Dr.K.Kalaivani

@vbithyd.ac.in

Assistant Professor
Vignana Bharathi Institute of Technology Aushapur (V), Ghatkesar (M), Medchal Dist, Hyderabad, Telangana – 501301.

Dr.K.Kalaivani


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https://researchid.co/kalaivani

RESEARCH INTERESTS

MACHINE LEARNING
DEEP LEARNING
SOFT COMPUTING
DATA MINING
14

Scopus Publications

42

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Optimizing distributed inference in healthcare IoT: reinforcement learning and explainable AI for dynamic neural network pruning
    Venu Gopal Gaddam, K. Kalaivani, K.V.S.S. Ramakrishna, Srinivasulu Singaraju, Ravikanth Motupalli
    Expert Systems with Applications, 2026
  • GENERATING OPTIMAL TEST CASES USING ELITIST GENETIC ALGORITHM
    Journal of Theoretical and Applied Information Technology, 2025
  • Navigating the digital frontier: data privacy and security challenges in society 5.0
    K. Kalaivani, N. Balamurugan, V.V. Parthu
    Human Centric Integration of Next Generation Data Science and Blockchain Technology Advancing Society 5 0 Paradigms, 2025
  • A novel and secured email classification using deep neural network with bidirectional long short-term memory
    A. Poobalan, K. Ganapriya, K. Kalaivani, K. Parthiban
    Computer Speech and Language, 2025
  • Neuro-Swarm Diffusion Reinforcement with Probabilistic Eco-Adaptive Modeling for Coastal Flood Risk Management
    Ravi Selshi Angel, Kalaivani K, Abdul Basith K, Karan Babaso Patil, Sumaiya Samreen, Hyma Lakshmi T V
    Proceedings of 2025 10th International Conference on Science Technology Engineering and Mathematics Iconstem 2025, 2025
    The climate change and eco-varying causes floods that occur along the coasts to be unpredictable. The aim of the research is to create dynamic and uncertaintyconscious logic of precise prediction and control of flood risks. The paper presents a new model and proposed research namely Eco-Adaptive Neuro-Swarm Flood Management Framework (EANS-FM), a new framework that integrates Neuro-Swarm Diffusion Reinforcement that enables maximized and adaptive policy achievement and Probabilistic Eco-Adaptive Modeling which handles ecological uncertainty. This framework is tested on the basis of the Coastal Climate Risk Dataset that consists of geospatial, climatic, and ecological characteristics. Findings indicate that EANS-FM has a prediction root of 98.98 percent with the ability of adjusting to the dynamic environmental factors and deliver dependable measurements of the flood risks. All in all, the study offers a strong, real time, and ecologically knowledgeable remedy to coastal flooding management pointing to the fact that it integrates adaptative learning and ecological uncertainty modeling into one.
  • Denoising Diffusion Probabilistic Models with Federated Learning for Privacy-Preserving Wind Power Forecasting
    Gummala Alekya, Kalaivani. K, Nagalakshmi M, Omeshwar D Verma, Ramkumar A, Praveen Kumar G
    Proceedings of 2025 10th International Conference on Science Technology Engineering and Mathematics Iconstem 2025, 2025
    Wind energy prediction plays a vital role in grid stability as well as efficient resource utilization and integrating renewable energy, but the traditional centralized solutions have privacy concerns and cannot work with a lot of SCADA data noise. In this paper, the authors seek to create a privacy-preserving, high-accuracy prediction model through a combination of Denoising Diffusion Probabilistic Models (DDPMs) with Federated Learning (FL). It has been not tried before because it can be used on sequential SCADA time-series to condition DDPMs, so that learning and the denoising and predicting step remain robust and do not need to learn many turbines or wind farms to share the raw data with the others. The suggested model preprocesses the SCADA measurements and conditions them temporarily, trains DDPMs at different clients and protects them, and assembles them to create a global model. It has been found that experiments predict with a 98 percent accuracy, the highest of any other classical models, like LSTM, ARIMA and CNN; as well as noise resilience and privacy of information. This work concludes that federated DDPM is an efficient, scalable, and secure technique to predict wind energy in decentralized energy systems.
  • Enhancing 5G Networks with D2D Communication: Architectures, Protocols, and Energy-Efficient Strategies for Future Smart Cities
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • Performance Improvement for Reconfigurable Processor System Design in IoT Health Care Monitoring Applications
    Ganapriya, K., Poobalan, A., Kalaivani, K., Gopinath, S.
    Tehnicki Vjesnik, 2024
    This research focuses on critical hardware components of an Internet of Things (IoT) system for reconfigurable processing systems. Single-Instruction Multiple-Data (SIMD) processors have recently been utilized to preprocess data at energy-constrained sensor nodes or IoT gateways, saving significant energy and bandwidth for transmission. Using traditional CPU-based systems to implement machine learning algorithms is inefficient in terms of energy consumption. In the proposed method Single-Instruction Multiple-Data (SIMD) processors are assembled by scaling the largest possible operand value subunits into direct access to the internal memory, where the carry output of each unit is conditionally fed into the next unit based on the implementation of the SIMD Processor design for Internet of Things applications. Each method has evaluated sub-operations that contribute considerably to the overall potential of the design. If the single register file can complete the intended action, a zero (one)-signal is applied to each unit's carry input. Multiplexers combine two or more adders, sending the carry signal from one unit into another if additional units are necessary to compute the sum. The outcome results compare high-speed end device techniques in terms of area and power consumption. The proposed SIMD processor-based IoT healthcare monitoring system with a MIMD processor's performance analysis of comparison clearly demonstrates that the system produces decent outcomes. The suggested system has an area overhead of 85 m2, a power usage of 4.10 W, and a time delay of 20 ns.
  • Cheque truncation mechanism using blockchain
    Venkat Reddy Kumbam, K. Kalaivani, M. Balakrishna
    Cases on Uncovering Corporate Governance Challenges in Asian Markets, 2023
    Cheque truncation system (CTS) is an image-based cheque-clearing framework. The semi-manual process has certain limitations and takes up to three working days to clear an inter-bank national cheque. Faced with the limitations of this system, cheque users and commercial banks need an efficient and secure system which can clear a cheque within less than 24 hours along with providing integrity and confidentiality to the system. All banks intending towards participating in this framework must connect towards the proposed blockchain-based system. A comprehensive framework among four primary phases—(1) paper check clearing process, (2) digital check issuing and clearing process, (3) check fraud detection process, and (4) check transaction security procedure—was presented as a solution.
  • Diagnosis of heart disease using improved genetic algorithm-based naive bayes classifier
    Kalaivani Karuppiah, Uma Maheswari N., Balamurugan N., Venkatesh R.
    Using Multimedia Systems Tools and Technologies for Smart Healthcare Services, 2022
    Heart disease is one of the most common diseases all over the world. The primary objective of this investigation is to diagnosis heart disease using hybrid classification based on NaN prediction and ANOVA test (NAN-ANOVA). The anticipated system comprises of two subsets: hybrid accelerated artificial bee colony and chicken swarm optimization algorithm (AABC-CSO) for effectual feature selection, followed by a classification technique with genetic algorithm based naive bayes classifier (GA-NBC). The first system in co-operates three stages: (i) loading the numerical value from the dataset (ii) evaluating the NaN value (iii) performing ANOVA test for efficient selection using AABC-CSO optimization algorithm. In second method, GA-NBC is proposed. The heart data set obtained from UCI machine repository, and was utilized for performing the computation. An accuracy of 61.0777%, sensitivity of 31.5868%, specificity of 67.8467%, precision of 17.9505, F-measure of 23.4050, G-mean of 46.6928 and loss of about 0.4480 was achieved according to the validation scheme.
  • Heart disease diagnosis using optimized features of hybridized ALCSOGA algorithm and LSTM classifier
    K. Kalaivani, N. Uma Maheswari, R. Venkatesh
    Network Computation in Neural Systems, 2022
  • Classification of heart disease using MFO based neural network on mri images
    Kalaivani K., Uma Maheswari N., Venkatesh R.
    Current Medical Imaging, 2021
  • Analysis on Indian stock market prediction using deep learning models
    Kalaivani Karuppiah, Umamaheswari N., Venkatesh R.
    Challenges and Applications of Data Analytics in Social Perspectives, 2020
  • Predicting disease using information integration platform for large data
    K Kalaivani, N Uma Maheswari
    2017 International Conference on Energy Communication Data Analytics and Soft Computing Icecds 2017, 2018

RECENT SCHOLAR PUBLICATIONS

  • Denoising Diffusion Probabilistic Models with Federated Learning for Privacy-Preserving Wind Power Forecasting
    K K
    Tenth International Conference on Science Technology Engineering and … , 2026
    2026
  • Optimizing distributed inference in healthcare IoT: reinforcement learning and explainable AI for dynamic neural network pruning
    GV Gopal, K Kalaivani, K Ramakrishna, S Srinivasulu, R Motupalli
    Expert Systems with Applications, 131069 , 2025
    2025
    Citations: 1
  • GENERATING OPTIMAL TEST CASES USING ELITIST GENETIC ALGORITHM
    LL Scientific
    Journal of Theoretical and Applied Information Technology 103 (8) , 2025
    2025
    Citations: 1
  • Neuro-Swarm Diffusion Reinforcement with Probabilistic Eco-Adaptive Modeling for Coastal Flood Risk Management
    HLTV Kalaivani K., Angel R.S., Abdul Basith K., ...Samreen S.
    Proceedings of 2025 10th International Conference on Science Technology … , 2025
    2025
  • Chapter 6 - Navigating the digital frontier: data privacy and security challenges in society 5.0
    B K. Kalaivani, N. Balamurugan, V.V. Parthu
    Human- Centric Integration of Next-Generation Data Science and Blockchain … , 2025
    2025
  • Deep Learning-Based Patient Monitoring System for Exercise Recommendations
    DK Kalaivani
    3rd International Conference on Optimization Techniques in the Field of … , 2025
    2025
  • A novel and secured email classification using deep neural network with bidirectional long short-term memory
    A Poobalan, K Ganapriya, K Kalaivani, K Parthiban
    Computer Speech & Language 89, 101667 , 2025
    2025
    Citations: 10
  • Microwave Bone Imaging: Simulation and Experimental Study using Monopole Antenna
    DMJ K. Ganapriya, A. Poobalan, K. Kalaivani
    Frontiers in Health Informatics 13 (3), 11199-11212 , 2024
    2024
  • Zomato Review Analysis Using Natural Language Processing
    DKKCAMBNDVV Parthu
    Journal of Engineering Education Transformations (JEET) 38 (2), 75–84 … , 2024
    2024
  • Analyzing Dueling Q-Learning for Workload Balancing in Edge Computing with Random Workload Distribution
    VN Thatha, K Kalaivani, VSN Reddy, K Ramakrishna, R Motupalli
    2024
  • Cheque Truncation Mechanism Using Blockchain
    I Venkat Reddy Kumbam (Vignana Bharathi Institute of Technology, India), K ...
    Cases on Uncovering Corporate Governance Challenges in Asian Markets … , 2024
    2024
  • Performance Improvement for Reconfigurable Processor System Design in IoT Health Care Monitoring Applications
    K Ganapriya, A Poobalan, K Kalaivani, S Gopinath
    Tehnički vjesnik 31 (1), 222-227 , 2024
    2024
  • Enhancing 5G Networks with D2D Communication: Architectures, Protocols, and Energy-Efficient Strategies for Future Smart Cities
    NR Reddy, K Kalaivani, KN Prasanthi, SM Azmal, PR Teja
    Int. J. Intell. Syst. Appl. Eng 12, 168-174 , 2024
    2024
    Citations: 4
  • Structuring Scientific Papers Using Language Elements of Style
    MMKP Dr. C Raghavendra Reddy, Dr. K Kalaivani, [Dr. V V Parthu, M. N. Sreedhar
    Tuijin Jishu/Journal of Propulsion Technology 44 (ISSN: 1001-4055), 5136-5140 , 2023
    2023
  • Patient Monitoring System Using Deep Learning Algorithms To Recommend Physical Exercise
    MB K.Kalaivani, N.Nandhini, Anantha Rao Gottimukkala, Avadhesh Kumar Dixit ...
    International Conference on Recent Trends in Data Science and its Applications , 2023
    2023
  • Diagnosis of heart disease using improved genetic algorithm-based naive bayes classifier
    R Karuppiah, K. , Uma Maheswari, N. , Balamurugan, N. , Venkatesh
    Using Multimedia Systems, Tools, and Technologies for Smart Healthcare … , 2022
    2022
    Citations: 4
  • Enhanced Deep Learning Algorithm for Tumour Prediction
    NB Dr.K.Kalaivani,Ganapriya.K, Dr.Poobalan A
    Advanced Engineering Science 54 (02), 3797-3808 , 2022
    2022
  • Heart disease diagnosis using optimized features of hybridized ALCSOGA algorithm and LSTM classifier
    NUMRV K. Kalaivani
    Network: Computation in Neural Systems , 2022
    2022
    Citations: 15
  • Analysis on Indian Stock Market Prediction Using Deep Learning Models
    NUMRV K. Kalaivani
    Challenges and Applications of Data Analytics in Social Perspectives, 324 , 2021
    2021
  • Classification of heart disease using mfo based neural network on mri images
    K Kalaivani, MN Uma, R Venkatesh
    Current Medical Imaging 17 (9), 1114-1127 , 2021
    2021
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • Heart disease diagnosis using optimized features of hybridized ALCSOGA algorithm and LSTM classifier
    NUMRV K. Kalaivani
    Network: Computation in Neural Systems , 2022
    2022
    Citations: 15
  • A novel and secured email classification using deep neural network with bidirectional long short-term memory
    A Poobalan, K Ganapriya, K Kalaivani, K Parthiban
    Computer Speech & Language 89, 101667 , 2025
    2025
    Citations: 10
  • Enhancing 5G Networks with D2D Communication: Architectures, Protocols, and Energy-Efficient Strategies for Future Smart Cities
    NR Reddy, K Kalaivani, KN Prasanthi, SM Azmal, PR Teja
    Int. J. Intell. Syst. Appl. Eng 12, 168-174 , 2024
    2024
    Citations: 4
  • Diagnosis of heart disease using improved genetic algorithm-based naive bayes classifier
    R Karuppiah, K. , Uma Maheswari, N. , Balamurugan, N. , Venkatesh
    Using Multimedia Systems, Tools, and Technologies for Smart Healthcare … , 2022
    2022
    Citations: 4
  • Smart irrigation system with iot monitoring and notification in indian agriculture
    K Kalaivani, V Vidhya, V Veerammal
    Journal of Critical Reviews 7 (14), 4055-4061 , 2020
    2020
    Citations: 4
  • Classification of heart disease using mfo based neural network on mri images
    K Kalaivani, MN Uma, R Venkatesh
    Current Medical Imaging 17 (9), 1114-1127 , 2021
    2021
    Citations: 2
  • Optimizing distributed inference in healthcare IoT: reinforcement learning and explainable AI for dynamic neural network pruning
    GV Gopal, K Kalaivani, K Ramakrishna, S Srinivasulu, R Motupalli
    Expert Systems with Applications, 131069 , 2025
    2025
    Citations: 1
  • GENERATING OPTIMAL TEST CASES USING ELITIST GENETIC ALGORITHM
    LL Scientific
    Journal of Theoretical and Applied Information Technology 103 (8) , 2025
    2025
    Citations: 1
  • Predicting disease using information integration platform for large data
    K Kalaivani, NU Maheswari
    2017 International Conference on Energy, Communication, Data Analytics and … , 2017
    2017
    Citations: 1
  • Denoising Diffusion Probabilistic Models with Federated Learning for Privacy-Preserving Wind Power Forecasting
    K K
    Tenth International Conference on Science Technology Engineering and … , 2026
    2026
  • Neuro-Swarm Diffusion Reinforcement with Probabilistic Eco-Adaptive Modeling for Coastal Flood Risk Management
    HLTV Kalaivani K., Angel R.S., Abdul Basith K., ...Samreen S.
    Proceedings of 2025 10th International Conference on Science Technology … , 2025
    2025
  • Chapter 6 - Navigating the digital frontier: data privacy and security challenges in society 5.0
    B K. Kalaivani, N. Balamurugan, V.V. Parthu
    Human- Centric Integration of Next-Generation Data Science and Blockchain … , 2025
    2025
  • Deep Learning-Based Patient Monitoring System for Exercise Recommendations
    DK Kalaivani
    3rd International Conference on Optimization Techniques in the Field of … , 2025
    2025
  • Microwave Bone Imaging: Simulation and Experimental Study using Monopole Antenna
    DMJ K. Ganapriya, A. Poobalan, K. Kalaivani
    Frontiers in Health Informatics 13 (3), 11199-11212 , 2024
    2024
  • Zomato Review Analysis Using Natural Language Processing
    DKKCAMBNDVV Parthu
    Journal of Engineering Education Transformations (JEET) 38 (2), 75–84 … , 2024
    2024
  • Analyzing Dueling Q-Learning for Workload Balancing in Edge Computing with Random Workload Distribution
    VN Thatha, K Kalaivani, VSN Reddy, K Ramakrishna, R Motupalli
    2024
  • Cheque Truncation Mechanism Using Blockchain
    I Venkat Reddy Kumbam (Vignana Bharathi Institute of Technology, India), K ...
    Cases on Uncovering Corporate Governance Challenges in Asian Markets … , 2024
    2024
  • Performance Improvement for Reconfigurable Processor System Design in IoT Health Care Monitoring Applications
    K Ganapriya, A Poobalan, K Kalaivani, S Gopinath
    Tehnički vjesnik 31 (1), 222-227 , 2024
    2024
  • Structuring Scientific Papers Using Language Elements of Style
    MMKP Dr. C Raghavendra Reddy, Dr. K Kalaivani, [Dr. V V Parthu, M. N. Sreedhar
    Tuijin Jishu/Journal of Propulsion Technology 44 (ISSN: 1001-4055), 5136-5140 , 2023
    2023
  • Patient Monitoring System Using Deep Learning Algorithms To Recommend Physical Exercise
    MB K.Kalaivani, N.Nandhini, Anantha Rao Gottimukkala, Avadhesh Kumar Dixit ...
    International Conference on Recent Trends in Data Science and its Applications , 2023
    2023