S Dinesh Krishnan

@bvrit.ac.in

Assistant Professor and Computer Science and Engineering
B V Raju Institute of Technology, Narsapur

S Dinesh Krishnan


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https://researchid.co/dineshtec

RESEARCH, TEACHING, or OTHER INTERESTS

Engineering
12

Scopus Publications

18

Scholar Citations

2

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • A novel optimized hybrid deep learning model for intrusion detection in software defined internet of things (SDIoT) environment
    A. Daniel, S. Dinesh Krishnan, Dyagala Naga Sudha, Balamurugan Balusamy, Anand Nayyar
    Cluster Computing, 2025
  • An algorithm for heterogeneous wireless network connections for user preferences and services
    S. Dinesh Krishnan, A. Daniel, S. Ayyasamy, Balamurugan Balusamy, Shitharth Selvarajan, Taher Al-Shehari, Nasser A. Alsadhan
    Scientific Reports, 2025
    Heterogeneous wireless networks (HWNs) present a challenge in selecting the optimal network for user devices due to the overlapping availability of multiple networks. In order to help users choose the best HWN connection, this research is trying to build a decision-making framework that takes user preferences and network performance characteristics into account. Using a multi-attribute decision-making (MADM) method that incorporates fuzzy logic and the Fuzzy Analytic Hierarchy Process (FAHP), our goal is to improve the decision-making process for network selection. The suggested system takes into account a number of network metrics, including latency, jitter, bandwidth, and cost, and uses user preferences to determine the relative importance of each to guarantee a tailored and adaptable recommendation. Our results demonstrate that the algorithm greatly enhances the efficiency of network selection and the level of user happiness, with UMTS being the best option for conversational services, WiMAX being the best for streaming, and LTE being the best for interactive services. Through the incorporation of user-centric decision-making into the network selection process, this research enhances adaptive wireless communication systems, leading to better user experience and network efficiency.
  • Energy optimization for IoT communication
    Energy Optimization and Security in Federated Learning for Iot Environments, 2025
  • Enhancing User Revocation Mechanisms for Shared Resources to Strengthen Data Security in Public Clouds
    P. Vamsipriya Darshini, D. Sangeetha, S. Dinesh Krishnan, V. Sathya Priya
    2024 International Conference on Electrical Electronics and Computing Technologies Iceect 2024, 2024
    Users may quickly change and share data among themselves using cloud-based data storage and sharing services. Users in the group must calculate signatures on every block of mutually exchanged data to guarantee that its integrity can be independently validated by third parties. Due to data updates made by many users, separate blocks of shared data are typically signed by various end users. For security concerns, if a person left a group, the blocks they previously signed are no longer valid. A current user must sign again for this revoked user. The simple approach, which enables a current user to download the relevant portion of Resigning shared data after a user revokes their access is ineffective because of the amount of the shared information and data in the cloud. In this research paper, we provide a new method for public auditing that guarantees the state of information and data shared while improving the effectiveness of user revocation. Proxy re-signatures enable the cloud to relinquish data blocks on behalf of users who have had their access revoked, removing the requirement for these users to manually download and re-sign the blocks. Furthermore, a public verifier does not need to download the complete file in order to confirm data integrity, even in cases where the cloud has re-signed some of the shared data. Moreover, our approach makes batch auditing easier by allowing several auditing jobs to be validated at the same time. Experimental findings demonstrate that our technique can greatly increase the effectiveness of user revocation.
  • Handwritten Text Recognition and Conversion System
    S.Dinesh Krishnan, Vaishnavi Tangallapelli, Honey Rechal Talari, Vaignika Chinthakunta, Shailaja Pulluri
    8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions Csitss 2024, 2024
    Even with differences in handwriting styles and quality, handwritten text can be recognized and converted using platforms offered by handwritten text recognition and conversion systems. For offline handwritten word recognition, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Connectionist Temporal Classification (CTC) are used. The system's goals are to increase productivity and accuracy in a number of areas, including address identification, signature verification, and application interpretation. It uses OpenCV and TensorFlow image processing algorithms for training and recognition, generating digital outputs in the process. The research also discusses the more general problem of Automated Handwritten Text Recognition (AHTR) and highlights how difficult it is to classify a variety of handwritten characters, such as numbers, symbols, and scripts written in several languages. The suggested approach makes use of CNNs to efficiently recognize handwriting in a variety of formats. Grammar mistakes are eliminated, and meaningful statements are produced. It also has other capabilities including the ability to transform text from any language to a user-specified language, which would significantly improve the system's accessibility and usefulness.
  • Twin Technology: Exploring Types and Applications of Digital Twins
    S. Dinesh Krishnan, K. V. Mahalakshmi, Dyagala Naga Sudha, V. Sathya Priya, A. Daniel
    Digital Twin Technology and Applications, 2024
    This document provides an in-depth exploration of twin technology, which involves creating digital replicas of physical objects, systems, or processes. The chapter discusses the types of twin technology, including virtual twins, digital twins, and hybrid twins, along with their respective characteristics and applications. Furthermore, it highlights various industries where twin technology is applied, such as manufacturing, healthcare, transportation, and smart cities. Real-world examples and conceptual diagrams are provided to illustrate the practical implementation and benefits of twin technology in each application area.
  • Blockchain Technology Provides Machine Learning, Cloud Computing and Secure Data Transmission
    S. Dinesh Krishnan, G. Prathyusha, V. Sathya Priya
    Proceedings of the 2023 International Conference on Emerging Techniques in Computational Intelligence Icetci 2023, 2023
    Blockchain technology is revolutionizing industries all over the world. Due to recent developments in machine learning, new technologies like cloud computing and safe data exchange have evolved. The collecting and processing of training data on centralized systems is required for conventional machine learning methods. New decentralized machine learning algorithms and cloud computing have made ML on-device information learning possible. IoT devices may use cloud computing services to outsource training duties, enabling AI at the network edge. Nevertheless, these scattered edges intelligence systems introduce new problems, such as concerns over user privacy and information security. Blockchain has been suggested as a workable solution to these problems. Due to its decentralized, accessible, and secure structure, blockchain has developed into a breakthrough invention for the future of numerous sectors' purposes. Additionally, this system has reliable automated scripting execution and immutable information recorders. Recently, as quantum technologies have become more practical, blockchain has begun to face potential threats from quantum computing. In this work, we summaries the literature in the research domains of blockchain-based cloud computing, machine learning, and safe data sharing in order to offer a synopsis of the present state-of-the-art in these cutting-edge developments. We also provide a basic introduction to post-quantum blockchain.
  • Improved graph neural network-based green anaconda optimization for segmenting and classifying the lung cancer
    S. Dinesh Krishnan, Danilo Pelusi, A. Daniel, V. Suresh, Balamurugan Balusamy
    Mathematical Biosciences and Engineering, 2023
    <abstract> <p>Normal lung cells incur genetic damage over time, which causes unchecked cell growth and ultimately leads to lung cancer. Nearly 85% of lung cancer cases are caused by smoking, but there exists factual evidence that beta-carotene supplements and arsenic in water may raise the risk of developing the illness. Asbestos, polycyclic aromatic hydrocarbons, arsenic, radon gas, nickel, chromium and hereditary factors represent various lung cancer-causing agents. Therefore, deep learning approaches are employed to quicken the crucial procedure of diagnosing lung cancer. The effectiveness of these methods has increased when used to examine cancer histopathology slides. Initially, the data is gathered from the standard benchmark dataset. Further, the pre-processing of the collected images is accomplished using the Gabor filter method. The segmentation of these pre-processed images is done through the modified expectation maximization (MEM) algorithm method. Next, using the histogram of oriented gradient (HOG) scheme, the features are extracted from these segmented images. Finally, the classification of lung cancer is performed by the improved graph neural network (IGNN), where the parameter optimization of graph neural network (GNN) is done by the green anaconda optimization (GAO) algorithm in order to derive the accuracy maximization as the major objective function. This IGNN classifies lung cancer into normal, adeno carcinoma and squamous cell carcinoma as the final output. On comparison with existing methods with respect to distinct performance measures, the simulation findings reveal the betterment of the introduced method.</p> </abstract>
  • Traffic Sign Recognition Using Convolutional Neural Network
    R. Pitchai, Pasham Tejaswini yadav, Pasupula Harshavardhan, Parlapati Sudheer kumar, S. Dinesh Krishnan, G. Arun Prasath
    Proceedings of IEEE 2023 5th International Conference on Advances in Electronics Computers and Communications Icaecc 2023, 2023
    Research in the field of road and traffic sign recognition is one sector that has the potential to be of assistance in the creation of both an inventory system and an in-car advising system. Both of these systems are intended to assist drivers while they are behind the wheel. Both of these applications might stand to benefit from the implementation of road and traffic sign recognition technology. An Intelligent Transport System monitors the driver, the vehicle, and the road continuously in order to avoid accidents on the road. For example, alert the driver in a timely manner about upcoming decision points regarding navigation and potentially dangerous traffic situations. A road and traffic sign recognition system could, in theory, be developed as part of an Intelligent Transport System. The modern technology, which is based on artificial intelligence, is equipped with the potential to recognize traffic signs, which is a crucial component. The goal of traffic signs is to provide drivers with information about traffic rules, road conditions, and route directions in order to assist them in driving in a manner that is more competent and safe. In order to be able to recognize road signs, a convolution neural network, often known as a CNN, has been constructed to categorize the many kinds of traffic signs that are utilized in candidate areas. The results of the experiments suggest that our technique is an efficient means of accomplishing the objectives that we set out to accomplish.
  • Improving Action Recognition through Pose Estimation and Directed Graph Neural Networks
    S Dinesh Krishnan, Arun Prasath G, Rahul Gatla, Bindhusri Kommula, Vishnu Vardhan Kalali, Bharath Kaparthi
    2023 IEEE International Conference on Research Methodologies in Knowledge Management Artificial Intelligence and Telecommunication Engineering Rmkmate 2023, 2023
    To improve the accurate performance and robustness of action recognition systems, this study attempts to merge the posture estimation and skeleton-based action recognition. While pose estimation provides a spatial representation of important places on the human body, skeleton-based action identification focuses on examining the time dynamics of human motions. By combining these two methods, we increase the performance of action recognition by utilizing the complementary data from both the spatial and temporal domains. Through empirical evaluation on benchmark dataset, this study shows the efficiency of alternative approaches for fusing posture estimation with action recognition. The outcomes show that this integrated method has the potential to attain cutting-edge performance in identifying human actions.
  • Blockchain-based Privacy-Preserving System for Internet of Things (IoT)
    C. N. Ravi, S Dinesh Krishnan, Manikannan Kaliyaperumal, Sumita Kumar, A. V. S. Ram Prasad, S. Suma Christal Mary
    Proceedings of the 8th International Conference on Communication and Electronics Systems Icces 2023, 2023
  • A Holistic Approach based computing Authentication System
    Ankit Kumar Navalakha, S Dinesh Krishnan, Sunita Pachar, Pratap Patil, Ajay Singh Yadav, Sarvesh Kumar
    Proceedings 2022 2nd International Conference on Innovative Sustainable Computational Technologies Cisct 2022, 2022

RECENT SCHOLAR PUBLICATIONS

  • 10 Privacy and Trust in eHealth: A Fuzzy Linguistic Solution
    GA Prasath, P Nisha, SD Krishnan, D Arockiam
    Healthcare 5.0 with Fuzzy Logic: Artificial Intelligence, Cyber-Physical … , 2025
    2025.0
  • A novel optimized hybrid deep learning model for intrusion detection in software defined internet of things (SDIoT) environment
    A Daniel, SD Krishnan, DN Sudha, B Balusamy, A Nayyar
    Cluster Computing 28 (15), 981 , 2025
    2025.0
  • A novel method on deep learning models for time series data analysis and traffic flow prediction
    SVS Aparna, DK Subramaniam, G Shruthi, R Usha, Moulika, BN Goud
    AIP Conference Proceedings 3342 (1), 060032 , 2025
    2025.0
  • An algorithm for heterogeneous wireless network connections for user preferences and services
    SD Krishnan, A Daniel, S Ayyasamy, B Balusamy, S Selvarajan, ...
    Scientific Reports 15 (1), 17340 , 2025
    2025.0
    Citations: 1
  • Handwritten Text Recognition and Conversion System
    SD Krishnan, V Tangallapelli, HR Talari, V Chinthakunta, S Pulluri
    2024 8th International Conference on Computational System and Information … , 2024
    2024.0
  • Twin Technology
    SD Krishnan, KV Mahalakshmi, DN Sudha, VS Priya, A Daniel
    Digital Twin Technology and Applications , 2024
    2024.0
  • Twin Technology: Exploring Types and Applications of Digital Twins
    SD Krishnan, KV Mahalakshmi, DN Sudha, VS Priya, A Daniel
    Digital Twin Technology and Applications, 81-108 , 2024
    2024.0
  • Enhancing User Revocation Mechanisms for Shared Resources to Strengthen Data Security in Public Clouds
    PV Darshini, D Sangeetha, SD Krishnan, VS Priya
    2024 International Conference on Electrical Electronics and Computing … , 2024
    2024.0
  • Improving Action Recognition through Pose Estimation and Directed Graph Neural Networks
    SD Krishnan, A Prasath, R Gatla, B Kommula, VV Kalali, B Kaparthi
    2023 International Conference on Research Methodologies in Knowledge … , 2023
    2023.0
  • Blockchain Technology Provides Machine Learning, Cloud Computing and Secure Data Transmission
    SD Krishnan, G Prathyusha, VS Priya
    2023 International Conference on Emerging Techniques in Computational … , 2023
    2023.0
    Citations: 1
  • Traffic Sign Recognition Using Convolutional Neural Network
    R Pitchai, P Harshavardhan, SD Krishnan, GA Prasath
    2023 IEEE Fifth International Conference on Advances in Electronics … , 2023
    2023.0
  • Improved graph neural network-based green anaconda optimization for segmenting and classifying the lung cancer.
    SD Krishnan, D Pelusi, A Daniel, V Suresh, B Balusamy
    Mathematical biosciences and engineering: MBE 20 (9), 17138-17157 , 2023
    2023.0
    Citations: 12
  • Blockchain-based Privacy-Preserving System for Internet of Things (IoT)
    CN Ravi, SD Krishnan, M Kaliyaperumal, S Kumar, AVSR Prasad, ...
    2023 8th International Conference on Communication and Electronics Systems … , 2023
    2023.0
    Citations: 2
  • A Holistic Approach based computing Authentication System
    AK Navalakha, SD Krishnan, S Pachar, P Patil, AS Yadav, S Kumar
    2022 2nd International Conference on Innovative Sustainable Computational … , 2022
    2022.0
  • Protect Women from Abuse and Assure Immediate Safety
    SD Krishnan, S Robin, VR Sivasurya, ST Kannan
    International Journal of Emerging Technologies in Engineering Research … , 2020
    2020.0
    Citations: 2
  • Energy optimization for IoT communication
    G Arun Prasath, S Dinesh Krishnan, AS Shanthi, D Arockiam
  • Decision Based Algorithm for the Removal of High Density Salt and Pepper Noise in Images and Videos
    SD Krishnan, RM Kumar
  • Link-Stability and Energy Aware Routing in Wireless Sensor Networks
    D Rajapriya, SD Krishnan, VS Priya

MOST CITED SCHOLAR PUBLICATIONS

  • Improved graph neural network-based green anaconda optimization for segmenting and classifying the lung cancer.
    SD Krishnan, D Pelusi, A Daniel, V Suresh, B Balusamy
    Mathematical biosciences and engineering: MBE 20 (9), 17138-17157 , 2023
    2023.0
    Citations: 12
  • Blockchain-based Privacy-Preserving System for Internet of Things (IoT)
    CN Ravi, SD Krishnan, M Kaliyaperumal, S Kumar, AVSR Prasad, ...
    2023 8th International Conference on Communication and Electronics Systems … , 2023
    2023.0
    Citations: 2
  • Protect Women from Abuse and Assure Immediate Safety
    SD Krishnan, S Robin, VR Sivasurya, ST Kannan
    International Journal of Emerging Technologies in Engineering Research … , 2020
    2020.0
    Citations: 2
  • An algorithm for heterogeneous wireless network connections for user preferences and services
    SD Krishnan, A Daniel, S Ayyasamy, B Balusamy, S Selvarajan, ...
    Scientific Reports 15 (1), 17340 , 2025
    2025.0
    Citations: 1
  • Blockchain Technology Provides Machine Learning, Cloud Computing and Secure Data Transmission
    SD Krishnan, G Prathyusha, VS Priya
    2023 International Conference on Emerging Techniques in Computational … , 2023
    2023.0
    Citations: 1
  • 10 Privacy and Trust in eHealth: A Fuzzy Linguistic Solution
    GA Prasath, P Nisha, SD Krishnan, D Arockiam
    Healthcare 5.0 with Fuzzy Logic: Artificial Intelligence, Cyber-Physical … , 2025
    2025.0
  • A novel optimized hybrid deep learning model for intrusion detection in software defined internet of things (SDIoT) environment
    A Daniel, SD Krishnan, DN Sudha, B Balusamy, A Nayyar
    Cluster Computing 28 (15), 981 , 2025
    2025.0
  • A novel method on deep learning models for time series data analysis and traffic flow prediction
    SVS Aparna, DK Subramaniam, G Shruthi, R Usha, Moulika, BN Goud
    AIP Conference Proceedings 3342 (1), 060032 , 2025
    2025.0
  • Handwritten Text Recognition and Conversion System
    SD Krishnan, V Tangallapelli, HR Talari, V Chinthakunta, S Pulluri
    2024 8th International Conference on Computational System and Information … , 2024
    2024.0
  • Twin Technology
    SD Krishnan, KV Mahalakshmi, DN Sudha, VS Priya, A Daniel
    Digital Twin Technology and Applications , 2024
    2024.0
  • Twin Technology: Exploring Types and Applications of Digital Twins
    SD Krishnan, KV Mahalakshmi, DN Sudha, VS Priya, A Daniel
    Digital Twin Technology and Applications, 81-108 , 2024
    2024.0
  • Enhancing User Revocation Mechanisms for Shared Resources to Strengthen Data Security in Public Clouds
    PV Darshini, D Sangeetha, SD Krishnan, VS Priya
    2024 International Conference on Electrical Electronics and Computing … , 2024
    2024.0
  • Improving Action Recognition through Pose Estimation and Directed Graph Neural Networks
    SD Krishnan, A Prasath, R Gatla, B Kommula, VV Kalali, B Kaparthi
    2023 International Conference on Research Methodologies in Knowledge … , 2023
    2023.0
  • Traffic Sign Recognition Using Convolutional Neural Network
    R Pitchai, P Harshavardhan, SD Krishnan, GA Prasath
    2023 IEEE Fifth International Conference on Advances in Electronics … , 2023
    2023.0
  • A Holistic Approach based computing Authentication System
    AK Navalakha, SD Krishnan, S Pachar, P Patil, AS Yadav, S Kumar
    2022 2nd International Conference on Innovative Sustainable Computational … , 2022
    2022.0
  • Energy optimization for IoT communication
    G Arun Prasath, S Dinesh Krishnan, AS Shanthi, D Arockiam
  • Decision Based Algorithm for the Removal of High Density Salt and Pepper Noise in Images and Videos
    SD Krishnan, RM Kumar
  • Link-Stability and Energy Aware Routing in Wireless Sensor Networks
    D Rajapriya, SD Krishnan, VS Priya