Radhakrishnan

@tagore-engg.ac.in

Professor, Department of Electronics and Communication Engineering
Tagore Engineering College

Radhakrishnan


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https://researchid.co/radhakrishnanp
, Professor in Electronics and Communication Engineering presently working in Tagore Engineering College, Chennai. He received his Ph.D Degree from Anna University. He received M.E Degree in Faculty of Information and Communication Engineering, Anna University, Chennai. He has published a number of research papers/ articles in peer review Journals. He also presented various academic as well as research-based papers at several national and International conferences. He has a teaching experience of more than 24 years and is a life member of IETE. His research areas include Signal Processing, VLSI,
Embedded systems, Image Processing and IoT.

RESEARCH, TEACHING, or OTHER INTERESTS

Multidisciplinary, Electrical and Electronic Engineering
20

Scopus Publications

145

Scholar Citations

6

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • Hybrid spectrum sensing framework for optical OTSM waveform in 5G visible light communication systems
    Megha Patil, P. Radhakrishnan, B. Manjunatha, R. Krishnamoorthy, Aziz Nanthaamornphong
    Journal of Optical Communications, 2026
    This paper presents a robust hybrid spectrum sensing framework for optical orthogonal time–space modulation (OTSM)-based 5G visible light communication (VLC) systems aimed at enhancing detection accuracy, spectral efficiency, and communication reliability under practical optical channel conditions. The proposed method integrates time-domain energy detection with delay–Doppler domain feature extraction and applies adaptive SNR-dependent decision fusion to improve sensing performance across varying noise levels. A comprehensive mathematical system model is developed considering IM/DD constraints, LED nonlinearity, multipath VLC propagation, and additive optical noise. The framework is implemented in MATLAB and evaluated using key performance metrics including probability of detection (Pd), probability of false alarm (Pfa), receiver operating characteristic (ROC), adaptive threshold behavior, bit error rate (BER), and power spectral density (PSD). Simulation results demonstrate that the proposed hybrid algorithm achieves significant SNR gains at a target detection probability and substantially reduces false alarm rates compared to ED, MF, CSD, ML-based sensing, and conventional optical OTSM approaches. At a BER of 10 −3 , considerable SNR savings are achieved, confirming enhanced reliability. Improved spectral confinement and reduced out-of-band leakage further validate efficient bandwidth utilization, making the proposed framework suitable for next-generation VLC networks.
  • Deep Learning in Analyzing Carbon Flux Patterns for Environmental Health: Remote Sensing Insights for Climate Mitigation Strategies
    Hariharan Subramani, M. Neela Harish, Selva kumar S, P. Radhakrishnan, L. N. K. Sai Madupu, Kasturi Sai Sandeep, Divya Nimma
    Remote Sensing in Earth Systems Sciences, 2025
  • Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health
    Jampani Satish Babu, Dinesh G., R. Suganya, P. Radhakrishnan, Anand Sutar, Alex David S., L. Malleswara Rao
    Remote Sensing in Earth Systems Sciences, 2025
  • A Scalable Hybrid Edge-Cloud Approach to Minimizing Latency in IoT Applications
    P. Radhakrishnan, Smitha Kurian, V. Balaji Vijayan, M. Mahabooba, Dileep Pulugu, D. Menaga
    International Journal of Computational and Experimental Science and Engineering, 2025
    The increasing reliance on IoT applications demands efficient, scalable solutions to address latency, a critical factor in time-sensitive operations. Hybrid Edge-Cloud approaches leverage the strengths of both edge and cloud computing to optimize performance and ensure seamless connectivity. However, existing methods often struggle with excessive latency due to resource allocation inefficiencies, limited edge device capabilities, and network congestion. This study proposes a Hybrid model based on Scalable Hybrid Edge-Cloud Approach (SHECA) framework, designed to mitigate these challenges in IoT applications. SHECA integrates edge computing for real-time data processing and cloud computing for storage, advanced analytics, and long-term decision-making. By dynamically distributing computational loads and leveraging intelligent resource allocation, the framework significantly reduces latency and enhances system responsiveness. The findings demonstrate that SHECA reduces average latency by 35% compared to traditional cloud-only methods, ensuring faster response times, scalability, and improved user experience in IoT applications. This hybrid solution offers a robust approach for latency minimization in diverse IoT scenarios.
  • Throughput analysis of optical NOMA waveform through RNN and CNN neural networks with 256-QAM
    Arun Kumar, P. Radhakrishnan, Ch. Raja, Aziz Nanthaamornphong
    Journal of Optical Communications, 2025
    Optical non-orthogonal multiple access (NOMA) is a critical communication technology that allows multiple users to access the same frequency spectrum at the same time, greatly improving spectral efficiency. This paper explores neural network-based encryption methods to protect NOMA systems with high throughput. In particular, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used to study and improve encryption mechanisms. CNNs learn spatial features of the data, whereas RNNs are good at modeling temporal dependencies. Simulation outcomes show that RNN-based encryption provides better performance in dynamic scenarios with increased security against eavesdropping and lower bit error rate (BER). Under a Rayleigh fading channel, RNN encryption lowers the BER at SNR to 6.1 dB, which is better than CNN (8.1 dB), MIMO-NOMA (10.2 dB), OFDM (13 dB), and OTFS (15 dB). In the same way, in Rician channel conditions, RNN attains 5 dB BER, outperforming CNN (7.1 dB), MIMO-NOMA (9.1 dB), OFDM (12.1 dB), and OTFS (10.1 dB). RNN also enhances power spectral density (PSD), reducing it to −810 dB from −700 dB of CNN, −590 dB of MIMO-NOMA, and −400 dB and −510 dB of OFDM and OTFS, respectively. These advancements emphasize the compromises between complexity, throughput, and present a promising avenue for optical NOMA systems using deep learning-based methods.
  • Efficient hybrid signal detection for optical NOMA with 512-QAM in visible light communication
    Arun Kumar, Chandrashekhara K T, P. Radhakrishnan, Aziz Nanthaamornphong
    Journal of Optical Communications, 2025
    This work presents a hybrid signal detection scheme for optical non-orthogonal multiple access (NOMA) systems using 512-QAM modulation in visible light communication (VLC) networks. As future VLC systems demand ultra-high data rates and superior spectral efficiency, they require high-order modulation like 512-QAM, which is highly susceptible to noise, nonlinearities, and channel distortions, complicating reliable detection. To tackle these challenges, a hybrid detection strategy combining traditional algorithms – such as minimum mean square error (MMSE) and zero-forcing (ZF) – with advanced machine learning techniques like deep neural networks (DNNs) is proposed. This approach harnesses the low complexity and speed of conventional detectors alongside the adaptive learning and nonlinear handling capabilities of DNNs, enabling robust detection under challenging conditions. Simulations conducted under Rayleigh fading in a MIMO-VLC setup using MATLAB 2016 extensively evaluate BER and PSD performance. Results demonstrate that the hybrid model achieves the target BER of 10 −3 with up to a 13 dB SNR improvement over traditional methods. Moreover, it ensures better spectral containment, minimizing interference and enhancing bandwidth utilization. These outcomes highlight the potential of hybrid detection schemes to realize high-capacity, energy-efficient, and reliable VLC systems, making them suitable for smart indoor environments such as wireless data hubs, augmented reality, and smart lighting in hospitals and intelligent buildings.
  • Detection of Colon Cancer Using Image Processing
    P. Radhakrishnan, A. Anbarasi, K. Srujan Raju, B. V. Sai Thrinath
    Cybernetics and Systems, 2025
    Image processing is one of the most widely used techniques in the medical field for the early diagnosis of diseases. The most common application of image processing in medicine is the detection of cancer. Colon cancer is one of the most typical cancers worldwide. Early disease detection using image processing, which is more accurate. Early cancer detection can save lives because it enables more efficient medical care for those who are affected. The suggested approach involves gathering a set of histopathological images of the colon and using extraction, filtering, and classification to distinguish between normal and abnormal images. A grayscale version of the photo is created. Filters are used to reduce image noise. Procedure for feature extraction Using K-Mean, FCM, and FFT, the values are recorded. For storage of values, it is normalized. For each calculation, the mean, variance, standard deviation, kurtosis, and deviation are determined. Through the use of a classifier, nonlinear data is transformed into linear data.
  • Autoencoder-driven PTS for power optimization of optical-NOMA waveform amplifiers
    P. Radhakrishnan, Srinivasa Rao Kandula, Abdul Hussain Sharief, Kanishk Sharma, Arun Kumar, Aziz Nanthaamornphong
    Journal of Optical Communications, 2025
    This paper proposes an autoencoder-based partial transmit sequence (AE-PTS) framework for peak-to-average power ratio (PAPR) reduction and power efficiency enhancement in optical non-orthogonal multiple access (Optical-NOMA) systems with nonlinear optical amplifiers. The encoder learns compact representations of multi-user waveforms, while a differentiable PTS module generates candidate phase-rotated subblocks. A lightweight neural selector optimally minimizes peak power, and the decoder reconstructs compliant optical waveforms under real-valued intensity and non-negativity constraints. Unlike conventional techniques, AE-PTS jointly optimizes phase selection and waveform representation, effectively reducing PAPR, mitigating nonlinear distortion, and improving BER. Extensive MATLAB simulations for 64, 128, and 256 sub-carrier optical-NOMA confirm its robustness. At a CCDF of 10 −3 , AE-PTS achieves PAPR values of 1.8 dB, 2.8 dB, and 5.2 dB, corresponding to gains of up to 10 dB over baseline optical-NOMA and 2–5 dB over conventional PTS. Capacity analysis shows AE-PTS reaching ∼200 at 50 dB SNR, compared with 100 for baseline, while training accuracy results demonstrate stable convergence across sub-carrier sizes. These simulation results establish AE-PTS as a scalable and energy-efficient solution for future optical-NOMA networks.
  • Empirical Design of a Robotic Arm Control System based on Flex Sensors with Artificial Intelligence (AI) Association
    K. Nirmala Devi, Reema Rallan, P. Radhakrishnan, Sivanesan T M, Avinash Kumar, Ch. Raja
    Proceedings 2025 5th International Conference on Expert Clouds and Applications Icoeca 2025, 2025
    The evolution of robotic control systems has significantly transformed industries ranging from manufacturing to healthcare. This paper presents an empirical design of a robotic arm control system based on flex sensors, integrated with Artificial Intelligence (AI) for enhanced precision and responsiveness. The proposed system utilizes a Hybrid CNN-RNN model for effective feature extraction and sequence learning from sensor data, followed by Support Vector Machine (SVM) classification. An AutoEncoder is employed for dimensionality reduction, optimizing the data input to the AI models. The final decision-making is executed through an ensemble of Decision Trees and Artificial Neural Networks (ANN), while Long Short-Term Memory (LSTM) networks are utilized for predictive movement control. Experimental evaluations demonstrate that the proposed model outperforms nine existing models across multiple performance metrics. The proposed system achieved an accuracy of 96.88%, with a response time of 24.3 milliseconds, and a precision rate of 96.00%. Additionally, the system exhibited remarkable robustness with only a 4.1% performance degradation under noisy conditions. The results highlight the potential of AI-assisted flex sensor-based control systems for applications requiring high precision and real-time responsiveness. This study provides a robust foundation for future advancements in robotic control systems, with potential applications in industrial automation, medical robotics, and assistive technologies.
  • DoS attack detection and hill climbing based optimal forwarder selection
    Palamalai Radhakrishnan, Senthil Kumar Seeni, Dhamotharan Rukmani Devi, Tumuluri Kanthimathi, Devadhas David Neels Ponkumar, Vikram Nattamai Sankaran, Subbiah Murugan
    Indonesian Journal of Electrical Engineering and Computer Science, 2024
    <span>Wireless networks are becoming a more and more common form of networking and communication, with several uses in many industries. However, the rising popularity has also increased security risks, such as Denial of Service (DoS) attacks. To solve these issues, Denial of Service Attack Detection and Hill Climbing (DDHC) based optimal forwarder selection in Wireless Network. The suggested method seeks to efficiently identify DoS attacks and enhance network performance by preventing the communication hiccups brought on by such attacks. Fuzzy learning method is suggested to analyze trends and find DoS threats. The node bandwidth, connectivity, packet received rate, utilized energy and response time parameters to detect the node abnormality. This abnormality decides the node's future state and detects the DoS attacker. A fuzzy learning algorithm is proposed to detect DoS attacks, which increases attack detection accuracy and lowers false alarm rates. Using the Hill Climbing (HC) procedure, the proposed system transmits data from sender to receiver. Simulation results illustrate the DDHC mechanism increases the DoS attacker detection ratio and minimizes the false positive ratio. Furthermore, it raises the network throughput and reduces the Delay in the network</span>
  • EEG-Based Brain-Computer Interfaces Using Gazelle Optimization Algorithm with Deep Learning for Motor-Imagery Classification
    K. K., , , , , , Abullaıs Nehal Ahmed, K. Kalaiarasi, Koppisetti Giridhar, S. Thenappan
    Fusion Practice and Applications, 2024
  • Internet of Things Enabled Gas Leakage Detection Over Industrial Areas using Powerful MQ Series Sensor and Controller
    Rahul Jalinder Jadhav, P. Radhakrishnan, Dattatraya Arun Jadhav, B Ashreetha, J. Divya, Saptarshi Mukherjee
    7th International Conference on Inventive Computation Technologies Icict 2024, 2024
  • IoT-Driven Innovations for Sustainable Resource Use and Waste Reduction
    C. Sharanya, P. Radhakrishnan, N. Nirmalsingh, D. R. Ashwin Kumar, S. Cloudin, M. Robinson Joel
    Sustainability Innovation and Consumer Preference, 2024
  • EXPRESSION ANALYSIS OF DEATH RECEPTOR LIGANDS USING BIOMEDICAL IMAGES
    Journal of Environmental Protection and Ecology, 2024
  • A modular IOT sensing platform using hybrid learning ability for air quality prediction
    K. Sridhar, P. Radhakrishnan, G. Swapna, R. Kesavamoorthy, L. Pallavi, R. Thiagarajan
    Measurement Sensors, 2023
  • Machine Learning based Enhanced Autonomous Driving for Autonomous Vehicles
    Lavanya M C, Akshatha M, P. Radhakrishnan, Kshatriya Vinaya Sree Bai, S. Vijayalakshmi, Jarabala Ranga
    6th International Conference on Inventive Computation Technologies Icict 2023 Proceedings, 2023
  • Detection of Parkinson Disease using Machine Learning
    J. Divya, P. Radhakrishnan, Pavithra G, Anandbabu Gopatoti, D. Baburao, R. Krishnamoorthy
    6th International Conference on Inventive Computation Technologies Icict 2023 Proceedings, 2023
  • FPGA based Power Efficient Approximate 4:2 Compressor for Multimedia Applications
    Dinesh G, Arunkumar K, P. Radhakrishnan, Rajendra D. Jadhav, Twinkle Dasari, Hasamukh Lal
    Proceedings of the 2nd International Conference on Edge Computing and Applications Icecaa 2023, 2023
  • Fpga implementation of xor-mux full adder and subtractor based truncated dct for audio processing applications
    International Journal of Advanced Science and Technology, 2020
  • FPGA implementation of XOR-MUX full adder based DWT for signal processing applications
    P. Radhakrishnan, G. Themozhi
    Microprocessors and Microsystems, 2020

RECENT SCHOLAR PUBLICATIONS

  • Hybrid spectrum sensing framework for optical OTSM waveform in 5G visible light communication systems
    M Patil, P Radhakrishnan, B Manjunatha, R Krishnamoorthy, ...
    Journal of Optical Communications , 2026
    2026
  • DiffPriv-CloudML: A Privacy-Preserving Machine Learning Framework Leveraging Differential Privacy for Secure Data Analytics in Cloud Environments
    P Radhakrishnan, R Lotus, S Suneel, BMS Rani, MSK Chaitanya, ...
    2025 International Conference on Intelligent Computing, Information and … , 2025
    2025
  • Autoencoder-driven PTS for power optimization of optical-NOMA waveform amplifiers
    P Radhakrishnan, SR Kandula, A Hussain Sharief, K Sharma, A Kumar, ...
    Journal of Optical Communications , 2025
    2025
  • Sustainable IT Governance Models using Blockchain for Transparent Decision-Making
    P Radhakrishnan, AK Gupta, M Satyavathi, MSK Chaitanya, ...
    2025 4th International Conference on Innovative Mechanisms for Industry … , 2025
    2025
  • Prompt Engineering Optimization in Multimodal GPT Systems for Real-Time Decision Making
    P Radhakrishnan, BBN Prasad, S Suneel, BE Manjunath, KN Devi, ...
    2025 4th International Conference on Innovative Mechanisms for Industry … , 2025
    2025
  • Efficient hybrid signal detection for optical NOMA with 512-QAM in visible light communication
    A Kumar, C KT, P Radhakrishnan, A Nanthaamornphong
    Journal of Optical Communications , 2025
    2025
    Citations: 2
  • Detection of colon cancer using image processing
    P Radhakrishnan, A Anbarasi, K Srujan Raju, BV Sai Thrinath
    Cybernetics and Systems 56 (5), 498-510 , 2025
    2025
    Citations: 10
  • Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health
    JS Babu, D G, R Suganya, P Radhakrishnan, A Sutar, AD S, LM Rao
    Remote Sensing in Earth Systems Sciences 8 (2), 352-364 , 2025
    2025
    Citations: 3
  • Deep learning in analyzing carbon flux patterns for environmental health: remote sensing insights for climate mitigation strategies
    H Subramani, MN Harish, S S, P Radhakrishnan, LNKS Madupu, ...
    Remote Sensing in Earth Systems Sciences 8 (2), 337-351 , 2025
    2025
    Citations: 4
  • Throughput analysis of optical NOMA waveform through RNN and CNN neural networks with 256-QAM
    CRAN Arun Kumar, P. Radhakrishnan
    Journal of Optical Communications 1 (1), 1-5 , 2025
    2025
  • Empirical Design of a Robotic Arm Control System based on Flex Sensors with Artificial Intelligence (AI) Association
    KN Devi, R Rallan, P Radhakrishnan, S TM, A Kumar, C Raja
    2025 5th International Conference on Expert Clouds and Applications (ICOECA … , 2025
    2025
  • A Scalable Hybrid Edge-Cloud Approach to Minimizing Latency in IoT Applications
    DM P. Radhakrishnan1*, Smitha Kurian2, V. Balaji Vijayan3, M. Mahabooba4 ...
    international Journal of Computational and Experimental Science and … , 2025
    2025
    Citations: 6
  • A Smart and Energy-Efficient Framework for Micro Electric IoT Applications Leveraging Deep Learning
    DPJIR Dr.S.Velmurugan, Dr.A.Thankaraj, S.Madhanmohan, P.M.Suresh, Dr.P ...
    Communications on Applied Nonlinear Analysis 32 (9), 1780-1800 , 2025
    2025
  • A logical remote sensing based disaster management and alert system using AI-assisted internet of things technology
    K Nagaiah, K Kalaivani, R Palamalai, K Suresh, V Sethuraman, ...
    Remote Sensing in Earth Systems Sciences 7 (4), 457-471 , 2024
    2024
    Citations: 15
  • EEG-Based Brain-Computer Interfaces Using Gazelle Optimization Algorithm with Deep Learning for Motor-Imagery Classification.
    P Radhakrishnan, AN Ahmed, K Kalaiarasi, K Giridhar, S Thenappan
    Fusion: Practice & Applications 16 (1) , 2024
    2024
    Citations: 5
  • Design of asymmetrically loaded dual band antenna for ISM band applications
    N Sathishkumar, S Divya, DRP Rajarathnam, PG Ayyavu, ...
    SN Computer Science 5 (5), 555 , 2024
    2024
    Citations: 2
  • Internet of things enabled gas leakage detection over industrial areas using powerful MQ series sensor and controller
    RJ Jadhav, P Radhakrishnan, DA Jadhav, B Ashreetha, J Divya, ...
    2024 International Conference on Inventive Computation Technologies (ICICT … , 2024
    2024
    Citations: 13
  • DoS attack detection and hill climbing based optimal forwarder selection
    SM Palamalai Radhakrishnan, Senthil Kumar Seeni, Dhamotharan Rukmani Devi ...
    Indonesian Journal of Electrical Engineering and Computer Science 36 (2 … , 2024
    2024
  • Machine learning-based automatic text summarization techniques
    P Radhakrishnan, G Senthil kumar
    SN Computer Science 4 (6), 855 , 2023
    2023
    Citations: 4
  • FPGA based Power Efficient Approximate 4: 2 Compressor for Multimedia Applications
    G Dinesh, K Arunkumar, DJ Rajendra
    2023 2nd International Conference on Edge Computing and Applications (ICECAA … , 2023
    2023

MOST CITED SCHOLAR PUBLICATIONS

  • FPGA implementation of XOR-MUX full adder based DWT for signal processing applications
    P Radhakrishnan, G Themozhi
    Microprocessors and Microsystems 73, 102961 , 2020
    2020
    Citations: 30
  • A modular IOT sensing platform using hybrid learning ability for air quality prediction
    K Sridhar, P Radhakrishnan, G Swapna, R Kesavamoorthy, L Pallavi, ...
    Measurement: Sensors 25, 100609 , 2023
    2023
    Citations: 24
  • Detection of parkinson disease using machine learning
    J Divya, P Radhakrishnan, A Gopatoti, D Baburao, R Krishnamoorthy
    2023 International Conference on Inventive Computation Technologies (ICICT … , 2023
    2023
    Citations: 19
  • A logical remote sensing based disaster management and alert system using AI-assisted internet of things technology
    K Nagaiah, K Kalaivani, R Palamalai, K Suresh, V Sethuraman, ...
    Remote Sensing in Earth Systems Sciences 7 (4), 457-471 , 2024
    2024
    Citations: 15
  • Internet of things enabled gas leakage detection over industrial areas using powerful MQ series sensor and controller
    RJ Jadhav, P Radhakrishnan, DA Jadhav, B Ashreetha, J Divya, ...
    2024 International Conference on Inventive Computation Technologies (ICICT … , 2024
    2024
    Citations: 13
  • Detection of colon cancer using image processing
    P Radhakrishnan, A Anbarasi, K Srujan Raju, BV Sai Thrinath
    Cybernetics and Systems 56 (5), 498-510 , 2025
    2025
    Citations: 10
  • A Scalable Hybrid Edge-Cloud Approach to Minimizing Latency in IoT Applications
    DM P. Radhakrishnan1*, Smitha Kurian2, V. Balaji Vijayan3, M. Mahabooba4 ...
    international Journal of Computational and Experimental Science and … , 2025
    2025
    Citations: 6
  • EEG-Based Brain-Computer Interfaces Using Gazelle Optimization Algorithm with Deep Learning for Motor-Imagery Classification.
    P Radhakrishnan, AN Ahmed, K Kalaiarasi, K Giridhar, S Thenappan
    Fusion: Practice & Applications 16 (1) , 2024
    2024
    Citations: 5
  • Deep learning in analyzing carbon flux patterns for environmental health: remote sensing insights for climate mitigation strategies
    H Subramani, MN Harish, S S, P Radhakrishnan, LNKS Madupu, ...
    Remote Sensing in Earth Systems Sciences 8 (2), 337-351 , 2025
    2025
    Citations: 4
  • Machine learning-based automatic text summarization techniques
    P Radhakrishnan, G Senthil kumar
    SN Computer Science 4 (6), 855 , 2023
    2023
    Citations: 4
  • Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health
    JS Babu, D G, R Suganya, P Radhakrishnan, A Sutar, AD S, LM Rao
    Remote Sensing in Earth Systems Sciences 8 (2), 352-364 , 2025
    2025
    Citations: 3
  • Battery monitoring and smart charging using Iot for electrical vehicle applications
    G Themozhi, A Prabha, P Radhakrishnan, K Manigandan
    Int J Aquat Sci 12 (03) , 2021
    2021
    Citations: 3
  • Efficient hybrid signal detection for optical NOMA with 512-QAM in visible light communication
    A Kumar, C KT, P Radhakrishnan, A Nanthaamornphong
    Journal of Optical Communications , 2025
    2025
    Citations: 2
  • Design of asymmetrically loaded dual band antenna for ISM band applications
    N Sathishkumar, S Divya, DRP Rajarathnam, PG Ayyavu, ...
    SN Computer Science 5 (5), 555 , 2024
    2024
    Citations: 2
  • Machine learning based enhanced autonomous driving for autonomous vehicles
    MC Lavanya, M Akshatha
    2023 International Conference on Inventive Computation Technologies (ICICT … , 2023
    2023
    Citations: 2
  • An IoT based Healthcare Monitoring System
    PR B. Nancharaiah, G.Chandra Sekhar
    International Journal of Pharmaceutical Negative Results, 14 (03), 127-134 , 2023
    2023
    Citations: 2
  • COVID-19 outbreak data analysis and prediction
    R Anandan, T Nalini, S Chiwhane, M Shanmuganathan, ...
    Measurement: Sensors 25, 100585 , 2023
    2023
    Citations: 1
  • Hybrid spectrum sensing framework for optical OTSM waveform in 5G visible light communication systems
    M Patil, P Radhakrishnan, B Manjunatha, R Krishnamoorthy, ...
    Journal of Optical Communications , 2026
    2026
  • DiffPriv-CloudML: A Privacy-Preserving Machine Learning Framework Leveraging Differential Privacy for Secure Data Analytics in Cloud Environments
    P Radhakrishnan, R Lotus, S Suneel, BMS Rani, MSK Chaitanya, ...
    2025 International Conference on Intelligent Computing, Information and … , 2025
    2025
  • Autoencoder-driven PTS for power optimization of optical-NOMA waveform amplifiers
    P Radhakrishnan, SR Kandula, A Hussain Sharief, K Sharma, A Kumar, ...
    Journal of Optical Communications , 2025
    2025