Chodagam Srinivas | Engineering | Best Research Article Award

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/

Y. Reddy | Computer Science | Innovative Research Award

Innovative Research Award

Y. Reddy
Lakireddy Bali Reddy College of Engineering, India

Y. Reddy
Affiliation Lakireddy Bali Reddy College of Engineering
Country India
Scopus ID 57200293074
Documents 13
Citations 30
h-index 2
Subject area Data Science
Event Indian Scientist Awards
ORCID 0000-0002-8840-3060

Y. Reddy is identified in the supplied information as a researcher affiliated with Lakireddy Bali Reddy College of Engineering in India, with Data Science listed as the principal subject area. The profile includes a Scopus author identifier, an ORCID record, and reported bibliometric indicators. These details provide a basis for reviewing research activity and potential recognition through the Indian Scientist Awards. The information should be checked against authoritative records before formal publication or award consideration. [1] [2]

Abstract

This article presents a concise academic profile of Y. Reddy, whose stated research area is Data Science. The supplied information identifies an institutional affiliation and provides Scopus and ORCID identifiers, alongside reported publication and citation metrics. It outlines considerations for evaluating research contributions and award suitability while distinguishing supplied information from independently verified findings. [1] [2]

Keywords

Y. Reddy; Data Science; academic research; data analytics; computational methods; scholarly publications; bibliometrics; citation analysis; research impact; scientific recognition; Indian Scientist Awards. [1] [3]

 Introduction

Data Science combines statistical analysis, computing, and domain knowledge to extract useful insights from complex datasets. Its applications include predictive modelling, decision support, pattern recognition, and evidence-based planning. Y. Reddy’s supplied profile identifies Data Science as a research area. Evaluating this work requires examining verified studies, methodological rigour, reproducibility, and documented practical relevance. [3]

 Research Profile

Y. Reddy is listed in the supplied information as affiliated with Lakireddy Bali Reddy College of Engineering, India. The profile provides Scopus author ID 57200293074 and ORCID 0000-0002-8840-3060. It reports 13 documents, 30 citations, and an h-index of 2. These indicators should be checked against current database records and matched to the correct author before academic use. [1] [2]

Research Contributions

Data Science research can contribute through improved algorithms, reliable analytical workflows, effective visualisation, and methods for interpreting complex information. Specific contributions by Y. Reddy should be established through verified publications and documented results rather than inferred from subject classification alone. Assessment should consider originality, sound methodology, reproducibility, comparative performance, and potential usefulness in relevant academic or practical settings. [3]

 Publications

The supplied profile reports 13 documents associated with the researcher’s Scopus author record. A comprehensive publication list should verify article titles, author order, journal or conference details, publication dates, and available digital object identifiers. Individual publication titles and DOI numbers were not supplied for this article, so specific papers cannot be reliably identified without checking the linked bibliographic records. [1] [2]

 Research Impact

The supplied profile reports 30 citations and an h-index of 2. These measures offer limited indicators of publication visibility and citation activity, but do not independently demonstrate originality, quality, or societal benefit. Interpretation should account for career stage, publication dates, disciplinary practices, database coverage, and the content of citing works. Documented applications and independent evaluations can provide additional evidence of impact. [1]

 Award Suitability

Suitability for an innovative research award depends on the official eligibility requirements and evaluation criteria established by the organiser. Relevant evidence may include original findings, methodological advances, peer-reviewed publications, research integrity, and demonstrated benefits. The supplied profile provides preliminary bibliometric information but does not establish eligibility, nomination, or selection. A fair assessment requires verified research outputs and confirmation of the award’s current rules. [3]

Conclusion

The supplied information identifies Y. Reddy with Lakireddy Bali Reddy College of Engineering and lists Data Science as the research area. Reported publication and citation indicators offer a starting point for academic review. A credible recognition profile should additionally document individual research contributions, verify bibliographic records, assess research quality, and confirm award criteria before drawing conclusions about suitability or achievement. [1] [2]

References

  1. Elsevier. (n.d.). Scopus author details: Y. Reddy, Author ID 57200293074. Scopus. The author record is provided for bibliographic verification; reported metrics should be checked directly.

    https://www.scopus.com/authid/detail.uri?authorId=57200293074

  2. ORCID. (n.d.). ORCID record: 0000-0002-8840-3060. ORCID provides persistent researcher identifiers and supports links between researchers and scholarly outputs.

    https://orcid.org/0000-0002-8840-3060

  3. Indian Scientist Awards. (n.d.). Official website. Consult current award categories, eligibility rules, nomination requirements, and published results directly with the organiser.

    https://indianscientist.in/

Banupriya N | Computer Science | Young Researcher Award

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

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Rounak Raman | Information Technology | Research Excellence Award

Mr. Rounak Raman | Information Technology | Research Excellence Award

Netaji Subhas University of Technology | India

Rounak Raman is a technology researcher and engineering innovator with a strong academic background in Information Technology and a multidisciplinary portfolio spanning artificial intelligence, machine learning, generative AI, computer vision, IoT systems, wireless sensor networks, and network security. He has gained significant research and development experience through contributions at DRDO-INMAS, where he worked on EEG-based cognitive analytics and neurofeedback systems, and at NSUT, where he designed advanced IoT protocols including energy-aware clustering, trust-based opportunistic communication, and secure hierarchical key-rotation mechanisms. His technical work extends into generative AI solutions, semantic search systems, real-time computer vision applications, and large-scale image and document intelligence models. He has led impactful projects such as PRAGATI smart parking, SyntheX document analysis framework, and QuickTag for AI-driven product taxonomy. His achievements include securing positions in national innovation challenges, hackathons, entrepreneurship competitions, and international fellowship programs. Alongside industry-recognized certifications in data science, system design, and cybersecurity, he has also held leadership roles in student organizations, mentoring teams, guiding open-source contributions, and facilitating research collaboration. His research interests include GenAI, NLP, IoT security, WSNs, cryptography, edge intelligence, and autonomous network optimization. He remains committed to creating scalable, socially impactful technological solutions.

Profiles: Google Scholar

Featured Publication

Raman, R., Yadav, A., Kukreja, D., & Sharma, D. K. (2025). CONTEXT-NET: A context-aware nexus-based aggregation protocol for opportunistic networks. Internet of Things, 101809.

Raman, R., Yadav, A., & Kukreja, D. (2025). ARMor-IoT: Aggregated reliable mechanism for optimized trust in IoT. In International Conference on Artificial Intelligence and Its Application (pp. 229–241).