Sridevi.V

@psgcas.irins.org

Assistant Professor and Department of Computer Science
PSG College of Arts and Science

Sridevi.V


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https://researchid.co/vissridevi

EDUCATION

M.C.A, M.Phil, Ph.D

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Vision and Pattern Recognition
6

Scopus Publications

149

Scholar Citations

3

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • A Secure Interoperability Framework for Heterogeneous Internet of Things Devices in Intelligent Transportation Systems
    Dr.M. Sangeetha, Dr.V. Sridevi
    International Academic Journal of Science and Engineering, 2025
    The rapid deployment of diverse types of IoT devices throughout ITS has led to major obstacles concerning data interoperability and the security of networks that support these devices. Multi-layer models traditionally are subject to a high computational latency and to incompatibility in protocol translation. The Secure Interoperability Framework (SIF) is a novel proposed framework for supporting secure interoperability among multiple communications protocols at the network edge, such as MQTT, CoAP and REST, through the employment of the newly-developed Secure Semantic Translation (SST) algorithm. The proposed experimental design will utilize both simulation-based experimental designs through the network simulator NS-3 and real-world vehicle mobility data from the CRAWDAD database to evaluate the framework's performance with 'real-world' traffic in an urban environment. Results show that the SIF optimizes the Security-Interoperability trade-off substantially, as evidenced by a quantitative analysis that demonstrates a 33% improvement in end-to-end latency when compared to current models, with an average processing delay of 74 ms, well within the safety-critical threshold for vehicular communications. In addition, the framework demonstrated a high Interoperability Success Rate (ISR) of 96.8% and a Packet Delivery Ratio (PDR) of 94.2%. Statistical validation through a T-test verified the statistical significance of the performance improvements with a p-value < 0.001. Further, the inclusion of lightweight encryption techniques ensures strong protection, yielding a 91.5% attack detection rate with only a small security overhead of 8%. The findings of this study suggest that through the use of the SST algorithm, a highly scalable and secure method has been achieved for deploying a heterogeneous ITS environment. Finally, there are plans for using machine learning approaches to automate the protocol mapping process in a future 6G-enabled V2X environment.
  • Autonomous Drone Swarms for Environmental Monitoring and Wildfire Detection Using Edge Intelligence
    Dr.K.R. Nandagopal, Dr.V. Sridevi, Dr.P. Balamurugan
    International Academic Journal of Science and Engineering, 2025
    Wildfires are one of the serious environmental and ecological hazards resulting in massive loss of forest ecosystems, wild animals, and human settlements. Timely identification and active surveillance of wildfires will play a crucial role in reducing the area of the wildfire and responding promptly to the disaster. This paper offers an independent drone swarm system to monitor nature and detect wildfires through the use of unmanned aerial vehicles (UAVs), edge intelligence, and deep learning technologies. The system makes use of swarming UAVs, fitted with RGB and radiometric thermal sensors, to survey high-resolution aerial images of wildfire-prone areas. A deep learning model in multimodal is created to process the visual and thermal data and provide a precise indication of the wildfire along with fire region division. The FLAME 3 Dataset is used to conduct the experimental assessment that offers synchronized UAV-based RGB and thermal wildfire imagery as well as fire segmentation annotations. In order to facilitate real-time analysis and minimize communication latency, the trained deep learning model is deployed in edge computing modules that are packaged in UAV platforms. Edge intelligence enables every drone to interpret acquired imagery on the ground, identify the area of the wildfire, and create instantaneous alert messages without any full dependency on centralized cloud services. Another thing is that by use of a swarm coordination mechanism, a group of UAVs is able to monitor large areas of forest, exchange environmental information, and maximize coverage. The framework is tested based on the standard machine learning metrics such as accuracy, precision, recall, F1-score, and Intersection over Union (IoU), as well as operational metrics such as the detection latency and monitoring coverage efficiency. The experimental results demonstrate that the proposed system achieves an accuracy of 94.3%, precision of 92.8%, recall of 93.6%, F1-score of 93.2%, and IoU score of 89.5%, while maintaining a low detection latency of 1.8 seconds and achieving a monitoring coverage efficiency of 92.4%. The experimental results demonstrate that multimodal UAV imagery, edge-based processing, and swarm coordination methods allow improvement of accuracy in wildfire detection and maximize efficiency in wildfire monitoring significantly. The proposed solution is a very efficient and scaled way of protecting the environment in real time and early warning of wildfires in large forest habitats.
  • Innovative pedagogies for 6G security educating the next generation
    Anandhi Damodaraswamy, V. Sridevi, V. Revathi, Lavish Kansal, Melanie Lourens, Joshuva Arockia Dhanraj
    6g Security Education and Multidisciplinary Implementation, 2024
    The introduction of 6G networks promises previously unheard-of levels of innovation and connectivity in today's quickly changing technological environment. But this progress also means that strong security measures are desperately needed to guard against new cyberthreats. Investigating cutting-edge pedagogical strategies that promote in-depth comprehension and real-world application of security concepts is crucial as we train the next generation of professionals to handle the complexity of 6G security. The goal of the chapter is to transform the way that security is taught and learned in relation to 6G networks.
  • A combined deep CNN-lasso regression feature fusion and classification of MLO and CC view mammogram image
    V. Sridevi, J. Abdul Samath
    International Journal of System Assurance Engineering and Management, 2024
  • MLO and CC View of Feature Fusion and Mammogram Classification Using a Deep Convolution Neural Network
    Applied Intelligence for Medical Image Analysis, 2024
  • Deep Neural Networks Implementation on IoT Devices: A Practical Guide
    V. Sridevi, Dipen Bhuva, Abhishek Bhuva, Mithilesh Kumar Sharma, Sumeet Gupta, Mukesh Soni
    Proceedings 3rd International Conference on Advances in Computing Communication and Applied Informatics Accai 2024, 2024
    Deep neural network-powered Internet of Things (IoT) devices are anticipated to revolutionise a range of industrial applications, propelled by the recent expansion of both IoT and DNN fields. On the other hand, DNNs process the data produced by IoT devices using a variety of parameters and processes. High energy consumption and latency in data processing are the outcomes of this. In order to address these problems and integrate real-time DNNs into IoT devices with constrained resources, new strategies are being investigated.

RECENT SCHOLAR PUBLICATIONS

  • Social engineering and spam detection of AI-driven Phishing emails
    V Sridevi, SM Saravanakumar
    Social engineering and spam detection of AI-driven Phishing emails 12 (3 … , 2025
    2025.0
    Citations: 1
  • Breast Cancer Examination in Digitized Mammograms using Integrated K-Means Clustering with Garbor Filter and Shrunk Kernel KNN Method
    DV Sridevi
    Indian Journal of Science and Technology 17 (23), 2444-2454 , 2024
    2024.0
    Citations: 2
  • BREAST CANCER PROGNOSIS PREDICTION USING NOVEL SHRUNK KERNEL KNN METHOD WITH MLO AND CC FEATURES
    DV Sridevi
    Futuristic Trends in Artificial Intelligence 3, 137-154 , 2024
    2024.0
  • Deep neural networks implementation on IoT devices: a practical guide
    V Sridevi, D Bhuva, A Bhuva, MK Sharma, S Gupta, M Soni
    2024 International Conference on Advances in Computing, Communication and … , 2024
    2024.0
    Citations: 2
  • MLO and CC View of Feature Fusion and Mammogram Classification Using a Deep Convolution Neural Network
    JAS V. Sridevi
    Applied Intelligence for Medical Image Analysis, 16 , 2024
    2024.0
  • Innovative Pedagogies for 6G Security Educating the Next Generation
    JAD Anandhi Damodaraswamy, V. Sridevi, V. Revathi, Lavish Kansal, Melanie ...
    6G Security Education and Multidisciplinary Implementation, 1-22 , 2024
    2024.0
  • A combined deep CNN‑lasso regression feature fusion and classifcation of MLO and CC view mammogram image
    VSJA Samath
    International Journal of System Assurance Engineering and Management, https … , 2023
    2023.0
    Citations: 9
  • DIAGNOSIS OF DIABETES MELLITUS USING MACHINE LEARNING TECHNIQUES FOR EFFICIENT REVIEW
    MVS Dr C THIYAGARAJAN, Mrs A VAIDEGHY
    International Journal of Early Childhood Special Education 14 (02), 4184-4187 , 2022
    2022.0
  • MLO and CC view of feature fusion and mammogram classification using deep convolution neural network
    DJAS V.Sridevi
    International Journal of Health Sciences 6 (S7), 47194–47205 , 2022
    2022.0
    Citations: 3
  • Design and implementation of transfer learned deep CNN with feature fusion for automated mammogram classification
    DJAS V.Sridevi
    International Journal of Health Sciences 6 (s6), 3033 - 3047 , 2022
    2022.0
    Citations: 1
  • Advancement on Breast Cancer Detection Using Medio-Lateral-Oblique (Mlo) and Cranio-Caudal (CC) Features
    DJAS V. Sridevi
    Test Engineering & Management 83 (May - June 2020), 85 - 93 , 2020
    2020.0
    Citations: 3
  • A survey on breast cancer segmentation and classification using several methods
    JAS V. Sridevi
    International Journal of Scientific Research in Computer Science … , 2019
    2019.0
    Citations: 2
  • User interface design
    S Sridevi
    International Journal of Computer Science and Information Technology … , 2014
    2014.0
    Citations: 120
  • FICTION WORK ON FUZZY BASED CANCER GENE IDENTIFICATION THROUGH CLUSTERING
    PV V. Sridevi
    International Journal of computer science and communication networks 4 (6) , 2014
    2014.0
  • MULTIMODAL BIOMETRIC SCHEME USING FINGERPRINT & IRIS FUSION
    V Sridevi
    International Journal of Emerging Technologies in Computational and Applied … , 2014
    2014.0
  • Dynamic Storage Security in Cloud Computing
    PV V. Sridevi
    International Journal of Emerging Technologies in Computational and Applied … , 2014
    2014.0
  • Segmentation of Medical Images using Image Registration
    A Nirmala, V Sridevi
    2014.0
  • A Review on Inspection of welding defects using Segmentation Techniques
    A Nirmala, V Sridevi
    International Journal of Scientific and Engineering Research 5 (2), 276 - 278 , 2014
    2014.0
  • Network Delineate Safeguard for Surplus Control
    VS A. Nirmala, C.Kumuthini
    International Journal of Scientific and Research Publications 3 (3), 1-4 , 2013
    2013.0
  • Cancer Gene Identification through Clustering
    V Sridevi, P Vidhya

MOST CITED SCHOLAR PUBLICATIONS

  • User interface design
    S Sridevi
    International Journal of Computer Science and Information Technology … , 2014
    2014.0
    Citations: 120
  • A combined deep CNN‑lasso regression feature fusion and classifcation of MLO and CC view mammogram image
    VSJA Samath
    International Journal of System Assurance Engineering and Management, https … , 2023
    2023.0
    Citations: 9
  • Inspection Of Welding Images Using Image Segmentation Techniques
    V Sridevi, A Nirmala
    International Journal of Engineering Research & Technology (IJERT) Vol 2 , 0
    Citations: 6
  • MLO and CC view of feature fusion and mammogram classification using deep convolution neural network
    DJAS V.Sridevi
    International Journal of Health Sciences 6 (S7), 47194–47205 , 2022
    2022.0
    Citations: 3
  • Advancement on Breast Cancer Detection Using Medio-Lateral-Oblique (Mlo) and Cranio-Caudal (CC) Features
    DJAS V. Sridevi
    Test Engineering & Management 83 (May - June 2020), 85 - 93 , 2020
    2020.0
    Citations: 3
  • Breast Cancer Examination in Digitized Mammograms using Integrated K-Means Clustering with Garbor Filter and Shrunk Kernel KNN Method
    DV Sridevi
    Indian Journal of Science and Technology 17 (23), 2444-2454 , 2024
    2024.0
    Citations: 2
  • Deep neural networks implementation on IoT devices: a practical guide
    V Sridevi, D Bhuva, A Bhuva, MK Sharma, S Gupta, M Soni
    2024 International Conference on Advances in Computing, Communication and … , 2024
    2024.0
    Citations: 2
  • A survey on breast cancer segmentation and classification using several methods
    JAS V. Sridevi
    International Journal of Scientific Research in Computer Science … , 2019
    2019.0
    Citations: 2
  • Social engineering and spam detection of AI-driven Phishing emails
    V Sridevi, SM Saravanakumar
    Social engineering and spam detection of AI-driven Phishing emails 12 (3 … , 2025
    2025.0
    Citations: 1
  • Design and implementation of transfer learned deep CNN with feature fusion for automated mammogram classification
    DJAS V.Sridevi
    International Journal of Health Sciences 6 (s6), 3033 - 3047 , 2022
    2022.0
    Citations: 1
  • BREAST CANCER PROGNOSIS PREDICTION USING NOVEL SHRUNK KERNEL KNN METHOD WITH MLO AND CC FEATURES
    DV Sridevi
    Futuristic Trends in Artificial Intelligence 3, 137-154 , 2024
    2024.0
  • MLO and CC View of Feature Fusion and Mammogram Classification Using a Deep Convolution Neural Network
    JAS V. Sridevi
    Applied Intelligence for Medical Image Analysis, 16 , 2024
    2024.0
  • Innovative Pedagogies for 6G Security Educating the Next Generation
    JAD Anandhi Damodaraswamy, V. Sridevi, V. Revathi, Lavish Kansal, Melanie ...
    6G Security Education and Multidisciplinary Implementation, 1-22 , 2024
    2024.0
  • DIAGNOSIS OF DIABETES MELLITUS USING MACHINE LEARNING TECHNIQUES FOR EFFICIENT REVIEW
    MVS Dr C THIYAGARAJAN, Mrs A VAIDEGHY
    International Journal of Early Childhood Special Education 14 (02), 4184-4187 , 2022
    2022.0
  • FICTION WORK ON FUZZY BASED CANCER GENE IDENTIFICATION THROUGH CLUSTERING
    PV V. Sridevi
    International Journal of computer science and communication networks 4 (6) , 2014
    2014.0
  • MULTIMODAL BIOMETRIC SCHEME USING FINGERPRINT & IRIS FUSION
    V Sridevi
    International Journal of Emerging Technologies in Computational and Applied … , 2014
    2014.0
  • Dynamic Storage Security in Cloud Computing
    PV V. Sridevi
    International Journal of Emerging Technologies in Computational and Applied … , 2014
    2014.0
  • Segmentation of Medical Images using Image Registration
    A Nirmala, V Sridevi
    2014.0
  • A Review on Inspection of welding defects using Segmentation Techniques
    A Nirmala, V Sridevi
    International Journal of Scientific and Engineering Research 5 (2), 276 - 278 , 2014
    2014.0
  • Network Delineate Safeguard for Surplus Control
    VS A. Nirmala, C.Kumuthini
    International Journal of Scientific and Research Publications 3 (3), 1-4 , 2013
    2013.0