Kavi Priya S

@mepcoeng.ac.in

Associate Professor, Computer Science and Engineering
Mepco Schlenk Engineering College

Kavi Priya S


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https://researchid.co/urskavi

EDUCATION

M.E(CSE), PH.D

RESEARCH INTERESTS

Sensor Networks, Data Science

FUTURE PROJECTS

IoT Bra to sense breast cancer at earliest stage


Applications Invited
17

Scopus Publications

362

Scholar Citations

11

Scholar h-index

14

Scholar i10-index

Scopus Publications

  • RETRACTED: Multi-objective comprehensive container scheduling and resource allocation for container cloud using tuna swarm optimization algorithm(Journal of Intelligent & Fuzzy Systems, (2025), 48, (229-345))
    J. Lavanya, S. Kavi Priya
    Journal of Intelligent and Fuzzy Systems, 2025
    This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434 .
  • AI-Driven Aircraft Maintenance System for Real-Time Crack Detection and Predictive Maintenance
    V Sathya Prieya, Kavitha Subramani, B V Nikitha, G Jyothika, S Kavi Priya
    2025 International Conference on Smart and Sustainable Technology Incsst 2025, 2025
    This paper proposes the development of an advanced AI-driven aircraft maintenance system designed to address critical inefficiencies in traditional maintenance practices. The system integrates multiple deep learning and machine learning models to enhance predictive capabilities. YOLO (You Only Look Once) is employed for real-time crack detection in aircraft structures, offering rapid and accurate identification of structural damage. Machine learning algorithms are utilized to estimate battery life, incorporating real-world data such as charge/discharge cycles and environmental conditions. Additionally, predictive models analyze sensor data from jet engines to forecast their remaining useful life (RUL), allowing for timely interventions. By combining these functionalities into a centralized platform, the proposed system enables proactive maintenance, reduces downtime, enhances safety, and lowers operational costs. This integrated approach transforms conventional reactive and schedule-based maintenance into a dynamic, data-driven framework. The real-time insights and personalized predictions provided by the system empower maintenance teams to address potential failures before they occur, ensuring optimized maintenance schedules and improved operational reliability. By leveraging cutting-edge AI technologies, this research has the potential to revolutionize aircraft maintenance practices, delivering significant advancements in safety, efficiency, and cost-effectiveness within the aviation industry.
  • Software Defined Network Based Artificial Intelligence Empowered Internet of Medical Things (SDN-AI-IoMT) to Predict COVID-19: Evolution and Challenges
    S. Kavi Priya, N. Saranya
    Wireless Communication Technologies Roles Responsibilities and Impact of Iot 6g and Blockchain Practices, 2024
    COVID-19 has become a derogatory term around the world from its very discovery in Wuhan, China. The people around the globe are fighting the virus till an efficient solution is formulated. Because of advances in various detectors, which leads to another term Internet of things-based medical systems. The Internet of Medical Things (IoMT) is a fusion of healthcare devices and the Internet of Things (IoT). In IoMTs all wearable gadgets are linked and surveyed via the Internet by medical practitioners. As it develops, this enables rapid and fairly low-cost medical services. Smart wearable devices combined with AI will provide consistent monitoring characteristics that allow doctors and nurses to predict the person's illness further better. Employing SDN in AI-empowered IoMT will be effective in diagnosis and monitoring as it provides enormous support to execute complex algorithms and makes better decisions. This chapter discusses the technologies of AI and IoMT in healthcare. This chapter briefly explores the introduction of AI, IoMT, and SDN in healthcare, how the SDN can be integrated with IoMT, and challenges faced by AI empowered IoMT. The literature analysis exhibits that the SDN-based AI-IoMT predicts and monitors various diseases effectively.
  • An Effective Chronic Disease Prediction using Multi-Objective Firefly Optimisation Random Forest Algorithm
    S. Kavi Priya, N. Saranya
    IETE Journal of Research, 2024
    In recent years, the solitary reasons for mortality in the world are chronic diseases such as heart disease, diabetes, and chronic kidney disease. These diseases should be diagnosed earlier; however, the technique is costly as well as it leads to many complications. Considering the complexity, datamining performs a major part in accurately classifying chronic disease. A new approach to classify chronic disease is by merging the multi-objective firefly optimisation algorithm (MOFFA) and random forest (RF). The main goal is generating an efficient and heterogeneous decision trees, while determining the optimum training sets to run at the same time. Rather utilising traditional approach like bootstrap, multi-objective firefly optimisation algorithm and random forest algorithm are proposed in this method. As a result, to train random forest, various training sets are generated with alternative instances and attributes. As a result, the performance of random forests can be improved and thus the prediction accuracy. The effectiveness of the proposed method is explored by juxtaposing the effectiveness of the proposed method with other classifiers for different datasets. The proposed work is tested on six UCI datasets. According to the findings, the proposed MOFFA-RF algorithm surpass other classifiers by the accuracy of 88% on CKD, 87% on CVD, 82% on diabetes, 88% on hepatitis, 88% on WBC, and 76% on ILPD.
  • An embedded feature selection approach for depression classification using short text sequences
    Kavi Priya S., Pon Karthika K.
    Applied Soft Computing, 2023
  • A Contemporarymulti-Objective Feature Selection Model for Depression Detection Using a Hybrid pBGSK Optimization Algorithm
    S. Kavi Priya, K. Pon Karthika
    International Journal of Applied Mathematics and Computer Science, 2023
    Depression is one of the primary causes of global mental illnesses and an underlying reason for suicide. The user generated text content available in social media forums offers an opportunity to build automatic and reliable depression detection models. The core objective of this work is to select an optimal set of features that may help in classifying depressive contents posted on social media. To this end, a novel multi-objective feature selection technique (EFS-pBGSK) and machine learning algorithms are employed to train the proposed model. The novel feature selection technique incorporates a binary gaining-sharing knowledge-based optimization algorithm with population reduction (pBGSK) to obtain the optimized features from the original feature space. The extensive feature selector (EFS) is used to filter out the excessive features based on their ranking. Two text depression datasets collected from Twitter and Reddit forums are used for the evaluation of the proposed feature selection model. The experimentation is carried out using naive Bayes (NB) and support vector machine (SVM) classifiers for five different feature subset sizes (10, 50, 100, 300 and 500). The experimental outcome indicates that the proposed model can achieve superior performance scores. The top results are obtained using the SVM classifier for the SDD dataset with 0.962 accuracy, 0.929 F1 score, 0.0809 log-loss and 0.0717 mean absolute error (MAE). As a result, the optimal combination of features selected by the proposed hybrid model significantly improves the performance of the depression detection system.
  • EliteVec: Feature Fusion for Depression Diagnosis Using Optimized Long Short-Term Memory Network
    S. Kavi Priya, K. Pon Karthika
    Intelligent Automation and Soft Computing, 2023
    Globally, depression is perceived as the most recurrent and risky disorder among young people and adults under the age of 60. Depression has a strong influence on the usage of words which can be observed in the form of written texts or stories posted on social media. With the help of Natural Language Processing(NLP) and Machine Learning (ML) techniques, the depressive signs expressed by people can be identified at the earliest stage from their Social Media posts. The proposed work aims to introduce an efficacious depression detection model unifying an exemplary feature extraction scheme and a hybrid Long Short-Term Memory network (LSTM) model. The feature extraction process combines a novel feature selection method called Elite Term Score (ETS) and Word2Vec to extract the syntactic and semantic information respectively. First, the ETS method leverages the document level, class level, and corpus level probabilities for computing the weightage/score of the terms. Then, the ideal and pertinent set of features with a high ETS score is selected, and the Word2vec model is trained to generate the intense feature vector representation for the set of selected terms. Finally, the resultant word vector obtained is called EliteVec, which is fed to the hybrid LSTM model based on Honey Badger optimizer with population reduction technique (PHB) which predicts whether the input textual content is depressive or not. The PHB algorithm is integrated to explore and exploit the optimal hyperparameters for strengthening the performance of the LSTM network. The comprehensive experiments are carried out with two different Twitter depression corpus based on accuracy and Root Mean Square Error (RMSE) metrics. The results demonstrated that the proposed EliteVec+LSTM+PHB model outperforms the state-of-art models with 98.1% accuracy and 0.0559 RMSE.
  • An Intelligent Approach for Accurate Prediction of Chronic Diseases
    S. Kavi Priya, N. Saranya
    Computer Systems Science and Engineering, 2023
    Around the globe, chronic diseases pose a serious hazard to healthcare communities. The majority of the deaths are due to chronic diseases, and it causes burdens across the world. Through analyzing healthcare data and extracting patterns healthcare administrators, victims, and healthcare communities will get an advantage if the diseases are early predicted. The majority of the existing works focused on increasing the accuracy of the techniques but didn’t concentrate on other performance measures. Thus, the proposed work improves the early detection of chronic disease and safeguards the lives of the patients by increasing the specificity and sensitivity of the classifiers along with the accuracy. The proposed work used a hybrid optimization algorithm called the Hybrid Gravitational Search algorithm and Particle Swarm Optimization algorithm (HGSAPSO) to upgrade the detection of chronic diseases. Existing classifier parameters with their optimized parameters are compared and evaluated. Classifiers such as Artificial Neural Network (ANN), Support Vector Machines (SVM), K-Nearest Neighbor (Knn), and Decision tree (DT) are used. Health care data are obtained from the UCI machine learning repository to evaluate the proposed work. The proposed work is assessed on 6 benchmark datasets and the performance metrics such as Accuracy, Specificity, Sensitivity, F-measure, Recall, and Precision are compared. The experimental results exhibit that the proposed work attains better accuracy on Artificial Neural Network-Hybrid Gravitational Search algorithm and Particle Swarm Optimization algorithm (ANN-HGSAPSO) classifier compared to other classifiers. ANN-HGSAPSO provides 93% accuracy for Chronic Kidney Disease (CKD), Cardio Vascular Disease (CVD) 96%, Diabetes 82%, Hepatitis 94%, Wisconsin Breast Cancer (WBC) 91%, and for Liver disease dataset 96%.
  • Applied fuzzy heuristics for automation of hygienic drinking water supply system using wireless sensor networks
    S. Kavi Priya, G. Shenbagalakshmi, T. Revathi
    Journal of Supercomputing, 2020
  • QBuzZ – Conductorless Bus Transportation System
    S. Kavi Priya, S. Naveen Kumar, K. Sathish Kumar, S. Manikandan
    Lecture Notes on Data Engineering and Communications Technologies, 2020
  • Medical image denoising using multi-resolution transforms
    J. Relin Francis Raj, K. Vijayalakshmi, S. Kavi Priya
    Measurement Journal of the International Measurement Confederation, 2019
  • Architecture of smart sensors for real time drinking water quality and contamination detection in water distributed mains
    Romanian Journal of Information Science and Technology, 2019
  • IoT Based Automation of Real Time In-Pipe Contamination Detection System in Drinking Water
    S. Kavi Priya, G. Shenbagalakshmi, T. Revathi
    Proceedings of the 2018 IEEE International Conference on Communication and Signal Processing Iccsp 2018, 2018
  • Multi-constraint multi-objective QoS aware routing heuristics for query driven sensor networks using fuzzy soft sets
    Kavi Priya S., Revathi T., Muneeswaran K.
    Applied Soft Computing Journal, 2017
  • Maximize network lifetime using SRN techniques in wireless sensor networks
    International Journal of Applied Engineering Research, 2015
  • A study on particle swarm optimization in clustering wireless sensor networks
    International Journal of Control Theory and Applications, 2015
  • Heuristic routing with bandwidth and energy constraints in sensor networks
    S. Kavi Priya, T. Revathi, K. Muneeswaran, K. Vijayalakshmi
    Applied Soft Computing Journal, 2015

RECENT SCHOLAR PUBLICATIONS

  • Concentrating Solar Power Technology for Hydrogen Production by Biophotolysis
    S Priya, P Brijesh
    Biotechnology Innovations for a Sustainable Future: Integrating Clean Energy … , 2026
    2026
  • IoT-enabled healthcare system using machine learning
    PJ Thilaga, KV Saravanan, SK Priya, K Vijayalakshmi
    Internet of Things enabled Machine Learning for Biomedical Applications, 41-57 , 2024
    2024
    Citations: 3
  • Software Defined Network Based Artificial Intelligence Empowered Internet of Medical Things (SDN-AI-IoMT) to Predict COVID-19: Evolution and Challenges
    SK Priya, N Saranya
    Wireless Communication Technologies, 241-258 , 2024
    2024
  • Effectiveness of neurolinguistic programme upon emotional intelligence among nursing faculty
    S Priya, K Vijayalakshmi, A Merlin
    RESEARCH REVIEW International Journal of Multidisciplinary 9 (5), 251-261 , 2024
    2024
    Citations: 3
  • Brain tumor segmentation based on kernel fuzzy c-means and penguin search optimization algorithm
    JRF Raj, K Vijayalakshmi, SK Priya, A Appathurai
    Signal, Image and Video Processing 18 (2), 1793-1802 , 2024
    2024
    Citations: 10
  • An effective chronic disease prediction using multi-objective firefly optimisation random forest algorithm
    S Kavi Priya, N Saranya
    IETE Journal of Research 70 (1), 307-321 , 2024
    2024
    Citations: 4
  • Wireless-Enabled Machine Learning Integration for Enhanced Lung Cancer Detection via Electronic Nose VOC Sensors
    SK Priya, C Narmadha
    JOURNAL OF AMERICAN MEDICAL SCIENCE AND RESEARCH Учредители: Research Floor … , 2024
    2024
  • Predictors of creativity among college faculty
    S Priya, K Vijayalakshmi, A Merlin
    International Journal of Indian Psychology 12 (2), 2927-2936 , 2024
    2024
    Citations: 5
  • An integrated approach for depression diagnosis using 3S feature embeddings and G-BLS with T-pHBGO optimizer
    K Pon Karthika, S Kavi Priya
    Expert Systems with Applications 235 (0), 1-15 , 2024
    2024
    Citations: 3
  • Instructional system design for wireless networks
    DSK Priya
    Copyright Office, India , 2023
    2023
  • An embedded feature selection approach for depression classification using short text sequences
    SK Priya, KP Karthika
    Applied Soft Computing 147 , 2023
    2023
    Citations: 12
  • Instructional System Design for Mobile Application Development
    DSK Priya, MCA Devi
    Copyright Office, India , 2023
    2023
  • Depression analysis using visual and textual cues
    SK Priya, S Priyadharsini, KP Karthika
    AIP Conference Proceedings 2831 (1), 020004 , 2023
    2023
  • Data protection and security enhancement in cyber-physical systems using AI and blockchain
    K Vignesh Saravanan, P Jothi Thilaga, S Kavipriya, K Vijayalakshmi
    AI models for blockchain-based intelligent networks in IoT systems: Concepts … , 2023
    2023
    Citations: 22
  • Integration of AI, blockchain, and IoT technologies for sustainable and secured Indian public distribution system
    SK Priya, N Balaganesh, KP Karthika
    AI Models for Blockchain-Based Intelligent Networks in IoT Systems: Concepts … , 2023
    2023
    Citations: 8
  • Mult-iobjective comprehensive container scheduling and resource allocation for container cloud using Tuna Swarm optimization algorithm
    DSKP Mrs.J.Lavanya
    International Conference on multidisciplinary approach technology and social … , 2023
    2023
    Citations: 2
  • EliteVec: Feature Fusion for Depression Diagnosis Using Optimized Long Short-Term Memory Network.
    S Kavi Priya, K Pon Karthika
    Intelligent Automation & Soft Computing 36 (2) , 2023
    2023
    Citations: 6
  • MOBILE CROWD SENSING BASED SMART PILOT FOR VISUALLY IMPAIREDAND ELDER PEOPLE (M-PILOT)
    S Kavi Priya, N Malathy, N Saranya
    IN Patent 202,341,010,899 , 2023
    2023
  • An Intelligent Approach for Accurate Prediction of Chronic Diseases
    S Kavi Priya, N Saranya
    Computer Systems Science and Engineering 46 (2), 2571-2587 , 2023
    2023
    Citations: 6
  • Enlightening network lifetime based on dynamic time orient energy optimization in wireless sensor network
    S Priya, P Suganthi
    International Journal on Recent and Innovation Trends in Computing and … , 2023
    2023
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • Design of smart sensors for real time drinking water quality monitoring and contamination detection in water distributed mains
    S Kavi Priya, MG Shenbagalakshmi, DT Revathi
    International Journal of Engineering & Technology 7 (1), 47-51 , 2018
    2018
    Citations: 66
  • IoT based automation of real time in-pipe contamination detection system in drinking water
    S Kavi Priya, G Shenbagalakshmi, T Revathi
    2018 International conference on communication and signal processing (ICCSP … , 2018
    2018
    Citations: 28
  • Medical image denoising using multi-resolution transforms
    JRF Raj, K Vijayalakshmi, S Kavi Priya
    Measurement 145, 769-778 , 2019
    2019
    Citations: 26
  • Data protection and security enhancement in cyber-physical systems using AI and blockchain
    K Vignesh Saravanan, P Jothi Thilaga, S Kavipriya, K Vijayalakshmi
    AI models for blockchain-based intelligent networks in IoT systems: Concepts … , 2023
    2023
    Citations: 22
  • Architecture of Smart Sensors for Real Time Drinking Water Quality and Contamination Detection in Water Distributed Mains
    S Kavi Priya, G Shenbagalakshmi, T Revathi
    SCIENCE AND TECHNOLOGY 22 (3-4), 202-214 , 2019
    2019
    Citations: 18
  • Multi-constraint multi-objective QoS aware routing heuristics for query driven sensor networks using fuzzy soft sets
    S Kavi Priya, T Revathi, K Muneeswaran
    Applied Soft Computing 52, 532-548 , 2017
    2017
    Citations: 17
  • In silico ADMET analysis, Molecular docking and in vivo anti diabetic activity of polyherbal tea bag formulation in Streptozotocin-nicotinamide induced diabetic rats
    A Quazi, FP Mohsina, IP Faheem, S Priya
    International Journal of Health Sciences 6 (3), 343-372 , 2022
    2022
    Citations: 16
  • Moth-Flame Optimization for Early Prediction of Heart Diseases
    DUS Dr.S.Haseena, Dr.S.Kavi Priya, Dr.S.Saroja, Dr.R. Madavan, Mr.M. Muhibbullah
    Computational and Mathematical Methods in Medicine 2022 (-), 1-10 , 2022
    2022
    Citations: 13
  • QBuzZ – Conductorless Bus Transportation System
    S Kavi Priya, S Naveen Kumar, K Sathish Kumar, S Manikandan
    Emerging Trends in Computing and Expert Technology 35, 899-907 , 2019
    2019
    Citations: 13
  • An embedded feature selection approach for depression classification using short text sequences
    SK Priya, KP Karthika
    Applied Soft Computing 147 , 2023
    2023
    Citations: 12
  • Movie recommendation system with hybrid collaborative and content-based filtering using convolutional neural network
    S Kavi Priya, S Manonmani, N Dharshana, K Ragaanasuya
    International Journal of Health Sciences 174 (6), 5357 , 2022
    2022
    Citations: 12
  • Brain tumor segmentation based on kernel fuzzy c-means and penguin search optimization algorithm
    JRF Raj, K Vijayalakshmi, SK Priya, A Appathurai
    Signal, Image and Video Processing 18 (2), 1793-1802 , 2024
    2024
    Citations: 10
  • A Contemporarymulti–Objective Feature Selection Model for Depression Detection Using a Hybrid pBGSK Optimization Algorithm
    S Kavi Priya, K Pon Karthika
    International Journal of Applied Mathematics and Computer Science 33 (1 … , 2023
    2023
    Citations: 10
  • Evaluation OF anti diabetic activity OF ichnocarpus frutescens L
    FP Mohsina, IP Faheem, S Priya, SMA Husain
    International Journal of Advances in Pharmacy and Biotechnology 4 (2), 1-12 , 2018
    2018
    Citations: 10
  • Integration of AI, blockchain, and IoT technologies for sustainable and secured Indian public distribution system
    SK Priya, N Balaganesh, KP Karthika
    AI Models for Blockchain-Based Intelligent Networks in IoT Systems: Concepts … , 2023
    2023
    Citations: 8
  • Heuristic routing with bandwidth and energy constraints in sensor networks
    S Kavi Priya, T Revathi, K Muneeswaran, K Vijayalakshmi
    Applied Soft Computing 29, 12-25 , 2015
    2015
    Citations: 8
  • Caption Generation Based on Emotions Using CSPDenseNet and BiLSTM with Self-Attention
    DBM Dr.S.Kavi Priya, Ms.K.Pon Karthika, Dr.Jayakumar Kaliappan, Dr.Senthil ...
    Applied Computational Intelligence and Soft Computing 2022 (-), 1-13 , 2022
    2022
    Citations: 7
  • In silico analysis on macroalgae metabolites against skin cancer protein, phylogenetic and statistical analysis
    S Mahesh, SKP Priya, RM Prasanth, P Anantharaman
    Indian Journal of Biochemistry and Biophysics (IJBB) 58 (3), 219-228 , 2021
    2021
    Citations: 7
  • Applied fuzzy heuristics for automation of hygienic drinking water supply system using wireless sensor networks
    S Kavi Priya, G Shenbagalakshmi, T Revathi
    The Journal of Supercomputing 76 (6), 4349-4375 , 2020
    2020
    Citations: 7
  • EliteVec: Feature Fusion for Depression Diagnosis Using Optimized Long Short-Term Memory Network.
    S Kavi Priya, K Pon Karthika
    Intelligent Automation & Soft Computing 36 (2) , 2023
    2023
    Citations: 6

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

Received and completed a project titled "Automation of Hygienic Drinking Water Supply System using Wireless Sensor Networks (AHDWS)" funded by DST(WTI) as Principal Investigator during 31/12/2014 to 31/03/2018 for , 32708

STARTUP

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