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
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Citations
151

Documents
10

h-index
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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

Alimul Rajee | Computer Science | Young Scientist Award

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Mr. Alimul Rajee | Computer Science | Young Scientist Award

Mr. Alimul Rajee, Dept. of ICT, Comilla University, Kotbari, Bangladesh

Alimul Rajee is a Lecturer at the Department of Information and Communication Technology, Comilla University. His academic journey includes a stellar performance with a CGPA of 3.69 in his M.Sc. in Information Technology from Jahangirnagar University. Rajee’s research interests span Machine Learning, Data Science, Artificial Intelligence, Cyber Security, and Robotics, with a focus on real-world applications such as traffic accident data analysis and smart waste management. He has contributed significantly to several research projects, and his work has been published in prestigious journals, such as Knowledge-Based Systems and Heliyon. In addition to his research, Rajee is an active educator, mentoring students and supervising projects in areas like IoT and deep learning. His dedication extends beyond the classroom to extracurricular activities, where he has received multiple awards and recognitions, including an international award for his project at Fujitsu Research Institute in Tokyo.

Professional Profile

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Suitability Summary of Young Scientist Awards

Alimul Rajee stands out as an excellent candidate for the Research for Young Scientist Award due to his impressive academic achievements, significant research contributions, and commitment to advancing in the fields of Machine Learning, Data Science, Artificial Intelligence, Cyber Security, and IoT. He has a strong educational background, earning his M.Sc. and B.Sc. with high CGPA rankings from Jahangirnagar University, which reflects his deep knowledge and dedication to his field.

Rajee’s research work is highly commendable, with several publications in reputable, Scopus-indexed journals such as Knowledge-Based Systems and Heliyon, where he has contributed to the development of novel algorithms and methodologies, especially in big data analysis, sentiment analysis, and AI-based applications. His ongoing and completed research projects, including a hybrid smart waste management system and aspect-based sentiment analysis for Bengali text, further showcase his innovative thinking and practical application of emerging technologies to address real-world problems. Additionally, his leadership in supervising over 40 academic projects and his participation in global training programs, like those held at the Fujitsu Research Institute in Japan, illustrate his proactive approach to both learning and teaching.

🎓  Education

Alimul Rajee completed his M.Sc. in Information Technology from Jahangirnagar University, securing a CGPA of 3.69 out of 4, ranking 6th in his batch. Before this, he earned his B.Sc. (Hons.) in the same field, also from Jahangirnagar University, with a CGPA of 3.71, again securing the 6th position. Rajee’s academic excellence dates back to his secondary education, where he achieved the highest CGPA of 5.00 in both his HSC and SSC exams from Govt. Ananadamohan College and Islamnagar Sailampur High School. His continuous pursuit of academic excellence earned him merit-based scholarships throughout his education. His academic prowess has laid a strong foundation for his research and professional career, as he continues to excel in his field with a focus on cutting-edge technologies such as AI and IoT.

💼 Professional Experience

Alimul Rajee’s professional career began as a Junior Data Scientist at Oculin Tech BD Ltd., where he worked from March 2020 to May 2021. He then served as a Senior Officer (ICT) at Sonali Bank PLC for a brief period before becoming a Lecturer at Comilla University in November 2021, where he currently teaches. Rajee’s teaching journey includes roles at Bangladesh University of Business and Technology (BUBT) and Jahangirnagar University (IIT-JU), where he was a Teacher Assistant. His extensive experience also includes supervising over 40 academic projects focused on machine learning, deep learning, and IoT. As an educator, he fosters a positive learning environment, guiding students through complex technical concepts while contributing to the development of innovative research and real-world applications.

🏅  Awards and Recognition

Alimul Rajee’s achievements have been recognized at both national and international levels. He has received several awards, including the UGC Research Grant from Comilla University for consecutive fiscal years, which is a testament to his research capabilities. Rajee’s work has been recognized by prestigious institutions such as Fujitsu Research Institute (FRI) in Tokyo, where his final project won 1st prize. He has also been a reviewer for the International Conference on Embracing Industry 4.0 for Sustainable Business Growth. His consistent academic and research excellence has earned him regular merit-based scholarships and fellowships, such as the National Science & Technology Fellowship from the ICT Division of Bangladesh.

🌍 Research Skills On Computer Science

Alimul Rajee specializes in the application of cutting-edge technologies such as Machine Learning, Artificial Intelligence, Cyber Security, and IoT. His research includes a diverse range of topics like traffic accident data analysis, sentiment analysis of Bengali text, and smart waste management. Rajee has honed his expertise in Data Science and deep learning methods, contributing to several high-impact publications in renowned journals such as Knowledge-Based Systems and Heliyon. His current research projects include Aspect-Category-Opinion-Sentiment Quad Extraction for Bengali Text and a Hybrid Smart Waste Management Technique using Deep Learning and IoT. Rajee’s proficiency in data analysis, algorithm design, and system integration showcases his strong research skills and his commitment to advancing technology for societal benefit.

📖 Publication Top Notes

  • “Aspect-based sentiment analysis for Bengali text using bidirectional encoder representations from transformers (BERT)”
    • Authors: MM Samia, A Rajee, MR Hasan, MO Faruq, PC Paul
    • Citation: International Journal of Advanced Computer Science and Applications, 13(12)
    • Year: 2022
  • “Detecting the provenance of price hike in agri-food supply chain using private Ethereum blockchain network”
    • Authors: MH Sayma, MR Hasan, M Khatun, A Rajee, A Begum
    • Citation: Heliyon, 10(11)
    • Year: 2024
  • “Analyzing depression on social media utilizing machine learning and deep learning methods”
    • Authors: PC Paul, MT Ahmed, MR Hasan, A Rajee, K Sultana
    • Citation: Indian Journal of Computer Science and Engineering, 14(5), 740-746
    • Year: 2023
  • “WFFS—An ensemble feature selection algorithm for heterogeneous traffic accident data analysis”
    • Authors: A Rajee, MS Satu, MZ Abedin, KMA Ali, S Aloteibi, MA Moni
    • Citation: Knowledge-Based Systems, 113089
    • Year: 2025

Ahona Ghosh | Computer Science | Best Researcher Award

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Ms. Ahona Ghosh | Computer Science | Best Researcher Award

👤 Ms. Ahona Ghosh, Maulana Abul Kalam Azad University of Technology, West Bengal, India

Ahona Ghosh is a promising researcher in the field of Computer Science and Engineering with a focus on artificial intelligence, machine learning, and rehabilitation technologies. Currently completing her Ph.D. at Maulana Abul Kalam Azad University of Technology, West Bengal, Ahona has made significant strides in the academic and research community. Her work involves a blend of deep learning, cognitive rehabilitation, and IoT-based systems for improving quality of life. With several publications in prestigious international journals and conferences, she has earned recognition for her contributions to the scientific community. Ahona has been awarded the Best Paper Award for her work on IoT-based waste management and has ranked highly in various competitions like MAKATHON’22. She is passionate about leveraging technology for social good, particularly in healthcare and rehabilitation systems.

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🌟 Ms. Ahona Ghosh, Summary of Suitability

Dr. Ahona Ghosh is an outstanding candidate for the Research for Best Researcher Award, demonstrating exceptional academic accomplishments, innovative research contributions, and consistent excellence throughout her career. Her extensive academic background includes a Ph.D. in Computer Science and Engineering from Maulana Abul Kalam Azad University of Technology, West Bengal, with her pre-submission and viva completed, reflecting her advanced expertise and dedication to her field. She has received accolades for her academic and research endeavors, such as the Best Paper Award for her IoT-based waste management system and the Academic Excellence Award from Brainware University.

Her robust portfolio of research contributions includes an impressive array of international journal articles, conference papers, patents, and book chapters. Dr. Ghosh’s work spans cutting-edge topics such as deep learning, cognitive rehabilitation, IoT applications, and fuzzy systems, addressing societal challenges like healthcare, rehabilitation, and sustainable development.

🎓 Education 

Ahona Ghosh has a strong academic background, with a Ph.D. in Computer Science and Engineering from Maulana Abul Kalam Azad University of Technology (MAKAUT), where she is in the final stages of her thesis submission. She completed her Master of Technology (M.Tech.) in the same field at MAKAUT in 2019, with a CGPA of 8.73. Her Bachelor’s degree in Computer Science and Engineering (B.Tech.) was awarded by Techno India College of Technology in 2017, where she achieved a CGPA of 7.66. Ahona’s early education includes Higher Secondary in Science from Taki House Government Sponsored Girls High School, with an aggregate of 67.4%. She also passed the Madhyamik Pariksha (Class 10) from Duff High School for Girls with a remarkable score of 82.88%. Ahona is also certified in NTA-NET for the years 2018 and 2019.

💼  Professional Experience

Ahona Ghosh has worked extensively in academia and research, focusing on artificial intelligence, IoT, and healthcare applications. Currently, she is a Doctoral Fellow at Maulana Abul Kalam Azad University of Technology (MAKAUT). Her research includes contributions to cognitive rehabilitation using machine learning and EEG-based sensor systems. She has also been involved in various projects concerning IoT-based solutions for healthcare, such as designing smart systems for cognitive rehabilitation and enhancing data-driven rehabilitation methods. In addition, Ahona has been a part of multiple international conferences where she presented papers, co-authored patents, and contributed to the scientific community with impactful research. Her teaching experience includes mentoring undergraduate students and guiding research projects, as well as working on industry collaborations in technology development. Ahona’s expertise in both theoretical and applied aspects of Computer Science has shaped her as a versatile professional in the field.

🏅 Awards and Recognition

Ahona Ghosh has received several accolades for her academic and research achievements. She won the Best Paper Award at the IETE Eastern Zonal Seminar with ISF Congress in 2017 for her paper on “Waste Management System Based on Internet of Things (IoT)”. Her innovative contributions earned her the Academic Excellence Award from Brainware University in January 2020, based on exceptional student feedback. She also achieved 2nd place in the MAKATHON’22 competition organized by MAKAUT’s Innovation Council. Ahona’s recognition extends beyond awards, as she is a prominent figure in academic circles, having presented her research at several prestigious IEEE conferences. Her qualifications include passing the NTA-NET exams in 2018 and 2019, reinforcing her academic prowess. Ahona’s dedication to research and innovation continues to receive recognition, making her an influential presence in her field.

🌍 Research Skills On Computer Science 

Ahona Ghosh has developed a comprehensive set of research skills, particularly in the areas of Artificial Intelligence, Machine Learning, Deep Learning, and Cognitive Rehabilitation. Her expertise extends to using IoT for healthcare applications, including creating systems for rehabilitative therapy and mental health analysis. Ahona is proficient in data analysis, algorithm design, and modeling for both real-time and research-driven applications. Her experience with neural networks, sensor systems, and signal processing further enhances her ability to tackle complex problems. Ahona has contributed to developing innovative frameworks using fuzzy logic, sensor networks, and electroencephalography (EEG) in health-related projects. She excels in academic writing, having published in numerous peer-reviewed journals and international conferences. Additionally, she is well-versed in patent filing, research methodology, and project management, which are crucial in carrying out high-impact scientific work.

📖 Publication Top Notes

Scope of Sentiment Analysis On News Articles Regarding Stock Market and GDP in Struggling Economic Condition
  • Authors: S Biswas, A Ghosh, S Chakraborty, S Roy, R Bose
    Journal: International Journal of Emerging Trends in Engineering Research, 8 (7), 3594
    Citation: 30
    Year: 2020
A Detailed Study on Data Centre Energy Efficiency and Efficient Cooling Techniques
  • Authors: D Mukherjee, S Chakraborty, I Sarkar, A Ghosh, S Roy
    Journal: International Journal of Advanced Trends in Computer Science and Engineering
    Citation: 26
    Year: 2020
Recognition of hand gesture image using deep convolutional neural network
  • Authors: KM Sagayam, AD Andrushia, A Ghosh, O Deperlioglu, AA Elngar
    Journal: International Journal of Image and Graphics, 22 (03), 2140008
    Citation: 22
    Year: 2022
Service aware resource management into cloudlets for data offloading towards IoT
  • Authors: D Guha Roy, B Mahato, A Ghosh, D De
    Journal: Microsystem Technologies, 1-15
    Citation: 21
    Year: 2022
Mathematical modelling for decision making of lockdown during COVID-19
  • Authors: A Ghosh, S Roy, H Mondal, S Biswas, R Bose
    Journal: Applied Intelligence
    Citation: 17
    Year: 2021
Secured Energy-Efficient Routing in Wireless Sensor Networks Using Machine Learning Algorithm: Fundamentals and Applications
  • Authors: A Ghosh, CC Ho, R Bestak
    Journal: Deep Learning Strategies for Security Enhancement in Wireless Sensor Networks
    Citation: 12
    Year: 2020
A survey on Internet-of-Thing applications using electroencephalogram
  • Authors: D Chakraborty, A Ghosh, S Saha
    Book: Emergence of Pharmaceutical Industry Growth with Industrial IoT Approach, 21-47
    Citation: 12
    Year: 2020
Rehabilitation using neighbor-cluster based matching inducing artificial bee colony optimization
  • Authors: S Saha, A Ghosh
    Conference: 2019 IEEE 16th India Council International Conference (INDICON), 1-4
    Citation: 12
    Year: 2019
Dtnma: identifying routing attacks in delay-tolerant network
  • Authors: S Chatterjee, M Nandan, A Ghosh, S Banik
    Book: Cyber Intelligence and Information Retrieval: Proceedings of CIIR 2021, 3-15
    Citation: 11
    Year: 2022
Emotion detection using generative adversarial network
  • Authors: S Das, A Ghosh
    Book: Generative Adversarial Networks and Deep Learning, 165-182
    Citation: 10
    Year: 2023