Dr.M.Ramaraj

@ramaraj.cs@rathinam.in

Assistant professor Computer Science
Rathinam Global Deemed to be University

Dr.M.Ramaraj


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https://researchid.co/ramarajphdcs123
Dr. Muniappan Ramaraj is working as an Assistant Professor in the Department of Computer Science at Rathinam College of Arts and Science, Coimbatore. He holds a Ph.D., degree in computer science at Bharathiar University in the year of 2020 with specialization in Data Mining with Image Process and also Fuzzy logic in the image analysis. His research areas are Data Mining, Image Processing, Fuzzy Logic, Pattern Recognition and Deep Learning concept. He has published more research article in the reputed various national and international journals and also filed the patents in the same field. He has a reviewer of many international journals including with IEEE, ASTESJ, and JERS. He can be contacted at email: , or .

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Computer Science Applications, Computer Vision and Pattern Recognition, Decision Sciences

FUTURE PROJECTS

Hybrid Quantum-Classical Machine Learning Framework for Next-Generation Predictive Systems

The research contribution of this study lies in the development of an advanced Quantum Machine Learning (QML) framework that enhances prediction accuracy, computational efficiency, and intelligent decision-making for complex real-world datasets. The proposed model integrates quantum computing principles such as qubit representation, quantum superposition, entanglement, and variational quantum circuits with machine learning algorithms to overcome the limitations of classical models. The framework significantly improves feature optimization, nonlinear pattern extraction, and high-dimensional data processing. Experimental results demonstrate superior performance in terms of accuracy, precision, recall, F1-score, and computational scalability, thereby establishing QML as an efficient next-generation solution for intelligent predictive analytics and scientific research applications.


Applications Invited
11

Scopus Publications

44

Scholar Citations

3

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Educational data mining approach for predicting student performance and behavior using deep learning techniques
    Muniappan Ramaraj, Sabareeswaran Dhendapani, Jothish Chembath, Selvaraj Srividhya, Nainan Thangarasu, Bhaarathi Ilango
    Iaes International Journal of Artificial Intelligence, 2025
    Educational Data Mining (EDM) uncovers insights from large datasets collected from various educational platforms, such as online learning systems, student information databases, and classroom tools. EDM helps educators identify hidden patterns that improve teaching strategies, personalize learning experiences, and predict student performance. Predicting student success has become a key focus of EDM, allowing institutions to implement targeted interventions and personalized support. The dataset included academic achievement grades from 1,001 students enrolled in various courses during the fall semester across multiple years, to demonstrate how proposed models provide more accurate predictions compared to traditional machine learning methods. Models such as YOLO, Fast R-CNN, Artificial Neural Networks (ANNs), and Long Short-Term Memory (LSTM) networks are used to capture complex, non-linear relationships within the data. The comparative analysis shows that these deep learning models significantly outperform traditional techniques, such as decision trees and support vector machines (SVMs). The results indicate that proposed method offers improved predictive accuracy, enabling educational institutions to identify at-risk students and deliver tailored interventions. This study highlights the potential of enhanced method to transform personalized education and enhance student success by better understanding individual learning needs and behaviors.
  • An optimized deep learning framework based on LEE for real time student performance prediction in educational data
    Ramaraj Muniappan, Sowmya Devi Devarajan, Lavanya Subbarayalu Ramamurthy, Ayshwarya Balakumar, Prathap Gunaseelan, Shyamala Palanisamy, Srividhya Selvaraj, Dhendapani Sabareeswaran, Ilango Bhaarathi
    Bulletin of Electrical Engineering and Informatics, 2025
    Predicting student performance in real-time remains a critical challenge in educational data mining (EDM), especially with large, noisy, and high-dimensional datasets. This study proposes an advanced deep learning framework that integrates learning entropy estimation (LEE) with models such as support vector machines (SVM), you only look once (YOLO), recurrent convolutional neural networks (RCNN), and artificial neural networks (ANN) to enhance feature selection and classification accuracy. The framework follows a systematic pipeline involving data preprocessing, LEE-based feature extraction, and model training on a real-time academic dataset comprising student demographics, attendance, and performance metrics. Among the proposed models, the LEE-based YOLO (LBYOLO) achieved the highest testing accuracy of 93% and the fastest execution time of 1.84 seconds, while the LEE-based ANN (LBANN) demonstrated consistent performance across precision, recall, and F1-score. The results confirm the superiority of deep learning methods over traditional machine learning techniques for educational prediction tasks. This approach enables early detection of at-risk students and supports timely, data-driven educational interventions. Future work will focus on adaptive learning systems and multi-platform student behavior analysis to support personalized education strategies.
  • Role of Innovative Technology for Evolving Sustainability Trends, Navigating Future Directions and Overcoming Challenges
    M. Usha Devi, Velumani Thiyagarajan, S. Santhana Megala, Ramaraj Muniappan, Balaganesh Duraisamy
    World Sustainability Series, 2025
  • Edge Sec: A Privacy-Preserving IoT-ML Framework for Smart Home Security Threat Detection
    Vijiyalakshmi V, Shanmugapriya Velmurugan, Nagarajan P, Srikanth R, Ramaraj Muniappan, N Thangarasu
    Proceedings of the 6th International Conference on Smart Electronics and Communication Icosec 2025, 2025
  • Interpretable Appendicitis Detection using EfficientNetB1 and SHAP with CT Imaging Across Age Groups
    Rajesh Natarajan, Nagarajan Karthikeyan, Sujatha Krishna, D Prasanth, Ramaraj Muniappan, N Thangarasu
    Proceedings of the 9th International Conference on Inventive Systems and Control Icisc 2025, 2025
    Appendicitis causes emergency abdominal surgery in all age groups, so early identification is crucial to avoid complications and unnecessary treatments. Clinical assessments and laboratory markers can help make a diagnosis, but CT is usually needed. This study provides an AI-driven diagnostic system that uses deep learning and explainable AI to autonomously identify appendicitis from abdominal CT scans using the AppendiXNet CT Dataset. A modified EfficientNetB1 architecture is used to support grouped axial CT slices. To increase soft-tissue contrast in inflamed appendices, Hounsfield unit windowing, axial slice extraction, resizing, and normalization are used. The pretrained EfficientNetB1 model using ImageNet learns radio morphological parameters related to inflammation, periappendiceal fat stranding, and wall thickening to categorize CT images as appendicitispositive or negative. Slice-Wise Shapley Additive Explanations (SHAP) at the slice level identify the most important axial views and voxel positions influencing the model's decision-making for transparency and clinical interpretability. Supplemental Grad-CAM heatmaps help radiologists diagnose appendicitis in scans. Crossvalidated tests show that the suggested method improves diagnostic accuracy, sensitivity, and ROC curve area. The scalability and real-time engagement paradigm could be expanded into web-based diagnostic aid. This method is robust, impartial, and interpretable for AIassisted appendicitis diagnosis in children and adults.
  • Optimizing feature extraction for tampering image detection using deep learning approaches
    Ramaraj Muniappan, Dhendapani Sabareeswaran, Chembath Jothish, Joe Arun Raja, Srividhya Selvaraj, Thangarasu Nainan, Bhaarathi Ilango, Dhinakaran Sumbramanian
    Indonesian Journal of Electrical Engineering and Computer Science, 2024
    Tamper image detection approach using deep learning involves, creating a model that can accurately identify and localize instances of image tampering, by employing advanced feature extraction methods, object detection algorithms, and optimization techniques that could be manipulated on need basis. Enhance the integrity of visual content by automating the detection of unauthorized alterations, to ensure the reliability of digital images across various applications and domains. The problem addressing the optimization feature extraction techniques involves the detection of subtle manipulations, handling diverse tampering techniques, and achieving robust performance across different types of images and scenarios. The proliferation of sophisticated image editing tools makes it challenging to detect tampered regions within images, necessitating proposed techniques for automated tamper image detection. The research work will focus on four different feature extraction algorithms such as non-negative factorization (NNF), singular value decomposition (SVD), explicit semantic analysis (ESA), principal component analysis (PCA), which are outsourced. Detecting tampered images through deep learning necessitates the meaningful selection and adjustment of several parameters to enhance the model's effectiveness. Integrating the feature extraction algorithm with the suggested methods effectively identifies critical features within the dataset, thereby improving the detection capabilities and achieving higher accuracy.
  • Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases
    Ramaraj Muniappan, Thiruvenkadam Thangavel, Govindaraj Manivasagam, Dhendapani Sabareeswaran, Nainan Thangarasu, Chembath Jothish, Bhaarathi Ilango
    International Journal of Electrical and Computer Engineering, 2024
    In the empire of image processing and computer vision, the demand for advanced segmentation techniques has intensified with the growing complexity of visual data. This study focuses on the innovative paradigm of fuzzy mountain-based image segmentation, a method that harnesses the power of fuzzy logic and topographical inspiration to achieve nuanced and adaptable delineation of image regions. This research primarily concentrates on determining the age of tigers, a critical and challenging task in the current scenario. The primary objectives include the development of a comprehensive framework for FMBIS and an in-depth investigation into its adaptability to different image characteristics. This research work incorporates those domains of image processing and data mining to predict the age of the tiger using different kinds of color images. Fuzzy mountain-based pixel segmentation arises from the need to capture the subtle gradients and uncertainties present in images, offering a novel approach to achieving high-fidelity segmentations in diverse and complex scenarios. The proposed methods enable image enhancement and filtering and are then assessed during process time, retrieval time, to give a more accurate and reduced error rate for producing higher results for real-time tiger image database.
  • Predicting User Behavior Models Using M-Commerce Methods Decorated with Weighted Scoring and Mining Techniques
    Nagarajan Karthikeyan, Muniappan Ramaraj, Ilango Bhaarathi, N. Thangarasu, Jothish Chembath, C. Kumuthini
    2024 2nd International Conference on Computing and Data Analytics Iccda 2024 Proceedings, 2024
    Mobile-based devices and systems are now a common feature of daily life. Mobile trade is a new and more powerful emerging technology. In addition to the fast progress of the increasing popularity of powerful mobile devices and wireless networking technology, smartphone users are now able to access global information from all over the world. In the future, in the mobile business era, some m-commerce platforms catch moving tracks and purchase from customers. An online social network is relevant in many fields including ads, public health programmers, administrative science, and even politics. Leaders and adherents using consumer details will be listed in this online study. Many businesses are strongly involved in SNSs and Social Media Marketing (SMM) is the new “holy marketing grid.” The real importance of SMM remains elusive, as social networking also determines its best practices and actions. Take the latest announcement Shop kick as an example to bring incentives to smartphone consumers when checking in shops and products. The objective of this paper is to develop customer patterns and mining predictions. Patterns are the transactions that a customer buys when he visits a shopping mall. It has information about shops and items. Data mining is used to predict the behavior of customers to gain more knowledge about mining. Data mining is intended to retrieve the F score and use confusion metrics primarily for error percentage determination of data mining algorithms.
  • A new fuzzy rule-based optimization approach for predicting the user behaviour classification in M-commerce
    Muniappan Ramaraj, Jothish Chembath, Balluru Thammaiahshetty Adishankar Nithya, Gnanakumar Ganesan, Balakrishnan Uma Shankari, Nagarajan Karthikeyan
    International Journal of Reconfigurable and Embedded Systems, 2023
    A novel approach for classification of user behaviour prediction using proposed embracing the optimized fuzzy techniques to predicting the user data in M-commerce. Using this technique, network users can be monitored and their behavior categorized according to their activity. Unauthorized use of the website, network security breach attempts, firewalls, unauthorized access to the service and frequency of attempts. The proposed method has been adapted with the user classification to predict the predefine segregation of information to extract from user logs. Pattern recognition is a method for information discovery that results in current information patterns. Continuing items are a required task in various knowledge mining operations in pursuit of fascinating types from the data banks, including association rules, connections, sequences, episodes, classifications, bunches and much more. The functionality findings achieved in relation to precision and recall show that our technique can contribute to predicting more accurately than the different approaches. This paper focuses on to enhance the far better forecast for the mobile phone users through locating more reliable frequent patterns coming from the consumer deal data bank through looking at the body weight value of each thing collection and also examining the consumer activities on all time intervals.
  • A sophisticated image filtering and enhancement method with worn speculative segmentation process
    Thangarasu Nainan, Ramaraj Muniappan, V. Vijayalakshmi, R. Rajalakshmi, Sabareeswaran Dhandapani, Murugadass Muthaiyan
    Handbook of Research on Advancements in AI and Iot Convergence Technologies, 2023
    Image mining is tantamount to data mining concept. It is most imperative to understand the data mining concept to the prior knowledge of image mining. Data mining is an assemblage of methods that are used to automated approach to exhaustively explore and create associations in very large datasets. Image mining process is an analyzing large set of domain-specific data and consequently extracting information and knowledge in a form of new relationships, patterns, or clusters for the decision-making process. This research article mainly focuses on color pixel-based image segmentation to infer the age of the tiger and also applied several reasonable and applicable filters, enhancement processes in the speculative real-time images. The objective of the research work is to be done on assessing the age of the tiger using the color pixel based image classification and clustering is the main of the research work. To optimization of reduce the processing Time, Retrieval Time, Accuracy and Error Rate by generating the better results is to real time tiger image database.
  • Sophisticated CPBIS methods applied for FBISODATA clustering algorithm using with real time image database
    Muniappan Ramaraj, Dhandapani Sabareeswaran, V. Vijayalaksmi, Chembath Jothish, N. Thangarasu, Govindaraj Manivasagam
    Indonesian Journal of Electrical Engineering and Computer Science, 2023

RECENT SCHOLAR PUBLICATIONS

  • An optimized deep learning framework based on LEE for real time student performance prediction in educational data
    R Muniappan, SD Devarajan, LS Ramamurthy, A Balakumar, ...
    Bulletin of Electrical Engineering and Informatics 14 (5), 3671-3682 , 2025
    2025
    Citations: 4
  • Edge Sec: A Privacy-Preserving IoT-ML Framework for Smart Home Security Threat Detection
    V Vijiyalakshmi, S Velmurugan, P Nagarajan, R Srikanth, R Muniappan, ...
    2025 6th International Conference on Smart Electronics and Communication … , 2025
    2025
  • Interpretable Appendicitis Detection using EfficientNetB1 and SHAP with CT Imaging Across Age Groups
    R Natarajan, N Karthikeyan, S Krishna, D Prasanth, R Muniappan, ...
    2025 9th International Conference on Inventive Systems and Control (ICISC … , 2025
    2025
  • Role of Innovative Technology for Evolving Sustainability Trends, Navigating Future Directions and Overcoming Challenges
    MU Devi, V Thiyagarajan, S Santhana Megala, R Muniappan, ...
    Green Economy and Sustainable Development, 97-110 , 2025
    2025
    Citations: 2
  • Homomorphic encryption, privacy-preserving feature extraction, and decentralized architecture for enhancing privacy in voice authentication
    K Murugesan, LS Ramamurthy, B Palanisamy, Y Chandrasekar, ...
    International Journal of Electrical and Computer Engineering (IJECE) 15 (2 … , 2025
    2025
  • Optimization techniques applied on image segmentation process by prediction of data using data mining techniques
    R Muniappan, S Selvaraj, RV Gurusamy, V Thiyagarajan, ...
    Int. J. Electr. Comput. Eng.(IJECE) 15, 2161 , 2025
    2025
    Citations: 1
  • Educational data mining approach for predicting student performance and behavior using deep learning techniques
    BI Muniappan Ramaraj, Sabareeswaran Dhendapani, Jothish Chembath, Selvaraj ...
    IAES International Journal of Artificial Intelligence (IJ-AI) 14 (05), 4113-4122 , 2025
    2025
    Citations: 2
  • OPTIMIZING AUTOMATIC SPEECH RECOGNITION WITH MODIFICATIONS OF PCA AND CNN ARCHITECTURES USING IN REAL-TIME SYSTEMS
    M Kathiresh, A Uthiramoorthy, M Ramaraj
    ICTACT Journal on Microelectronics 11 (1), 1997-2004 , 2025
    2025
  • Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases.
    R Muniappan, T Thangavel, G Manivasagam, D Sabareeswaran, ...
    International Journal of Electrical & Computer Engineering (2088-8708) 14 (3) , 2024
    2024
  • Predicting User Behavior Models Using M-Commerce Methods Decorated with Weighted Scoring and Mining Techniques
    R Muniappan
    2nd International Conference on Computing and Data Analytics (ICCDA-2024) , 2024
    2024
  • Optimizing feature extraction for tampering image detection using deep learning approaches
    R Muniappan
    IJEECS 35 (3) , 2024
    2024
  • Robust Fuzzy Chaotic Cuckoo Search Boosted Relief Feature Selection for Dimensionality Reduction to Improve Botnet Attack Detection in IoT
    V Antony, N Thangarasu
    International Conference on Intelligent Computing Techniques For Smart … , 2023
    2023
  • Deep Ensembles for Improving Time Series Predictions with Deep Learning
    V Ravikumar, AR Nawadkar, A Shukla, UM Daivagna, R Krishnamoorthy, ...
    2023 2nd International Conference on Futuristic Technologies (INCOFT), 1-6 , 2023
    2023
  • A new fuzzy rule-based optimization approach for predicting the user behaviour classification in M-commerce
    NK Muniappan Ramaraj, Jothish Chembath, Balluru Thammaiahshetty Adishankar ...
    International Journal of Reconfigurable and Embedded Systems 12 (3), 320-328 , 2023
    2023
    Citations: 1
  • Sophisticated CPBIS methods applied for FBISODATA clustering algorithm using with real time image database
    G Ramaraj, M. , Sabareeswaran, D. , Vijayalakshmi, V. , ... Thangarasu, N ...
    Indonesian Journal of Electrical Engineering and Computer Science 30 (1 … , 2023
    2023
    Citations: 3
  • M-COMMERCE USER BEHAVIOR PREDICTION FOR NUMEROUS CLUSTERING TECHNIQUES USING OPTIMIZED PATTERN MINING METHOD
    DMR Dr. N. Karthikeyan1, Dr. Gopinath D1, Prathap. G1
    Journal of Data Acquisition and Processing 38 (1), 1651-1661 , 2023
    2023
  • A Sophisticated Image Filtering and Enhancement Method With Worn Speculative Segmentation Process
    T Nainan, R Muniappan, V Vijayalakshmi, R Rajalakshmi, S Dhandapani, ...
    Handbook of Research on Advancements in AI and IoT Convergence Technologies … , 2023
    2023
  • WITHDRAWN: Modified color pixel based image segmentation using fbmc algorithms used with real time image database
    M Ramaraj, D Sabareeswaran, GA Prasanna
    Materials Today: Proceedings , 2021
    2021
    Citations: 2
  • Enhanced Image Segmentation To Infer The Age Of The Tiger Using Fuzzy Modified K-Means Clustering Algorithm (FMKMCA).
    SN M. Ramaraj
    Research Journal of Pharmaceutical, Biological and Chemical Sciences 10 (2 … , 2019
    2019
  • Comparative Analysis of Proposed Methods for Analyzing Color Pixel based Image Segmentation Using Tiger Image Dataset
    DSN M. Ramaraj
    International Journal of Research 7 (IX), 235-252 , 2018
    2018
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • A comparative study of CN2 rule and SVM algorithm and prediction of heart disease datasets using clustering algorithms
    M Ramaraj, TA Selvadoss
    Network and Complex Systems 3 (10), 1-6 , 2013
    2013
    Citations: 16
  • Color Based Image Segmentation Using KNN Classification with Contour Analysis Method
    DSN M.Ramaraj
    International Research Journal of Engineering Technology 3 (issue 10), 1169-1177 , 2016
    2016
    Citations: 5
  • An optimized deep learning framework based on LEE for real time student performance prediction in educational data
    R Muniappan, SD Devarajan, LS Ramamurthy, A Balakumar, ...
    Bulletin of Electrical Engineering and Informatics 14 (5), 3671-3682 , 2025
    2025
    Citations: 4
  • Sophisticated CPBIS methods applied for FBISODATA clustering algorithm using with real time image database
    G Ramaraj, M. , Sabareeswaran, D. , Vijayalakshmi, V. , ... Thangarasu, N ...
    Indonesian Journal of Electrical Engineering and Computer Science 30 (1 … , 2023
    2023
    Citations: 3
  • Plagiarism detection paradigm for web content using similarity analysis approach
    M Ramaraj, AS THANAMANI
    ISSN2348-9928IJAICTVolume1, Issue5, September2014Doi 1 , 2014
    2014
    Citations: 3
  • Role of Innovative Technology for Evolving Sustainability Trends, Navigating Future Directions and Overcoming Challenges
    MU Devi, V Thiyagarajan, S Santhana Megala, R Muniappan, ...
    Green Economy and Sustainable Development, 97-110 , 2025
    2025
    Citations: 2
  • Educational data mining approach for predicting student performance and behavior using deep learning techniques
    BI Muniappan Ramaraj, Sabareeswaran Dhendapani, Jothish Chembath, Selvaraj ...
    IAES International Journal of Artificial Intelligence (IJ-AI) 14 (05), 4113-4122 , 2025
    2025
    Citations: 2
  • WITHDRAWN: Modified color pixel based image segmentation using fbmc algorithms used with real time image database
    M Ramaraj, D Sabareeswaran, GA Prasanna
    Materials Today: Proceedings , 2021
    2021
    Citations: 2
  • Comparative Analysis of Proposed Methods for Analyzing Color Pixel based Image Segmentation Using Tiger Image Dataset
    DSN M. Ramaraj
    International Journal of Research 7 (IX), 235-252 , 2018
    2018
    Citations: 2
  • Design and Implementations of Color Pixel Based Image Segmentation Using Enhanced Data Clustering Algorithms to Applying on Tiger Image Dataset
    DSN M. Ramaraj
    International Journal of Pure and Applied Mathematics 119 (18), 2719-2739 , 2018
    2018
    Citations: 2
  • Optimization techniques applied on image segmentation process by prediction of data using data mining techniques
    R Muniappan, S Selvaraj, RV Gurusamy, V Thiyagarajan, ...
    Int. J. Electr. Comput. Eng.(IJECE) 15, 2161 , 2025
    2025
    Citations: 1
  • A new fuzzy rule-based optimization approach for predicting the user behaviour classification in M-commerce
    NK Muniappan Ramaraj, Jothish Chembath, Balluru Thammaiahshetty Adishankar ...
    International Journal of Reconfigurable and Embedded Systems 12 (3), 320-328 , 2023
    2023
    Citations: 1
  • Application of color based image segmentation paradigm on rgb color pixels using fuzzy c-means and k means algorithms
    R Muniappan
    2017
    Citations: 1
  • Edge Sec: A Privacy-Preserving IoT-ML Framework for Smart Home Security Threat Detection
    V Vijiyalakshmi, S Velmurugan, P Nagarajan, R Srikanth, R Muniappan, ...
    2025 6th International Conference on Smart Electronics and Communication … , 2025
    2025
  • Interpretable Appendicitis Detection using EfficientNetB1 and SHAP with CT Imaging Across Age Groups
    R Natarajan, N Karthikeyan, S Krishna, D Prasanth, R Muniappan, ...
    2025 9th International Conference on Inventive Systems and Control (ICISC … , 2025
    2025
  • Homomorphic encryption, privacy-preserving feature extraction, and decentralized architecture for enhancing privacy in voice authentication
    K Murugesan, LS Ramamurthy, B Palanisamy, Y Chandrasekar, ...
    International Journal of Electrical and Computer Engineering (IJECE) 15 (2 … , 2025
    2025
  • OPTIMIZING AUTOMATIC SPEECH RECOGNITION WITH MODIFICATIONS OF PCA AND CNN ARCHITECTURES USING IN REAL-TIME SYSTEMS
    M Kathiresh, A Uthiramoorthy, M Ramaraj
    ICTACT Journal on Microelectronics 11 (1), 1997-2004 , 2025
    2025
  • Optimization of CPBIS methods applied on enhanced fibrin microbeads approach for image segmentation in dynamic databases.
    R Muniappan, T Thangavel, G Manivasagam, D Sabareeswaran, ...
    International Journal of Electrical & Computer Engineering (2088-8708) 14 (3) , 2024
    2024
  • Predicting User Behavior Models Using M-Commerce Methods Decorated with Weighted Scoring and Mining Techniques
    R Muniappan
    2nd International Conference on Computing and Data Analytics (ICCDA-2024) , 2024
    2024
  • Optimizing feature extraction for tampering image detection using deep learning approaches
    R Muniappan
    IJEECS 35 (3) , 2024
    2024

Publications

1.Paper Published on “A Comparative Study on CN2 Rule and SVM Algorithm for Prediction of Heart Disease Datasets Using Clustering Algorithms” IISTE volume 3, No 10 on 2013.
2.Paper Published on “Plagiarism Detection Paradigm for Web Content Using Similarity Analysis Approach” on IJAICT volume 1, issue 5, on September 2014.
3. Paper Published On “Color Based Image Segmentation Using KNN Classification with Contour Analysis Method” IRJET, volume 03, issue 10, Oct 2016.
4. Paper Published On “An Analysis Of K Means Clustering Algorithm Image Segmentation With IQI” IJSRD, Volume 04, Issue 09, Nov 2016.
5.Published paper on “Application of Color Based Image Segmentation Paradigm on RgbColor Pixels Using Fuzzy C-Means and K Means Algorithms” IJCSMC, Vol-6, Issue-6, June-2017.
6. Published paper on “Grouping of Color Pixel Based Image Segmentation using on Clustering Techniques” IJEECSE, Vol-4, Issue-6, December 2017.(UGC Refeered Journal).
7. Published paper on “Color Pixel Based Image Classification and Clustering Using Fuzzy Method” International Journal for Research in Engineering Application & Management (IJREAM) ISSN : 2454-9150 Vol-04, Issue-04, July 2018. (UGC Refeered Journal).
8. Published Paper On “Color Pixel Based Image Segmentation Using Enhanced Data Clustering Algorithms Applying On Tiger Image Dataset” International Journal Of Advance And Innovative Research. ISSN: 2394-7780.Volume 5, Issue 3 (VII): July - September, 2018. UGC Approved Journals.

RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)

R-CNN Based Smart Healthcare Cloud Based IOT Model For Detection and Prevention.