Ramesh Kumar V | Computer Science | Best Faculty Award

Published on by Academic Excellence

Best Faculty Award

Ramesh Kumar V
C Byregowda Institute of Technology, India

Ramesh Kumar V
Affiliation C Byregowda Institute of Technology
Country India
Scopus ID 57208926798
Documents 3
Citations 22
h-index 2
Subject Area Computer Science
Event International Academic Excellence Awards
ORCID 0000-0003-3226-4986

Ramesh Kumar V is a faculty member affiliated with C Byregowda Institute of Technology, India, whose documented research activity is associated with computer science and computational approaches to electrical and energy-related systems. His indexed publication record includes work on artificial neural networks for electrical load forecasting and converter-based hybrid power systems. The available bibliographic information provides a basis for considering his academic and research contributions in the context of the Best Faculty Award.

Abstract

The academic record of Ramesh Kumar V demonstrates research engagement at the intersection of computer science, artificial neural networks, forecasting, and energy systems. His publications address daily and hourly peak-load forecasting and a bidirectional converter for hybrid power systems. These studies illustrate the application of computational intelligence and power-electronic methods to practical engineering problems. Bibliographic records identify three Scopus-indexed documents, 22 citations, and an h-index of 2 in the supplied profile data.

Keywords

Computer Science; Artificial Neural Networks; Load Forecasting; Energy Systems; Hybrid Power Systems; Power Electronics; Academic Research; Faculty Excellence.

Introduction

Forecasting electricity demand is an important computational problem because reliable predictions can support planning, scheduling, and efficient operation of electrical networks. Ramesh Kumar V’s published studies examine artificial neural-network approaches to peak-load prediction, while his later work considers converter technology within a hybrid power system. The combination of computational modelling and energy applications represents a practical research direction within computer science and engineering.

Research Profile

The supplied Scopus information records Ramesh Kumar V under author ID 57208926798, with three documents, 22 citations, and an h-index of 2. His documented research themes include artificial neural networks, peak-load forecasting, and hybrid power-system conversion. The publication record indicates an applied orientation, connecting computational methods with energy and electrical engineering challenges.

Research Contributions

  • Application of artificial neural networks to daily peak-load forecasting.
  • Development and evaluation of an artificial neural-network model for hourly peak-load forecasting.
  • Research on transformerless bidirectional converter technology for hybrid power systems.

Publications

Transformerless Bidirectional Converter Fed Hybrid Power System (2022), published as a book chapter in Lecture Notes in Electrical Engineering. [1]

Daily Peak Load Forecast Using Artificial Neural Network (2019), published in International Journal of Electrical and Computer Engineering. [2]

Artificial neural network model for hourly peak load forecast (2018), published in International Journal of Energy Economics and Policy, with Scopus record identifier. [3]

Research Impact

The citation record supplied for the researcher indicates measurable scholarly visibility, with 22 citations and an h-index of 2. The publications address applied problems that are relevant to energy-demand prediction and hybrid power-system operation. Such research can contribute to the broader development of data-driven approaches for electrical-system planning and operational analysis.

Award Suitability

The documented publication activity provides evidence relevant to evaluation for a Best Faculty Award, particularly in research-oriented academic contribution. His work demonstrates continuity across computational forecasting and energy-system applications, supported by indexed publications and citation activity. Final award assessment should consider the complete academic record, including teaching quality, institutional service, mentorship, innovation, publications, and independently verified research achievements.

Conclusion

Ramesh Kumar V’s documented profile reflects an applied research focus combining computer science methods with electrical and energy-system problems. His publications on neural-network forecasting and hybrid power conversion provide a coherent basis for academic recognition, subject to comprehensive evaluation of his broader faculty and scholarly record.

References

  1. Springer. (2022). Transformerless Bidirectional Converter Fed Hybrid Power System. Lecture Notes in Electrical Engineering. DOI: 10.1007/978-981-16-3690-5_107.
    https://doi.org/10.1007/978-981-16-3690-5_107
  2. International Journal of Electrical and Computer Engineering. (2019). Daily Peak Load Forecast Using Artificial Neural Network. 9(4), 2256–2263. DOI: 10.11591/ijece.v9i4.pp2256-2263.
    https://doi.org/10.11591/ijece.v9i4.pp2256-2263
  3. International Journal of Energy Economics and Policy. (2018). Artificial neural network model for hourly peak load forecast. Scopus record 2-s2.0-85053004165.
    Scopus record
  4. Elsevier. (n.d.). Scopus author details: Ramesh Kumar V, Author ID 57208926798. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57208926798
  5. ORCID. (n.d.). ORCID record for Ramesh Kumar V.
    https://orcid.org/0000-0003-3226-4986
  6. International Academic Excellence Awards. (n.d.). Academic Excellence Awards.
    https://academicexcellenceawards.com/

Deepika Lokesh | Computer Science | Academic Leadership Award

Published on by Academic Excellence

Academic Leadership Award

Deepika Lokesh
C byregowda Institute of Technology, India

Deepika Lokesh
Affiliation C byregowda Institute of Technology
Country India
Scopus ID 57444272400
Documents 3
Citations 6
h-index 2
Subject Area Computer Science
Event International Academic Excellence Awards

Deepika Lokesh is a researcher in Computer Science affiliated with C byregowda Institute of Technology, India. Her documented research interests include wireless sensor networks, energy-efficient target tracking, routing design, solar-powered mobility infrastructure, and machine-learning-assisted electromagnetic applications. Her publication record demonstrates a multidisciplinary approach connecting network optimization, intelligent systems, sustainable engineering, and emerging communication technologies.

Abstract

Deepika Lokesh’s research portfolio reflects sustained work in computer science with particular attention to wireless sensor networks and energy-aware intelligent systems. Her studies address multi-sensory scheduling, routing efficiency, latency reduction, and target-tracking applications, while later work extends toward solar-based charging infrastructure and machine-learning algorithms for sub-6G applications. These topics are relevant to efficient distributed computing, wireless communication, sustainable engineering, and intelligent network design. [1] [2]

Keywords

Wireless sensor networks, target tracking, energy-efficient routing, low-latency routing, multi-sensory scheduling, solar charging, machine learning, sub-6G applications, computer science, intelligent communication systems.

Introduction

Wireless sensor networks require efficient communication strategies because sensing nodes commonly operate under constrained energy resources. Research on target tracking therefore considers scheduling, routing, latency, and energy consumption as interconnected design factors. Lokesh’s published studies address these challenges through energy-efficient multi-sensory scheduling and routing approaches. [1] [3]

Research Profile

The available publication record indicates a research trajectory centered on computational and engineering problems. The portfolio combines wireless sensor network optimization with applications involving sustainable transportation infrastructure and machine-learning-enabled frequency selective surface design. The Scopus record supplied for this profile reports 3 documents, 6 citations, and an h-index of 2.

Research Contributions

  • Energy-aware multi-sensory scheduling for target tracking in wireless sensor networks. [1]
  • Energy-efficient and low-latency routing designs for target-tracking applications. [2] [3]
  • Design of a solar-based mobile vehicle charging station, linking engineering research with sustainable mobility. [4]
  • Application of machine-learning algorithms to dual-square frequency selective surfaces for sub-6G applications. [5]

Publications

1. “Energy efficient target tracking method for multi-sensory scheduling in wireless sensor networks,” International Journal of Innovative Technology and Exploring Engineering.[1]

2. “Energy Efficient Routing Design for Target Tracking in Wireless Sensor Network,” WSEAS Transactions on Information Science and Applications. [2]

3. “Energy Efficient Low Latency Routing Design for Target Tracking Applications of Wireless Sensor Network,” International Journal of Circuits, Systems and Signal Processing. [3]

4. “Design and Developed of a Solar based mobile vehicle charging station,” Gradiva Review Journal. [4]

5. “Machine learning Algorithms for Enhancement of a Dual Square FSS for Sub 6G Applications,” International Journal of Science Research in Engineering and Management. [5]

Research Impact

The research addresses practical challenges in networked sensing and communication, particularly the need to balance energy consumption, routing performance, latency, and tracking reliability. The additional work on solar charging and machine learning indicates application-oriented research extending beyond a single technical problem. The reported Scopus metrics provide a quantitative snapshot of the indexed research record and should be interpreted alongside publication quality, relevance, and broader scholarly contributions.

Award Suitability

For the International Academic Excellence Awards, the Academic Leadership Award category can be considered in relation to the documented evidence of research activity, publication output, and technical focus. Lokesh’s work presents identifiable contributions in energy-efficient wireless sensor networks and related intelligent engineering applications. The portfolio provides a reasonable scholarly basis for recognition, subject to the award’s formal evaluation criteria and independent verification of submitted credentials.

Conclusion

Deepika Lokesh’s research profile demonstrates work at the intersection of computer science, wireless sensor networks, intelligent routing, sustainable engineering, and machine-learning applications. Her publications document a progression from energy-efficient target tracking toward broader engineering and emerging communication applications. The available bibliographic and citation information provides a concise basis for academic recognition within the International Academic Excellence Awards.

References

  1. Lokesh, Deepika. (n.d.). Energy efficient target tracking method for multi-sensory scheduling in wireless sensor networks. International Journal of Innovative Technology and Exploring Engineering, 9(3), 1638–1644.
    https://doi.org/10.35940/ijitee.c8529.019320
  2. Lokesh, Deepika. (2022). Energy Efficient Routing Design for Target Tracking in Wireless Sensor Network. WSEAS Transactions on Information Science and Applications, 19, 132–137.
    https://doi.org/10.37394/23209.2022.19.13
  3. Lokesh, Deepika. (2022). Energy Efficient Low Latency Routing Design for Target Tracking Applications of Wireless Sensor Network. International Journal of Circuits, Systems and Signal Processing, 16, 1018–1026.
    https://doi.org/10.46300/9106.2022.16.124
  4. Lokesh, Deepika. (2023). Design and Developed of a Solar based mobile vehicle charging station. Gradiva Review Journal, 9(8), 565–568.
  5. Lokesh, Deepika. (2024). Machine learning Algorithms for Enhancement of a Dual Square FSS for Sub 6G Applications. International Journal of Science Research in Engineering and Management, 8(8).
    https://doi.org/10.55041/IJSREM35833
  6. Elsevier. (n.d.). Scopus author details: Deepika Lokesh, Author ID 57444272400. Scopus.
    https://www.scopus.com/pages/authors/57444272400

SYED NAVAZ A S | Computer Science | Research Excellence Award

Published on by Academic Excellence

Dr. SYED NAVAZ A S | Computer Science | Research Excellence Award

Shine & Inspire Academy | India

Dr. A. S. Syed Navaz is an accomplished academician, researcher, and educational leader with over 14 years of teaching experience at both undergraduate and postgraduate levels in the field of Computer Science and Applications. He holds a Ph.D. in Computer Science from Prist University, Thanjavur, where his doctoral research focused on Layer-Based and Flow-Based Channel Assignment in Tree-Structured Wireless Sensor Networks for Fast Data Collection, reflecting his strong expertise in networking and data communication systems. Beyond academia, Dr. Syed Navaz plays prominent leadership roles as Publisher and Chief Editor of the International Organization of Innovative Research & Publishers (IOIRP) and as Managing Director of Shine & Inspire Academy, where he supports research, Ph.D. guidance, publications, patents, entrepreneurship training, and motivational and soft-skill development. He has also successfully mobilized government funding through DST–NSTEDB for multiple Entrepreneurship Awareness Camps, demonstrating his commitment to innovation and societal development. With multidisciplinary expertise spanning education, research, entrepreneurship, blockchain consulting, and life advisory services, Dr. A. S. Syed Navaz continues to make impactful contributions to academic excellence, research advancement, and human capacity building.

 

Citation Metrics (Scopus)

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