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, et al. Proceedings of the 2024 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2024, 2024 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.
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, et al. Proceedings of the 2024 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2024, 2024 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.
Wrapper-based Feature Selection for Enhanced Intrusion Detection Using Random Forest Classification Polasi Sudhakar, Durga Prasanna N, Sreedhar Bhukya, Mohammad Azhar, G R Suresh, et al. Proceedings of 5th International Conference on Iot Based Control Networks and Intelligent Systems Icicnis 2024, 2024 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, et al. Proceedings of the 2024 International Conference on Innovative Computing Intelligent Communication and Smart Electrical Systems Icses 2024, 2024 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 Hybrid Neuro‐Fuzzy Optimization Framework for Self‐Healing and Lifetime Enhancement in Wireless Sensor Networks S Lakshmi, V Nanjappan, G Suresh, C Vivek International Journal of Communication Systems 39 (2), e70362 , 2026 2026
Optimizing Metabolic Health Tracking with SVM Classification and Real-Time IoT Data C Viswanathan, GR Suresh, D Joel Jebadurai, R Vinodha, N Dhivyadevi 2025 11th International Conference on Communication and Signal Processing … , 2025 2025
A Robust Artificial Intelligence Enabled Methodology to Predict Online Consumers Behaviour Using Hybrid Deep Learning Strategy S P, GR Suresh, K B, J N, R Chadge, AA Sherideh 2024 International Conference on Innovative Computing, Intelligent … , 2025 2025
Implementing Apache Spark GraphX in Big Data Using Breadth-First Search PR Parvathy, J Lenin, S Mishra Innovations in Intelligent Systems and Advanced Engineering (e-ISSN: 3107 … , 2025 2025
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), 020221 , 2024 2024 Citations: 4
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), 020234 , 2024 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), 020046 , 2023 2023
Tumor Detection Using Morphological Image Segmentation with DSP Processor TMS320C6748 TA Raju, KS Reddy, SA Rabbani, G Suresh, KS Reddy, KG Sravani Machine Learning Techniques for VLSI Chip Design, 185 , 2023 2023
Tumor Detection Using Morphological Image Segmentation with DSP Processor TMS320C6748 T Anil Raju, K Srihari Reddy, SA Rabbani, G Suresh, K Saikumar Reddy, ... Machine Learning for VLSI Chip Design, 185-194 , 2023 2023
Detection in dermoscopic images with inadequate control of blue-white structures P Bindhu, GR Suresh, S Rajalakshmi, C Selvi, S Prakash AIP Conference Proceedings 2523 (1), 020082 , 2023 2023 Citations: 1
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 2022 Citations: 2
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 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 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 2022 Citations: 5
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 2022 Citations: 34
Decision support system for diabetes using tongue images A Selvarani, GR Suresh 2020 International Conference on Communication and Signal Processing (ICCSP … , 2020 2020 Citations: 4
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 2020 Citations: 5
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 2020 Citations: 43
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 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 2020 Citations: 5
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 2009 Citations: 319
Path planning algorithm for autonomous mobile robot in dynamic environment MS Ganeshmurthy, GR Suresh 2015 3rd International Conference on Signal Processing, Communication and … , 2015 2015 Citations: 102
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 2009 Citations: 89
Wavelet based image denoising using adaptive thresholding S Sudha, GR Suresh, R Sukanesh Computational Intelligence and Multimedia Applications, International … , 2007 2007 Citations: 74
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 2014 Citations: 69
A survey on lossless compression for medical images MF Ukrit, A Umamageswari, GR Suresh International Journal of Computer Applications 31 (8), 47-50 , 2011 2011 Citations: 58
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 2020 Citations: 43
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 2013 Citations: 42
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 2009 Citations: 39
A survey on security in medical image communication A Umamageswari, MF Ukrit, GR Suresh International Journal of Computer Applications 30 (3), 41-45 , 2011 2011 Citations: 38
False alarm detection using dynamic threshold in medical wireless sensor networks S Saraswathi, GR Suresh, J Katiravan Wireless Networks, 1-13 , 2019 2019 Citations: 36
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 2022 Citations: 34
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 2013 Citations: 33
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 2017 Citations: 31
Automatic diagnosis of breast cancer using thermographic color analysis and SVM classifier AT Wakankar, GR Suresh The International Symposium on Intelligent Systems Technologies and … , 2016 2016 Citations: 30
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 2019 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 2014 Citations: 24
Coalition formation and Task Allocation of multiple autonomous robots GR Suresh 3rd InternationalConference on Signal Processing, Communication and … , 2015 2015 Citations: 22
Wavelet based image denoising using adaptive subband thresholding S Sudha, GR Suresh, R Sukanesh International Journal of Soft computing 2 (5), 628-632 , 2007 2007 Citations: 22
Infrared thermal imaging for diabetes detection and measurement A Selvarani, GR Suresh Journal of medical systems 43 (2), 23 , 2019 2019 Citations: 21
