G R SURESH

@saveetha.com

Professor, ECE
Saveetha School of Engineering



                 

https://researchid.co/sureshgr
25

Scopus Publications

463

Scholar Citations

7

Scholar h-index

10

Scholar i10-index

Scopus Publications



  • Wrapper-based Feature Selection for Enhanced Intrusion Detection Using Random Forest Classification
    Polasi Sudhakar, Durga Prasanna N, Sreedhar Bhukya, Mohammad Azhar, G R Suresh, and Mohan Ajmeera
    IEEE
    In network environments, detecting and mitigating cyber-attacks necessitates the creation of an effective Intrusion Detection System. This research describes an IDS framework that combines Random Forest (RF) classification and a wrapper-based feature selection strategy to increase performance. The wrapper-based strategy iteratively picks the most relevant features by lowering dimensionality and removing redundant or irrelevant data, increasing the RF classifier's efficiency. The framework is assessed against three well-known benchmark datasets: NSL-KDD, CICIDS-2019, and Bot-IoT. Experimental results show that when used in conjunction with wrapper-based feature selection, the RF classifier improves detection accuracy, precision, and recall significantly. This solution handles the complexity and variety of modern network traffic, resulting in a more accurate and efficient IDS than older systems.

  • Experimental Evaluation of Identifying Fraudulent Activities in E-Commerce to Protect Consumers by Using Intelligent Deep Learning Methodology
    Suganya B, Devipriya C, Suresh G R, Vinotha Vasuki A, Rajkumar Chadge, and A. Al Sherideh
    IEEE
    To make use of and analyze the vast quantities of data produced by online transactions, advanced cyber-infrastructure and information technology approaches are required. In this research, we provide a big data platform that online stores may use to address a number of problems plaguing the e-commerce sector. Businesses and individuals alike are susceptible to fraud, a global issue. Deep Learning and AI have been tremendous assets in the fight against fraud in today's tech-driven world. Electronic wallets and credit cards were among the first digital payment options offered with the rise of online marketplaces. The goal of implementing a fraud detection system is to identify and prevent fraudulent transactions that involve stolen or unverified credit card information. With the use of a deep learning algorithm, this work intends to offer substantial assistance, leading to the creation of a new model known as the Deep Regression Classification Model (DRCM). To assess the efficacy of the suggested model, it is cross-validated with the typical learning method known as Naive Bayes (NB). Electronic commerce platforms may improve their security posture, increase user trust and confidence, and reduce financial losses by implementing AI-powered fraud protection methods. This groundbreaking method represents a sea change in the fight against online fraud and highlights the revolutionary power of AI to protect consumers and companies. A system that employs an effective deep learning technique for precise fraud detection will be conceptualized and developed based on the results of the experimental assessment.

  • A Robust Artificial Intelligence Enabled Methodology to Predict Online Consumers Behaviour Using Hybrid Deep Learning Strategy
    Sophia P, G R Suresh, Kiruthika B, Juliet N, R Chadge, and Ala'a Al Sherideh
    IEEE
    Marketing and consumer behavior predictions are two areas where Artificial Intelligence (AI) is finding widespread use. Evaluating the accuracy of AI -based consumer behavior prediction is the focus of this article. Evidence from research in this area suggests that AI can help sift through mountains of data pertaining to customer performance. It includes looking at things like customer surveys, purchasing history, and behaviour. Furthermore, this sector may make use of deep learning techniques for consumer behavior forecasting. All sorts of advertising and marketing choices can benefit from this data. The Hybrid Learning for Behavioral Prediction (HLBP) method, first proposed in this study, integrates AI -assisted learning with categorization logic for processing. In order to assess the efficacy of the suggested program, this model is cross-validated with the traditional learning model known as Recurrent Neural Network (RNN). Even more encouraging is the possibility that AI can aid businesses in fine-tuning their advertising strategies. As a result, advertising may be more precisely targeted, and marketing budgets can be more efficiently used. Here, AI in a marketing context may provide customized experiences for consumers, such making better product recommendations and enhancing the shopping experience overall. In order to maximize the effectiveness of online advertising efforts, artificial intelligence can be employed. This research delves into the potential of artificial neural networks to identify customer behavior utilizing data collected from conventional surveys. Neural networks outperform classical discriminant analysis in most cases, demonstrating their strong discriminate potential.

  • An Effective Customer Segmentation Approach to Improve E-Commerce Platform Quality Based on Intensive Learning Methodology
    G R Suresh, Vinoparkavi D, Suresh Govindasamy, Rohini S, Jayant Giri, and M. Omar Sabri
    IEEE
    To accomplish company goals and reap advantages while satisfying and meeting consumer expectations, segmentation is an essential facilitator. Identifying subgroups of the target market that share common traits, requirements, and priorities is customer segmentation's primary goal. Various dataset dimensionalities are also considered, as are changes across time. In this study, we use time-based segmentation and clustering approaches to the e-commerce industry to conduct predictive neural network analysis using factors such as product reviews, goods, buying trend, watching pattern, and more. In this research, Intensive Neural Classification Learning (INCL) is presented as a novel deep learning technique. To assess its efficacy, it is cross-validated with the traditional Neural Network (NN) learning algorithm. We have evaluated unigrams, bigrams, and trigrams as part of the feature extraction process and identified the unique brands per consumer. In addition, the customer's chosen brands may be determined using a neural network, and the projected data can then be utilized for statistical analysis. We all know that sentiment analysis and categorization are huge help when trying to guess how customers feel about certain companies and items. Lastly, using the provided set of input data, we will choose the top brands by assessing the accuracy of the forecasts.


  • Detection in Dermoscopic Images with Inadequate Control of Blue-White Structures
    P. Bindhu., G. R. Suresh, S. Rajalakshmi, C. Selvi, and S. Prakash
    AIP Publishing

  • Optimization-enabled deep convolutional network for the generation of normal speech from non-audible murmur based on multi-kernel-based features
    T. Rajesh Kumar, G. R. Suresh, and K. Kalaiselvi
    World Scientific Pub Co Pte Ltd
    Automatic Speech Recognition schemes are the alternating modes in which individuals interrelate with various mobile application applications. The user interactivity needs are a huge vocabulary identification system, high accuracy, energy-efficient solution and time. Although the automatic Speech Recognition system requires power budget and huge memory bandwidth, it is not applicable for many tiny forms of battery-controlled devices. Hence, an effective method is developed using the proposed Multi-kernel Perceptual linear predictive and Stochastic Biogeography-based whale optimization algorithm optimization to adapt non-audible mutter to regular speech. First, the input speech signal is initially given to the pre-processed module. Then, the features, such as spectral centroid, pitch chroma, Taylor amplitude modulation spectrogram (AMS), spectral skewness, and the developed Multi-kernel Perceptual linear predictive, are extracted to determine the appropriate features. After extracting features, the speech recognition is performed based on Deep Convolutional Neural Network, which is trained by the proposed Stochastic Biogeography whale optimization algorithm. The Stochastic Biogeography whale optimization algorithm combines the stochastic gradient descent method, whale optimization algorithm, and biogeography-based optimization. The developed model showed improved results with maximum accuracy of 0.985, minimal FPR of 0.001, maximal TPR of 1, respectively.

  • Wearable Sensor-Based Human Exhalation Rhythm Recognition using Deep Learning Neural network
    S. Jaanaa Rubavathy, G R Suresh, C. Senthilkumar, P. Shyamala Bharathi, and V. Amudha
    IEEE
    Respiratory rhythms are critical in a variety of medical emergencies. Respiratory rhythms of clinical relevance may be detected using a non-invasive respiratory analysis device established in this study. Light-weight wireless sensor nodes attached to the chest and belly of 150 healthy participants were used to gather data on controlled breathing. We next created our own datasets by infusing portions of quiet inhalation with annotated samples of different patterns. For each test datasets, with the one deep neural network has been used to locate the position of every occurrence and categorize it as corresponding to the one of the above-mentioned event kinds. For quiet inhalation, we got a mean F1 score of 93%, for central sleep apnea. Supervised learning may be used to interpret the data through sensing devices, such as chest and abdominal movement, to give a nonintrusive system to measure respiratory rate. Recognizing apneas when sleeping at night and measuring respiratory episodes in ventilated patients medical and surgical patients unit might benefit from this technology.

  • DTKFA: Limiting the amount of needless file transfers by predicting the data gathering requirements of sensor nodes
    V. Amudha, S. Jaanaa Rubavathy, G R Suresh, C. Senthilkumar, and P. Shyamala Bharathi
    IEEE
    Wireless sensor networks have a substantial challenge in the form of excessive energy consumption brought on by the transmission of superfluous data (WSNs). The resolution of this issue results in an increase in the lifespan of any network and an improvement in the practicality of using the system for implementations. Expanding on this, a demand for electricity data gathering is becoming increasingly important for wireless sensor network applications that consist of low-powered sensing devices. In such situations, information grouping and modeling techniques that make use of symmetrical correlation inside the various sensors may be employed for the purpose of decreasing the amount of power that detectors need in order to continue collecting stored information. In this study, we propose a hybrid model called DTKFA that is premised on autoregressive integrated moving average, decision tree and Kalman filtering techniques. The aim of this process is to anticipate the data gathering requirements of sensor nodes in order to cut down on unneeded information transfer. Segmentation and information consolidation to every source node are applied in the WSNs in order to make appropriate sampling forecasts in an effective manner. This is primarily done in order to decrease the computational overheads that are generated by the forecasting model. The results of experimental tests, similarities, and job evaluation carried out in a variety of contexts demonstrate that now the accuracy rate of with us methodology could indeed keep growing Gaussian and probabilistic based models should give higher energy proficiency due to a reduction in the amount of data transmission. This is possible because our method reduces the number of packets that need to be sent.

  • Physical Fitness and Exercise to motivate the golden-ager Using Body Area Networks
    P. Shyamala Bharathi, V. Amudha, S. Jaanaa Rubavathy, G R Suresh, and C. Senthilkumar
    IEEE
    In light of the fact that the global population as a whole is becoming older and the effects that this will have, it is essential to ensure that people of retirement age may continue to be active and keep their sense of autonomy for a longer period of time. The purpose of this Physical Fitness system (PFS) is to encourage strolls across the city as a means of encouraging lives that are more healthy and active. Body local are used to combine data acquired by various types of sensors, which is then transferred to a server to enable careers in making educated choices while arranging physical activities for the people they are caring for. In this paper, the Physical Fitness system is described, and a summary is given on a test design that was constructed in order to evaluate the effectiveness of the currently deployed version and the important structures that make up that application. According to the findings, the Physical Fitness device has a strong performance in order to adequately to rebound from connection failures, its ability to upload data, the amount of battery life it consumes, and the electromagnetic range of its personal area network.

  • Conversion of NAM to Normal Speech Based on Stochastic Binary Cat Swarm Optimization Algorithm
    T. Rajesh Kumar, G. N. Balaji, D. Vijendra Babu, Soubraylu Sivakumar, K. Kalaiselvi, and G. R. Suresh
    Springer Nature Singapore

  • False alarm detection using dynamic threshold in medical wireless sensor networks
    S. Saraswathi, G. R. Suresh, and Jeevaa Katiravan
    Springer Science and Business Media LLC

  • Design of Pentacene-Based Organic Field-Effect Transistor for Low-Frequency Operational Transconductance Amplifier
    Cross T. Asha Wise, G. R. Suresh, M. Palanivelen, and S. Saraswathi
    World Scientific Pub Co Pte Lt
    Mounting electronics circuits on a plastic flexible substrate are pertinent for biosensing applications due to their resilient nature, minimal processing conditions, lightweight and low cost. Organic Field-Effect Transistors (OFET)-based amplifier for flexible biosensors have been proposed in this paper. To design flexible biosensing circuits, Metal Oxide Semiconductor Field-Effect Transistor (MOSFET) with Polycyclic Hydrocarbon is a suitable choice. It is a big challenge to build an organic circuit using graphene electrode due to its poor performance of [Formula: see text]-type OFET, therefore it is advisable to use Pentacene as [Formula: see text]- and [Formula: see text]-type Organic semiconductors. Pentacene being one among the foremost totally investigated conjugated organic molecules with a high application potential because the hole mobility in OFETs goes up to 0.2[Formula: see text]cm2/(Vs), which exceeds that of amorphous silicon. In biosignal process, the first and most important step is to amplify the biosignal for further processing. Operational Transconductance Amplifier (OTA) plays an essential role in biological signal measuring instruments like EEG, ECG, EMG modules which measure the heart, muscle and brain activities. The OTA designed using this OFET is adaptable for flexible sensor circuits and also it derives the transconductance of 67 which is similar to silicon OTA. The amplifier designed here gives unit gain of 42[Formula: see text]dB with a frequency of 195[Formula: see text]Hz which is suitable for low-frequency biosignal processing applications.

  • Taylor-AMS features and deep convolutional neural network for converting nonaudible murmur to normal speech
    T. Rajesh Kumar, G. R Suresh, S. Kanaga Subaraja, and C. Karthikeyan
    Wiley

  • Decision Support System for Diabetes using Tongue Images
    A. Selvarani and G. R. Suresh
    IEEE
    The tongue is a major part of the human body to taste, speak and swallow the food. Tongue portion is directly connected with our internal organ. If any problem in that it reflects the effect through the tongue. Tongue center portion is connected with stomach, pancreas. Side portions are connected with liver. Tongue tip is connected with the heart, etc. In this study, an efficient Decision Support System is used to diagnosis the diabetes based on Characterization of tongue images by analyzing tongue colour distribution and texture for diabetic patient and healthy person. Comparative analysis of tongue image of DM and Healthy Person by using Kernel Ensemble Classification (KEC) method. Also Show the lab values of Diabetes.

  • INTELLIGENT WEARABLE DEVICE for EARLY DETECTION of MYOCARDIAL INFARCTION USING IoT
    V. Kaviya and G.R. Suresh
    IEEE
    Heart attacks take the maximum toll of human life all over the world. Heart disease is reportedly killing approximately 17 million people in the world, and a similar scene is seen in india, where 3 million people die because of CVDs (cardio-vascular diseases), which include heart attack and stroke. Patients suffering from heart diseases need a earlier detection system to have a urgent treatment before it's too late. More over some existing methodology helps in detecting heart attack only if the patient is under doctor or expert observation and hence it is not possible for all the cases. To overcome the above disadvantage we have developed a system which will help to provide earlier detection of myocardial infarction, which avoid overdue of medical treatment. The proposed system is combined with a data collection and computational algorithm which can collects the multiple data continuously, according to the health risk conscious in each patient. In case of heart attack the system sends alert to person device and doctor or medical assistance through GSM. So, that medical assistant can reach the patient with the help of GPS location and provide early medical treatment.

  • An efficient approach for the detection of link failures in WBAN system for health care applications
    K. Kalaiselvi, G.R. Suresh, and V. Ravi
    Wiley
    Wireless body area network (WBAN) plays an important role in patient health care. The performance of this WBAN system is affected by link failures due to the presence of malicious sensor nodes. Hence, the detection and mitigation of this link failure is important for improving the efficiency of the WBAN system. This paper proposes a methodology for link failure detection using weight metric approach. The performance of the proposed methodology is analyzed in terms of packet delivery ratio (PDR), link failure detection latency, and link failure detection rate.

  • Speech Enhancement via Smart Larynx of Variable Frequency for Laryngectomee Patient for Tamil Language Syllables Using RADWT Algorithm
    P. Malathi, G. R. Suresh, M. Moorthi, and N. R. Shanker
    Springer Science and Business Media LLC

  • Genetic algorithm based sensor node classifications in wireless body area networks (WBAN)
    K. Kalaiselvi, G. R. Suresh, and V. Ravi
    Springer Science and Business Media LLC

  • Conversion of non-audible murmur to normal speech based on FR-GMM using non-parallel training adaptation method
    T. Rajesh Kumar, G.R Sursh, and S. Kanaga Subaraja
    IEEE
    In recent years, majority of people are affected with low voice, due to the biological variations in the environment and hereditary infections occurred in throat or vocal track. These people communicate with others in a murmured low voice utterances. This paper introduces a novel approach for the processing of non-audible murmur (NAM), with respect to a Full Rank Gaussian Mixture Model (FR-GMM) using non-parallel training adaptation method. Non-audible murmur means, lightly uttered soft voice that is very difficult to hear. The non-audible murmur is extracted directly from the soft tissues behind the ear, utilizing a body conductive non-audible murmur microphone made of stethoscope sensor. In the proposed technique, a FR-GMM is prepared with the traditional strategy while allowing the reference speaker voices using nonparallel training adaptation method. The Investigational analysis outputs prove that the proposed approach improving the natural quality of speech by 6% in word precision compared to the traditional approaches.

  • Infrared Thermal Imaging for Diabetes Detection and Measurement
    A. Selvarani and G. R. Suresh
    Springer Science and Business Media LLC

  • Enhancement of electrolaryngeal speech using frequency auditory masking and GMM based voice conversion
    P. Malathi, G. R. Sureshw, and M. Moorthi
    IEEE
    Laryngectomees lose their voice box after surgery and adapt various methods to restore their voice, one of them being Electrolaryngeal speech. The Electrolarynx suffers from producing natural speech by generating mechanical form of speech with suppressed unvoiced features, device and environment noise. This paper tends to remove the echo noise, device noise and environmental noise thereby enhancing the Electrolaryngeal speech to be more intelligible by spectral mapping using Gaussian Mixture Model (GMM) and auditory masking. The low frequency noise is masked by the pre-emphasised speech signal by determining the absolute threshold of masking. The spectral mapping technique using GMM based voice conversion in association with the source-filter model improves the voice quality and prosody. The objective and subjective evaluation measures, depict the significant enhancement of electrolaryngeal speech compared to previous enhancement methods which removed only low frequency noise and failed to include voice quality.

  • Conversion of non-audible murmur to normal speech based on full-rank Gaussian mixture model
    T. Rajesh Kumar, G. R. Suresh, and S. Kanaga Suba Raja
    American Scientific Publishers

RECENT SCHOLAR PUBLICATIONS

  • Detection of fire in forest area using chromatic measurements by Sobel edge detection algorithm compared with Prewitt gradient edge detector
    GS Yadav, T Sathish, GR Suresh
    AIP Conference Proceedings 2853 (1) 2024

  • Horizontal feature extraction of handwritten character by statistical and structural sorts compared with diagonal direction
    C Sasikumar, K Malathi, GR Suresh
    AIP Conference Proceedings 2853 (1) 2024

  • Segmentation of handwritten character identification from an image using digital image processing based on threshold level compared with edge based segmentation
    C Sasikumar, GR Suresh
    AIP Conference Proceedings 2822 (1) 2023

  • Detection in dermoscopic images with inadequate control of blue-white structures
    GR Suresh, S Rajalakshmi, C Selvi, S Prakash
    AIP Conference Proceedings 2523 (1) 2023

  • Wearable Sensor-Based Human Exhalation Rhythm Recognition using Deep Learning Neural network
    SJ Rubavathy, GR Suresh, C Senthilkumar, PS Bharathi, V Amudha
    2022 International Conference on Innovative Computing, Intelligent 2022

  • Physical Fitness and Exercise to motivate the golden-ager Using Body Area Networks
    P. S. Bharathi, V. Amudha, S. J. Rubavathy, G. R. Suresh and C. Senthilkumar
    2022 International Conference on Innovative Computing, Intelligent 2022

  • DTKFA: Limiting the amount of needless file transfers by predicting the data gathering requirements of sensor nodes
    V. Amudha, S. J. Rubavathy, G. R. Suresh, C. Senthilkumar and P. S. Bharathi
    2022 International Conference on Innovative Computing, Intelligent 2022

  • Conversion of NAM to Normal Speech Based on Stochastic Binary Cat Swarm Optimization Algorithm
    TR Kumar, GN Balaji, DV Babu, S Sivakumar, K Kalaiselvi, GR Suresh
    Distributed Computing and Optimization Techniques: Select Proceedings of 2022

  • Optimization-enabled deep convolutional network for the generation of normal speech from non-audible murmur based on multi-kernel-based features
    T Rajesh Kumar, GR Suresh, K Kalaiselvi
    International Journal of Wavelets, Multiresolution and Information 2022

  • Decision support system for diabetes using tongue images
    A Selvarani, GR Suresh
    2020 International Conference on Communication and Signal Processing (ICCSP 2020

  • Intelligent Wearable Device For Early Detection Of Myocardial Infarction Using IoT
    V Kaviya, GR Suresh
    2020 Sixth International Conference on Bio Signals, Images, and 2020

  • Taylor‐AMS features and deep convolutional neural network for converting nonaudible murmur to normal speech
    TR Kumar, GR Suresh, SK Subaraja, C Karthikeyan
    Computational Intelligence 36 (3), 940-963 2020

  • Design of Nano Sensor for Soil Nutrient Testing in Precision Agriculture
    P Swetha, A Vanitha, GR Suresh, N Vigneshwaran, B Ranganathan
    International Conference on Advances in Chemistry with Specific Reference to 2020

  • Design of Pentacene based Organic Field Effect Transistor for low-frequency Operational Transconductance Amplifier
    CTA Wise, GR Suresh, M Palanivelen, S Saraswathi
    Journal of Circuits, Systems and Computers 29 (11) 2020

  • Preclinical diagnosis of diabetes with tongue infrared thermography and PSO algorithm
    A Selvarani, GR Suresh
    Medico-Legal Update 20 (1), 191-197 2020

  • False alarm detection using dynamic threshold in medical wireless sensor networks
    S Saraswathi, GR Suresh, J Katiravan
    Wireless Networks, 1-13 2019

  • An efficient approach for the detection of link failures in WBAN system for health care applications
    K Kalaiselvi, GR Suresh, V Ravi
    International Journal of Communication Systems 32 (15), e4112 2019

  • Speech enhancement via smart larynx of variable frequency for laryngectomee patient for Tamil language syllables using RADWT algorithm
    P Malathi, GR Suresh, M Moorthi, NR Shanker
    Circuits, Systems, and Signal Processing 38, 4202-4228 2019

  • Genetic algorithm based sensor node classifications in wireless body area networks (WBAN)
    K Kalaiselvi, GR Suresh, V Ravi
    Cluster Computing 22 (Suppl 5), 12849-12855 2019

  • An Organic push pull amplifier using Pentacene Thin Film for Bio-Sensing Applications
    CTA Wise, GR Suresh, M Palanivelen, S Vani
    Caribbean Journal of Science 53 (2), 1473 - 1484 2019

MOST CITED SCHOLAR PUBLICATIONS

  • Speckle noise reduction in ultrasound images by wavelet thresholding based on weighted variance
    S Sudha, GR Suresh, R Sukanesh
    International journal of computer theory and engineering 1 (1), 7 2009
    Citations: 311

  • Path planning algorithm for autonomous mobile robot in dynamic environment
    MS Ganeshmurthy, GR Suresh
    2015 3rd International Conference on Signal Processing, Communication and 2015
    Citations: 93

  • Speckle noise reduction in ultrasound images using context-based adaptive wavelet thresholding
    S Sudha, GR Suresh, R Sukanesh
    IETE Journal of Research 55 (3), 135-143 2009
    Citations: 85

  • Performance analysis of fuzzy c means algorithm in automated detection of brain tumor
    R Preetha, GR Suresh
    2014 world congress on computing and communication technologies, 30-33 2014
    Citations: 73

  • Wavelet based image denoising using adaptive thresholding
    S Sudha, GR Suresh, R Sukanesh
    Computational Intelligence and Multimedia Applications, International 2007
    Citations: 73

  • A survey on lossless compression for medical images
    MF Ukrit, A Umamageswari, GR Suresh
    International Journal of Computer Applications 31 (8), 47-50 2011
    Citations: 56

  • Hybrid SVM classification technique to detect mental stress in human beings using ECG signals
    L Vanitha, GR Suresh
    2013 International conference on advanced computing and communication 2013
    Citations: 42

  • Taylor‐AMS features and deep convolutional neural network for converting nonaudible murmur to normal speech
    TR Kumar, GR Suresh, SK Subaraja, C Karthikeyan
    Computational Intelligence 36 (3), 940-963 2020
    Citations: 38

  • A survey on security in medical image communication
    A Umamageswari, MF Ukrit, GR Suresh
    International Journal of Computer Applications 30 (3), 41-45 2011
    Citations: 38

  • Comparative study on speckle noise suppression techniques for ultrasound images
    S Sudha, GR Suresh, R Sukanesh
    International Journal of Engineering and Technology 1 (1), 57 2009
    Citations: 38

  • Optimization-enabled deep convolutional network for the generation of normal speech from non-audible murmur based on multi-kernel-based features
    T Rajesh Kumar, GR Suresh, K Kalaiselvi
    International Journal of Wavelets, Multiresolution and Information 2022
    Citations: 31

  • Development of four stress levels in group stroop colour word test using HRV analysis
    L Vanitha, GR Suresh, M Chandrasekar, P Punita
    Biomedical research-India 28 (1), 98-105 2017
    Citations: 30

  • Security in medical image communication with arnold's cat map method and reversible watermarking
    A Umamageswari, GR Suresh
    2013 International Conference on Circuits, Power and Computing Technologies 2013
    Citations: 30

  • Automatic diagnosis of breast cancer using thermographic color analysis and SVM classifier
    AT Wakankar, GR Suresh
    Intelligent Systems Technologies and Applications 2016, 21-32 2016
    Citations: 28

  • Genetic algorithm based sensor node classifications in wireless body area networks (WBAN)
    K Kalaiselvi, GR Suresh, V Ravi
    Cluster Computing 22 (Suppl 5), 12849-12855 2019
    Citations: 25

  • Coalition formation and Task Allocation of multiple autonomous robots
    GR Suresh
    3rd InternationalConference on Signal Processing, Communication and 2015
    Citations: 24

  • Hierarchical SVM to detect mental stress in human beings using Heart Rate Variability
    L Vanitha, GR Suresh
    2014 2nd International Conference on Devices, Circuits and Systems (ICDCS), 1-5 2014
    Citations: 23

  • Wavelet based image denoising using adaptive subband thresholding
    S Sudha, GR Suresh, R Sukanesh
    International Journal of Soft computing 2 (5), 628-632 2007
    Citations: 22

  • Infrared thermal imaging for diabetes detection and measurement
    A Selvarani, GR Suresh
    Journal of medical systems 43 (2), 23 2019
    Citations: 20

  • High density impulse noise removal using BDND filtering algorithm
    G Thanakumar, S Murugappriya, GR Suresh
    2014 International Conference on Communication and Signal Processing, 1958-1962 2014
    Citations: 20