Geetha Murugesan Rangabai

@ponjesly.com

Professor, Electronics and Communication Engineering
Ponjesly College of Engineering

Geetha Murugesan Rangabai


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https://researchid.co/geetha028

RESEARCH, TEACHING, or OTHER INTERESTS

Control and Systems Engineering, Renewable Energy, Sustainability and the Environment, Signal Processing, Electronic, Optical and Magnetic Materials
17

Scopus Publications

120

Scholar Citations

6

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Optimizing Crop Yield Prediction and Suitability using Reinforcement Learning and Context Aware Multi-Channel Networks in IoT-Enabled Smart Agriculture
    J. Jencewin, M. R. Geetha
    Water Resources Management, 2026
  • Hybrid Optimization Algorithm_Dense ResNeXt Fused Deep Stacked Autoencoder for Wormhole Attack Mitigation on Network Control System
    Alexander Avina, Murugesan Rangabai Geetha, Thangaraj Rajesh, Murugesan Rangabai Kavitha
    Journal of Systems Science and Systems Engineering, 2026
  • ResNeXt and Deep Stacked Autoencoder based framework for wormhole attack detection on network control system
    Avina A, Geetha M. R
    Discover Computing, 2025
    Network Control Systems (NCS), including Wireless Sensor Networks (WSN) are broadly deployed across different application areas. A prominent dispute in WSNs is the liability to wormhole attacks, which lead to routing errors, degradation of sensor network lifetime, and disruption of network topology. Despite the development of numerous Wormhole attack detection methods, many of these techniques require additional hardware or consume significant system resources, limiting their practicality. This paper introduces a novel detection framework leveraging the ResNeXt architecture in conjunction with a Deep Stacked Autoencoder (ResNeXt-DSAE) for effective wormhole attack detection in NCSs. The framework begins with the simulation of a WSN, where routing is functioned using the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. The detection process is structured into multiple phases, starting with the evaluation of the Neighbor Ratio Threshold (NRT), followed by the wormhole attack detection through out-of-band and in-band detection strategies. In the out-of-band detection phase, the transmission range is analyzed, while in-band detection assesses Round-Trip Time (RTT) and Packet Delivery Ratio (PDR). The wormhole attack classification is subsequently performed using the ResNeXt-DSAE framework to distinguish between out-of-band and in-band attacks. Investigational outcomes demonstrate that the devised ResNeXt-DSAE framework accomplishes superior proficiency, with a maximum throughput of 97.55 Mbps, Packet Deliver Ratio (PDR) of 99.58%, network lifetime of 0.945, and a minimal delay of 0.815 s, network activity energy consumption of 0.255 J, and computational cost of 17.99 s for 200 nodes. Furthermore, the proposed method attains a detection rate of 0.958, an accuracy of 0.958, a precision of 0.940, a recall of 0.970, an F1-score of 0.955, a False Positive Rate (FPR) of 0.058, and a False Negative Rate (FNR) of 0.077 thereby transcending existing wormhole attack detection approaches.
  • HA-ESNet: A Hierarchical Attention With Echo State Network-Based Dynamic Low-Complexity Channel Estimation in FSO Communication Links Under Turbulent Channel Conditions
    M. R. Kavitha, M. R. Geetha, T. Rajesh
    Transactions on Emerging Telecommunications Technologies, 2025
    In today's rapidly evolving communication landscape, free space optical (FSO) communication systems face significant challenges when operating under atmospheric turbulence conditions. The specific characteristics of gamma–gamma turbulence introduce signal fading, scintillation, and potential link failures, impacting the reliability and performance of data transmission. To ensure high‐quality and reliable communication in such challenging environments, there is a critical need for low‐complexity parameter estimation techniques with low bit error rate (BER) and mean square error (MSE). Addressing these challenges, this paper proposes a low‐complexity channel estimation design named hierarchical attention echo state network (HA‐ESNet) model over gamma–gamma turbulence channels in FSO communications. The HA‐ESNet model leverages advanced deep learning techniques, attention mechanisms, and the echo state network (ESN) architecture to enhance parameter estimation accuracy and robustness. The hierarchical attention mechanism allows the network to selectively focus on informative channel characteristics while suppressing noise and irrelevant information. This selective attention enables the model to prioritize critical features and adapt to changing channel conditions effectively. The HA‐ESNet model's unique architecture combines the benefits of hierarchical attention mechanisms and ESN components to optimize signal transmission, adapt to channel variability, and improve training efficiency. By capturing the nonlinear dynamics of FSO channels through reservoir computing with echo state properties, the HA‐ESNet model can effectively model and adapt to the complex turbulence‐induced dynamics. Simulation results demonstrate the strong performance of the HA‐ESNet model in estimating parameters over turbulent FSO channels. The model achieves low BER, low MSE, and minimal computational complexity, showcasing its robustness and adaptability in capturing the dynamics of turbulent channels. The innovative approach of HA‐ESNet significantly enhances the reliability and performance of FSO communication systems in challenging atmospheric conditions, offering a promising solution for improving data transmission in FSO networks.
  • Resilient adaptive polarized optical transmission for mitigating atmospheric turbulence in free space optics communication
    M.H. Anit Monisha, M.R. Geetha
    Optik, 2025
  • Mitigating the Impact of Lognormal Atmospheric Turbulence Channel Estimation on FSO Communication Systems Using Advanced Deep Learning Modules
    M. R. Kavitha, M. R. Geetha, T. Rajesh
    International Journal of Communication Systems, 2025
    Currently, researchers and commercial entities worldwide are highly interested in optical wireless communication links operating in terrestrial environments. These links offer the distinct advantage of enabling high‐speed data transmission while keeping operational and installation costs low, all without requiring licensing. Free‐space optical (FSO) communication has established itself as a reliable method for delivering ultra‐high data‐rate services. However, the presence of atmospheric turbulence‐induced fading poses a significant challenge for FSO communication systems, particularly when operating over channels affected by time‐varying turbulence. The effectiveness of FSO systems is greatly influenced by atmospheric conditions in a specific area because the laser beam propagates through the atmosphere. As a consequence, this can lead to a significant degradation such as high complexity, high estimation error, and high bit error rate (BER) in the performance of FSO systems. This paper presents a stacked dual attention LSTM network with layered neural Turing machine (SDALSTM‐LaNTM) for FSO channel estimation over lognormal turbulence‐induced fading channels. In this approach, the stacked structure ensures a robust feature extraction capacity of SDALSTM and enhances its ability to capture intricate relationships among multiple atmospheric variables. After employing the stacked LSTM networks, the extracted features are combined with the augmented features obtained through the dual attention (DAttn) mechanism by concatenation. Then the LaNTM architecture used external memory to solve the channel parameter estimation problem and update new set of hidden features for estimation task. The ability to access and update memory enables the model to capture long‐term dependencies and context, making it beneficial for channel prediction in FSO systems. Presently, we are in the process of verifying the channel estimation model using measured FSO channel data. Simulation results clearly demonstrate the outstanding estimation performance of the SDALSTM‐LaNTM model in FSO systems, specifically when dealing with lognormal turbulence‐induced fading channels. Moreover, we highlight the effectiveness of the SDALSTM‐LaNTM model by conducting a comprehensive comparison with existing models, considering factors such as average capacity performance, outage probability estimation, and BER.
  • Knowledge-based adaptive routing for energy efficiency and attack detection in ad hoc wireless sensor networks
    M. Joselin Kavitha, M.R. Geetha, R. Isaac Sajan
    Computer Networks, 2025
    Ad hoc wireless sensor networks (WSNs) are susceptible to active attacks due to their dynamic and self-organizing nature. Existing ad hoc WSN secure routing consumes excessive energy and cannot effectively counter active attacks. Thus, an energy-efficient, attack-detecting routing solution is required. This research introduces a novel Knowledge-Based Route Mutation (KBRM) mechanism for real-time security and adaptability in ad hoc WSNs. KBRM employs a two-level reinforcement learning process to provide immediate decision-making for attack detection and defense. It adapts to changing network conditions, reducing energy consumption , and enhancing security. The research presents a unified mathematical model for selecting attack strategies and imposes constraints on energy consumption, security, quality of service , and adaptability. A Predictive Context-Aware Defense mechanism utilizes custom context variables, dynamic accuracy estimation, and predictive threat assessment for improved attack detection. The extended Q-learning algorithm integrates node mobility and the state of neighboring nodes, enabling adaptive route mutation while balancing security and energy efficiency. The research offers a comprehensive approach to enhancing the security and adaptability of ad hoc WSNs. Eventually, comprehensive experimental outcomes demonstrate the efficiency of our approach compared to other solutions.
  • An efficient secure cryptosystem using improved identity based encryption with multimodal biometric authentication and authorization in cloud environments
    R. Megiba Jasmine, J. Jasper, M. R. Geetha
    Wireless Networks, 2025
  • Block based motion estimation model using CNN with representative point matching algorithm for object tracking in videos
    C.K. Suryaraj, M.R. Geetha
    Expert Systems with Applications, 2024
  • Wall-Cor Net: wall color replacement via Clifford chance-based deep generative adversarial network
    M. Sabitha Preethi, M. R. Geetha, T. Jaya, T. Rajesh
    Signal Image and Video Processing, 2024
  • CAVIaR crayfish algorithm enabled Deep Kronecker Network for Wormhole attack mitigation on Network Control System
    Avina. A, M.R. Geetha
    2024 5th International Conference for Emerging Technology Incet 2024, 2024
  • Retinal blood vessel segmentation using root Guided decision tree assisted enhanced Fuzzy C-mean clustering for disease identification
    Balraj Sindhusaranya, Murugesan Rangabai Geetha
    Biomedical Signal Processing and Control, 2023
  • PID controller based current sharing in Luo converter for standalone photovoltaic system
    T. Rajesh, E. Leelavathi, MR Geetha, MR Kavitha
    Aip Conference Proceedings, 2022
  • Hybrid algorithm for retinal blood vessel segmentation using different pattern recognition techniques
    B. Sindhusaranya, M.R. Geetha, T. Rajesh, M.R. Kavitha
    Journal of Intelligent and Fuzzy Systems, 2022
  • Brain tumor detection using optimisation classification based on rough set theory
    T. Rajesh, R. Suja Mani Malar, M. R. Geetha
    Cluster Computing, 2019
  • MIMO FSO system using Gamma-Exponential channel model
    Kavitha MR, Geetha MR, Heleena J, Shamona G Bert
    2019 International Conference on Recent Advances in Energy Efficient Computing and Communication Icraecc 2019, 2019
  • ANFIS based MPPT and load regulation in paralleled connected LUO converters for standalone photovoltaic system
    International Journal of Applied Engineering Research, 2015

RECENT SCHOLAR PUBLICATIONS

  • Hybrid Optimization Algorithm_Dense ResNeXt Fused Deep Stacked Autoencoder for Wormhole Attack Mitigation on Network Control System
    A Avina, MR Geetha, T Rajesh, MR Kavitha
    Journal of Systems Science and Systems Engineering, 1-27 , 2026
    2026
  • Optimizing Crop Yield Prediction and Suitability using Reinforcement Learning and Context Aware Multi-Channel Networks in IoT-Enabled Smart Agriculture
    J Jencewin, MR Geetha
    Water Resources Management 40 (7), 274 , 2026
    2026
  • HA‐ESNet: A Hierarchical Attention With Echo State Network‐Based Dynamic Low‐Complexity Channel Estimation in FSO Communication Links Under Turbulent Channel Conditions
    MR Kavitha, MR Geetha, T Rajesh
    Transactions on Emerging Telecommunications Technologies 36 (9), e70236 , 2025
    2025
  • Turbulence-resilient multi-adaptive optical model for high-performance wireless transmission in FSO communication under varying turbulence environments
    MHA Monisha, MR Geetha, MR Kavitha, T Rajesh
    Optical and Quantum Electronics 57 (8), 486 , 2025
    2025
    Citations: 2
  • Mitigating the Impact of Lognormal Atmospheric Turbulence Channel Estimation on FSO Communication Systems Using Advanced Deep Learning Modules
    MR Kavitha, MR Geetha, T Rajesh
    International Journal of Communication Systems 38 (4), e6023 , 2025
    2025
    Citations: 1
  • Knowledge-based adaptive routing for energy efficiency and attack detection in ad hoc wireless sensor networks
    MJ Kavitha, MR Geetha, RI Sajan
    Computer Networks 259, 111086 , 2025
    2025
    Citations: 7
  • An efficient secure cryptosystem using improved identity based encryption with multimodal biometric authentication and authorization in cloud environments
    RM Jasmine, J Jasper, MR Geetha
    Wireless Networks 31 (1), 545-565 , 2025
    2025
    Citations: 8
  • Block based motion estimation model using CNN with representative point matching algorithm for object tracking in videos
    CK Suryaraj, MR Geetha
    Expert Systems with Applications 255, 124407 , 2024
    2024
    Citations: 8
  • Wall-Cor Net: wall color replacement via Clifford chance-based deep generative adversarial network
    MS Preethi, MR Geetha, T Jaya, T Rajesh
    Signal, Image and Video Processing 18 (5), 4075-4084 , 2024
    2024
    Citations: 2
  • CAVIaR crayfish algorithm enabled deep Kronecker network for wormhole attack mitigation on network control system
    MRG A Avina
    2024 International conference for emerging technology, 1-6 , 2024
    2024
    Citations: 2
  • Retinal blood vessel segmentation using root Guided decision tree assisted enhanced Fuzzy C-mean clustering for disease identification
    B Sindhusaranya, MR Geetha
    Biomedical Signal Processing and Control 82, 104525 , 2023
    2023
    Citations: 15
  • Hybrid algorithm for retinal blood vessel segmentation using different pattern recognition techniques
    B Sindhusaranya, MR Geetha, T Rajesh, MR Kavitha
    Journal of Intelligent & Fuzzy Systems 43 (6), 7605-7615 , 2022
    2022
    Citations: 3
  • An Artificial Intelligence Based Technique To Assist Paramedical Staffs And Monitor Their Work
    VT R.MEGIBA JASMINE,DR.J.JASPER,DR.M.R.GEETHA,S BERLIN SHAHEEMA,V.G. ANISHA ...
    2022
  • PID controller based current sharing in Luo converter for standalone photovoltaic system
    T Rajesh, E Leelavathi, MR Geetha, MR Kavitha
    AIP Conference Proceedings 2385 (1), 060003 , 2022
    2022
  • PID controller based current sharing in Luo converter for standalone photovoltaic system
    KMR Rajesh T, Leelavathi, Geetha MR
    2022
  • Current programmed transfer function model of Luo Converters for Standalone Photovoltaic System
    T Rajesh, RS Ambika, MR Kavitha, MR Geetha
    Annals of the Romanian Society for Cell Biology 25 (6), 10608-10619 , 2021
    2021
  • Brain tumor detection using optimisation classification based on rough set theory
    T Rajesh, RSM Malar, MR Geetha
    Cluster computing 22 (Suppl 6), 13853-13859 , 2019
    2019
    Citations: 52
  • MIMO FSO system using Gamma-Exponential channel model
    MR Kavitha, MR Geetha, J Heleena, GB Shamona
    2019 International Conference on Recent Advances in Energy-efficient … , 2019
    2019
    Citations: 1
  • Current sharing in paralleled connected luo converters for standalone photovoltaic system
    MRGMR Geetha, RSM Malar, TAT Ahilan
    Journal of Electrical Engineering 17 (1), 10-10 , 2017
    2017
    Citations: 1
  • Current sharing in parallel connected boost converters
    MR Geetha, RSM Malar, T Ahilan
    The Journal of Engineering 2016 (12), 444-452 , 2016
    2016
    Citations: 16

MOST CITED SCHOLAR PUBLICATIONS

  • Brain tumor detection using optimisation classification based on rough set theory
    T Rajesh, RSM Malar, MR Geetha
    Cluster computing 22 (Suppl 6), 13853-13859 , 2019
    2019
    Citations: 52
  • Current sharing in parallel connected boost converters
    MR Geetha, RSM Malar, T Ahilan
    The Journal of Engineering 2016 (12), 444-452 , 2016
    2016
    Citations: 16
  • Retinal blood vessel segmentation using root Guided decision tree assisted enhanced Fuzzy C-mean clustering for disease identification
    B Sindhusaranya, MR Geetha
    Biomedical Signal Processing and Control 82, 104525 , 2023
    2023
    Citations: 15
  • An efficient secure cryptosystem using improved identity based encryption with multimodal biometric authentication and authorization in cloud environments
    RM Jasmine, J Jasper, MR Geetha
    Wireless Networks 31 (1), 545-565 , 2025
    2025
    Citations: 8
  • Block based motion estimation model using CNN with representative point matching algorithm for object tracking in videos
    CK Suryaraj, MR Geetha
    Expert Systems with Applications 255, 124407 , 2024
    2024
    Citations: 8
  • Knowledge-based adaptive routing for energy efficiency and attack detection in ad hoc wireless sensor networks
    MJ Kavitha, MR Geetha, RI Sajan
    Computer Networks 259, 111086 , 2025
    2025
    Citations: 7
  • Hybrid algorithm for retinal blood vessel segmentation using different pattern recognition techniques
    B Sindhusaranya, MR Geetha, T Rajesh, MR Kavitha
    Journal of Intelligent & Fuzzy Systems 43 (6), 7605-7615 , 2022
    2022
    Citations: 3
  • Turbulence-resilient multi-adaptive optical model for high-performance wireless transmission in FSO communication under varying turbulence environments
    MHA Monisha, MR Geetha, MR Kavitha, T Rajesh
    Optical and Quantum Electronics 57 (8), 486 , 2025
    2025
    Citations: 2
  • Wall-Cor Net: wall color replacement via Clifford chance-based deep generative adversarial network
    MS Preethi, MR Geetha, T Jaya, T Rajesh
    Signal, Image and Video Processing 18 (5), 4075-4084 , 2024
    2024
    Citations: 2
  • CAVIaR crayfish algorithm enabled deep Kronecker network for wormhole attack mitigation on network control system
    MRG A Avina
    2024 International conference for emerging technology, 1-6 , 2024
    2024
    Citations: 2
  • Mitigating the Impact of Lognormal Atmospheric Turbulence Channel Estimation on FSO Communication Systems Using Advanced Deep Learning Modules
    MR Kavitha, MR Geetha, T Rajesh
    International Journal of Communication Systems 38 (4), e6023 , 2025
    2025
    Citations: 1
  • MIMO FSO system using Gamma-Exponential channel model
    MR Kavitha, MR Geetha, J Heleena, GB Shamona
    2019 International Conference on Recent Advances in Energy-efficient … , 2019
    2019
    Citations: 1
  • Current sharing in paralleled connected luo converters for standalone photovoltaic system
    MRGMR Geetha, RSM Malar, TAT Ahilan
    Journal of Electrical Engineering 17 (1), 10-10 , 2017
    2017
    Citations: 1
  • Control of current in parallel connected converters for standalone photovoltaic system
    MR Geetha, RSM Malar, T Ahilan
    International journal of advanced engineering technolog 7, 1040-1048 , 2016
    2016
    Citations: 1
  • Current Sharing in parallel connected converters with indirect duty ratio adjustment for photovoltaic system
    MR Geetha, T Ahilan, RSM Malar
    RevistaTécnica de la Facultad de Ingeniería Universidad del Zulia 39, 1-12 , 2016
    2016
    Citations: 1
  • Hybrid Optimization Algorithm_Dense ResNeXt Fused Deep Stacked Autoencoder for Wormhole Attack Mitigation on Network Control System
    A Avina, MR Geetha, T Rajesh, MR Kavitha
    Journal of Systems Science and Systems Engineering, 1-27 , 2026
    2026
  • Optimizing Crop Yield Prediction and Suitability using Reinforcement Learning and Context Aware Multi-Channel Networks in IoT-Enabled Smart Agriculture
    J Jencewin, MR Geetha
    Water Resources Management 40 (7), 274 , 2026
    2026
  • HA‐ESNet: A Hierarchical Attention With Echo State Network‐Based Dynamic Low‐Complexity Channel Estimation in FSO Communication Links Under Turbulent Channel Conditions
    MR Kavitha, MR Geetha, T Rajesh
    Transactions on Emerging Telecommunications Technologies 36 (9), e70236 , 2025
    2025
  • An Artificial Intelligence Based Technique To Assist Paramedical Staffs And Monitor Their Work
    VT R.MEGIBA JASMINE,DR.J.JASPER,DR.M.R.GEETHA,S BERLIN SHAHEEMA,V.G. ANISHA ...
    2022
  • PID controller based current sharing in Luo converter for standalone photovoltaic system
    T Rajesh, E Leelavathi, MR Geetha, MR Kavitha
    AIP Conference Proceedings 2385 (1), 060003 , 2022
    2022