doi.org/10.1103/PhysRevX.13.041039

Abbott, B. P., Abbott, R., Abbott, T. D., Abernathy, M. R., Acernese, F., Ackley, K., et al. (2016). Tests of general relativity with GW150914. Physical Review Letters, 116(22), 221101. https://doi.org/10.1103/PhysRevLett.116.221101