G.V.Sriramakrishnan

About

Dr. G. V. Srriamakrishnan received the Ph.D. degree in Computer Science and Engineering from St. Peter's University, Chennai, India. He is currently a Professor with the Department of Computer Science and Engineering. His research interests encompass a broad spectrum of areas, including Machine Learning, Deep Learning, Artificial Intelligence, and Networks. Over the years, he has been actively involved in academic research, contributing to advancements in intelligent computing systems and network optimization. Throughout his academic and research career, he has worked on various interdisciplinary projects that integrate computational intelligence with real-world applications. His work focuses on developing novel machine learning models, deep learning architectures, and artificial intelligence techniques to solve complex problems in data science and network systems. In addition to his research contributions, he has mentored numerous undergraduate, postgraduate, and doctoral students, guiding them in their academic pursuits and research endeavors. His expertise in the field has led to collaborations with fellow researchers and institutions, furthering the development of cutting-edge technologies in artificial intelligence and networked systems.

Employment

Employment history is unavailable.

Education

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Projects & Funding

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Publications (41)

• Managing the AI Period's Confluence of Security and Morality
  2024 DOI: 10.4018/979-8-3693-2643-5.ch003
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• DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI
  Network: Computation in Neural Systems 2024 DOI: 10.1080/0954898x.2024.2351159
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• Chronological pelican remora optimization-enabled deep learning for detection of autism spectrum disorder
  Signal, Image and Video Processing 2024 DOI: 10.1007/s11760-023-02741-6
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• Analyzing the differences between SVM and Naive Bayes for Feature Extraction
  2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023 2023 DOI: 10.1109/ICACITE57410.2023.10183068
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• Autism spectrum disorder detection using brain MRI image enabled deep learning with hybrid sewing training optimization
  Signal, Image and Video Processing 2023 DOI: 10.1007/s11760-023-02630-y
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• Compared to The Threshold Dependent Segmentation Technique, Normal Type Lung Cancer Identification in CT Images Utilizing Structural Flood Segmentation Process to Enhance Efficiency
  2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023 2023 DOI: 10.1109/ICACITE57410.2023.10183196
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• ExpHBA Deep-IoT: Exponential Honey Badger Optimized Deep Learning For Breast Cancer Detection in IoT Healthcare System
  Journal of Digital Imaging 2023 DOI: 10.1007/s10278-023-00878-x
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• IoT Enabled Soil Moisture and Heat Level Prediction Using Chimp Shuffled Shepherd Optimization-Based Deep LSTM for Plant Health Monitoring
  International Journal of Information Technology and Decision Making 2023 DOI: 10.1142/S0219622023500311
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• Rabble Based Autonomous Assistance Using Machine Learning Algorithms
  2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023 2023 DOI: 10.1109/ICACITE57410.2023.10182830
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• Feedback Artificial Tree-Anti Corona Virus Optimization enabled deep learning for detecting autism spectrum disorder
  Expert Systems 2022 DOI: 10.1111/exsy.13040
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