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

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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

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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)

160
80
40
10
0

Citations
151

Documents
10

h-index
6

Citations

Documents

h-index


View Scopus Profile
  View Google Scholar Profile

Featured Publications

Entropy Based Anomaly Detection System to Prevent DDoS Attacks in Cloud
– International Journal of Computer Applications, 2013
Data Visualization: Enhancing Big Data More Adaptable and Valuable
– International Journal of Applied Engineering Research, 2016
Face Recognition Using Principal Component Analysis and Neural Networks
– International Journal of Computer Networking, Wireless and Mobile Computing, 2013
Human Resource Management System
– IOSR Journal of Computer Engineering, 2013
Flow Based Layer Selection Algorithm for Data Collection in Tree Structure Wireless Sensor Networks
– International Journal of Applied Engineering Research, 2016

Kohila R | Computer Science | Best Scholar Award

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Mrs. Kohila R | Computer Science | Best Scholar Award

Muthayammal Engineering College | India

R. Kohila is a dedicated academician and researcher with strong expertise in deep learning, artificial intelligence, IoT-based systems, and advanced computing technologies. Her work spans innovative research, impactful publications, and contributions to emerging domains that integrate AI with real-world applications. She has presented her research at several reputed international conferences, covering domains such as energy-efficient wireless sensor networks, industrial IoT analytics, VoIP forensics, and AI-powered crop disease detection using edge devices and YOLOv5 classification. These works reflect her commitment to solving practical challenges through intelligent and scalable technological solutions. Kohila’s inventive mindset is evident in her portfolio of published patents, which include autonomous assistive devices for senior citizens, blockchain-based healthcare systems, AI-driven precision farming solutions, and cloud-based recovery forecasting models for post-surgery rehabilitation. She has also authored books on Cyber Forensics (2022), Deep Learning (2023), and Artificial Intelligence (2024), strengthening her role as a contributor to academic learning resources. Her professional development is enhanced by numerous certifications, including NPTEL, NITTT technical modules, full stack development, DevOps, and advanced research methodology. She has actively participated in specialized Faculty Development Programs on Microsoft Azure AI, IoT applications, cloud infrastructure, generative AI, deep learning, and AWS-based web solutions. Kohila’s excellence has been recognized through prestigious honors such as the University First Rank and Gold Medal from Periyar University, the Women Academic Achiever Award, Unity Champions Excellence Award, and multiple accolades for mentoring and academic performance. Driven by passion, innovation, and academic leadership, she continues to contribute meaningfully to the fields of AI, deep learning, and smart technologies.

Profile: Google Scholar

Featured Publications

1. Jovith, A. A., Ranganathan, C. S., Priya, S., Vijayakumar, R., Kohila, R., & Prakash, S. (2024). Industrial IoT sensor networks and cloud analytics for monitoring equipment insights and operational data. In Proceedings of the 10th International Conference on Communication and Signal Processing (ICCSP 2024).

2. Kohila, R. (2014). Efficient resource management mechanism with fault tolerant model for computational grids.
International Journal of Computer Applications Technology and Research, 3(12), 1–X.

3. Yeluripati Bala Tripura Sundari, K. N. V., Kohila, R., Sowmiya, M., Nagarjuna. (2025). Crop disease detection using edge IoT devices and YOLOv5-based classification. In Proceedings of the 3rd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS 2025).

4. Hemavathi, S., Ganesan, J., Geetha, G., Kumar, K., & Kohila, R. (2023). Energy proficient and dependable cluster routing in wireless sensor network. In Proceedings of the International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics.

5. Kohila, R., Mohanasabari, M. M., Boopathi, K., & Dharunraj, A. (2022). Big data analytics in cyber security.
Journal of Engineering Technologies and Innovative Research, 9(12), 4–X.

Fengrui Hao | Computer Science | Best Researcher Award

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Dr. Fengrui Hao | Computer Science | Best Researcher Award

Jinan University, China

Dr. Fengrui Hao is an emerging researcher in the field of computer science, currently pursuing his Ph.D. in Cyber Security at the School of Information Science and Technology, Jinan University, Guangzhou, China. He holds a B.S. degree in Information Management and Information Systems and an M.S. degree in Computer Technology from Guilin University of Electronic Technology, which laid the foundation for his deep engagement with advanced computing and security research. His primary focus lies in adversarial machine learning and trustworthy artificial intelligence, where he has made significant contributions to strengthening AI systems against vulnerabilities and ensuring fairness, transparency, and robustness in their applications. With more than ten publications in prestigious journals and conferences such as IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Industrial Informatics (TII), and IEEE Transactions on Knowledge and Data Engineering (TKDE), Dr. Hao has established himself as a promising scholar. His research contributions include the development of novel attack and defense mechanisms, fairness-aware algorithms, and privacy-preserving techniques for graph data publishing, all of which are shaping the foundation of secure and ethical AI. His work has been recognized through two completed or ongoing research projects, one consultancy project, and an impressive record of sixteen patents under process. With a citation index of twenty, his influence in the field continues to expand as he pursues innovative research directions. Dr. Hao’s dedication to advancing adversarial learning and trustworthy AI reflects his vision of creating resilient, secure, and human-centered AI technologies for the future.

Profiles: Scopus | Orcid

Featured Publications

  • FBA: Fairness Backdoor Attack on Graph Neural Networks – IEEE Transactions on Dependable and Secure Computing, 2025, DOI: 10.1109/TDSC.2025.3563668

  • dK-DGDP: A Differential Privacy Approach on Directed Social Network Graphs – Computers & Security, 2025, DOI: 10.1016/j.cose.2025.104647

  • GCPA: GAN-Based Collusive Poisoning Attack in Federated Recommender Systems – IEEE Transactions on Knowledge and Data Engineering, 2025, DOI: 10.1109/TKDE.2025.3579807

  • CBAs: Character-level Backdoor Attacks against Chinese Pre-trained Language Models – ACM Transactions on Privacy and Security, 2024, DOI: 10.1145/3678007

  • Collusive Model Poisoning Attack in Decentralized Federated Learning – IEEE Transactions on Industrial Informatics, 2024, DOI: 10.1109/TII.2023.3342901

 

Arivumalar Ravichandran | Computer science | Academic Excellence Award

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Dr. Arivumalar Ravichandran | Computer science | Academic Excellence Award

Dr. Arivumalar Ravichandran | Computer science | GreatLakes Institute of management | India

Dr. Arivumalar Ravichandran is an accomplished academician and researcher with an interdisciplinary background encompassing Information Technology, Computer Science, Engineering, and Human Resource Management. With over 17 years of teaching and research experience, she has held pivotal roles in prestigious institutions including Great Lakes Institute of Management, Sri Sairam Engineering College, and PRIST University. Her academic pursuit culminated in a Ph.D. in Techno-Management, expected to be conferred in 2025. Dr. Ravichandran’s work bridges computer science innovation with pragmatic management principles, enriching both technical and managerial education. Her research primarily targets IoT in agriculture, cloud-based smart campuses, cybersecurity, and logistics optimization. She is widely published in IEEE Xplore and international journals and known for translating theory into practice through her progressive teaching and research approach. Her dedication to both engineering and management education continues to inspire the next generation of data-driven, technology-enabled professionals.

Author Profile

Google Scholar

Education

Dr. Arivumalar Ravichandran’s educational journey reflects her diverse and rich academic expertise. She began with an M.Sc. in Information Technology from A.D.M. College for Women, followed by an M.Phil. in Computer Science from Periyar University, both with First Class distinction. To deepen her technical capabilities, she pursued an M.Tech in Computer Science and Engineering from PRIST University. Demonstrating her interdisciplinary vision, she obtained an M.B.A. in Human Resource Management from Bharathidasan University, blending technological acumen with managerial skills. Currently, she is a Ph.D. scholar in Techno-Management at Dr. N.G.P Institute of Technology, Coimbatore, with completion anticipated in 2025. This comprehensive academic background enables her to explore computer science from both an engineering and organizational perspective, making her uniquely suited for research that involves smart technology deployment in business and societal contexts.

Experience 

Dr. Arivumalar Ravichandran’s career spans over 17 years across leading academic institutions in India. She currently serves as Assistant Professor in Analytics & Operations at Great Lakes Institute of Management (since January 2024). She previously held dual roles in Sri Sairam Engineering College and SRM Valliammai Engineering College, teaching both CSBS and MBA programs. Her foundational experience includes five years as Assistant Professor in CSE at P.R. Engineering College and earlier academic roles at ARJ College, S.K. College of Arts & Science, and RDB College. Her career trajectory reflects an interdisciplinary footprint across Computer Science, MCA, and Management departments. She has a proven record of mentoring students, leading IT programs, and integrating research with curriculum delivery. As a department head and senior faculty, she has contributed to shaping institutional academic strategies while also engaging in publication-worthy research that aligns with industry and technology trends.

Awards and Honors

Dr. Arivumalar Ravichandran has consistently demonstrated excellence in research, academia, and leadership, earning her accolades in each institution she served. Though formal award titles are not explicitly listed, her career reflects significant recognitions in the form of trusted appointments in interdisciplinary teaching roles and departmental leadership. Her successful publication in prestigious Scopus-indexed and IEEE Xplore conferences and journals stands as a testament to her scholarly impact. Additionally, she has presented at international conferences and contributed to critical discourse in areas such as IoT in agriculture and risk management in logistics. These achievements mark her as a respected scholar and mentor in both technical and management circles. Her elevation to Assistant Professor roles across diverse departments and her long-standing service history are indicative of institutional recognition and peer trust. Her work continues to gain traction in the broader academic community.

Research Focus

Dr. Arivumalar Ravichandran’s research is rooted in addressing real-world challenges through advanced computing technologies. Her interdisciplinary focus spans IoT, cloud computing, cybersecurity, AI-driven smart campuses, and risk analysis in logistics. One of her prominent works involves developing a hybrid data acquisition model for precision agriculture using IoT, showcased at the ICOEI 2023 conference. She also investigates the role of cloud computing in building smart campuses, highlighting scalable solutions for educational transformation. Her earlier work focused on cyber threats, specifically mitigating malicious scripting via content security policies. Moreover, she explores techno-managerial topics such as global transportation risk management—blending IT expertise with operational strategy. This blend of computer science and business intelligence forms the core of her research philosophy: leveraging technology for sustainable, secure, and efficient solutions. Her ongoing Ph.D. enhances this integrative approach, promising further contributions at the intersection of computing, analytics, and enterprise systems.

Publication Titles 

  1. A Hybrid Data Acquisition Model for Precision Agriculture using IoT – IEEE Xplore, ICOEI 2023

  2. Analysis of Developing IoT and Cloud Computing Based Smart Campuses and its Applications – IEEE ACCAI 2024

  3. A Study on Risk Management of Global Transportation Service – Research Journal of Humanities and Social Sciences, 2023

  4. Mitigating Malicious Scripting Attacks with a Content Security Policy – IJARTET, July 2017

Conclusion

Dr. Arivumalar Ravichandran stands as a transformative figure in computer science education and research, integrating cutting-edge technical knowledge with human-centric solutions. With her strong academic background, robust publication record, and diverse teaching experience, she is a deserving candidate for the Computer Science Award. Her work continues to make a significant impact in academia and applied research, particularly in areas like IoT, smart systems, and security, reflecting both innovation and practical relevance.