Whale optimization approach for early heart disease prediction based on FCM using DCNN Rajakumar Raja Aswathi, Kolappanpillai Pazhani Kumar, Bagavathiperumal Ramakrishnan Review of Computer Engineering Research, 2023 Heart disease is the leading cause of death worldwide. It has an impact on not only the health of patients but also the economies and expenses of the countries. Numerous machine learning and data mining approaches are being developed and explored currently in order to predict various diseases. This paper aims to address the pressing global issue of heart disease by leveraging machine learning and data mining techniques. Specifically, it focuses on utilizing a Fuzzy C means (FCM) approach for attribute segmentation, employing the Whale Optimization Algorithm (WOA) for feature selection, and utilizing Deep Convolutional Neural Networks (DCNNs) for medical diagnosis and early prediction. In this study, the initial stage involves segmenting patient records' attributes using the FCM method. Subsequently, high-ranking features are selected through the WOA algorithm. These segmented features are then input into DCNNs to construct a robust medical diagnosis system and enable early-stage prediction. The DCNNs autonomously extract crucial features without human intervention, enhancing the accuracy of disease prediction. The performance evaluation of the proposed classifier is conducted using the Python platform, with the DCNN achieving an impressive accuracy level of 90.12% during testing. This indicates the DCNN's capability to accurately predict the presence or absence of cardiac disease, showcasing its potential as an effective tool in healthcare. The integration of FCM attribute segmentation, WOA feature selection, and DCNN-based prediction holds significant practical implications. It offers healthcare professionals a valuable tool for diagnosing and predicting heart disease early, potentially saving lives.
SCSLnO-SqueezeNet: Sine Cosine-Sea Lion Optimization enabled SqueezeNet for intrusion detection in IoT M. Masthan, K. Pazhanikumar, Meena Chavan, Jyothi Mandala, Sanjay Nakharu Prasad Kumar Network Computation in Neural Systems, 2023 Security and privacy are regarded as the greatest priority in any real-world smart ecosystem built on the Internet of Things (IoT) paradigm. In this study, a SqueezeNet model for IoT threat detection is built using Sine Cosine Sea Lion Optimization (SCSLnO). The Base Station (BS) carries out intrusion detection. The Hausdorff distance is used to determine which features are important. Using the SqueezeNet model, attack detection is carried out, and the network classifier is trained using SCSLnO, which is developed by combining the Sine Cosine Algorithm (SCA) with Sea Lion Optimization (SLnO). BoT-IoT and NSL-KDD datasets are used for the analysis. In comparison to existing approaches, PSO-KNN/SVM, Voting Ensemble Classifier, Deep NN, and Deep learning, the accuracy value produced by devised method for the BoT-IoT dataset is 10.75%, 8.45%, 6.36%, and 3.51% higher when the training percentage is 90.
An Effective Detection of Litchi Disease using Deep Learning Mansi Dahiya, Guru Prasad M S, Tanmay Anand, Khushi Kumar, Sparsh Bansal, Naveen Kumar H N 2023 14th International Conference on Computing Communication and Networking Technologies Icccnt 2023, 2023 Litchi plant diseases are a major threat to global agricultural productivity, economies, and the environment as they cause significant losses. Therefore, it is necessary to have an early and accurate litchi plant monitoring system for farmers, managers, and decision-makers. To develop a constraint-free and reliable work plan for total litchi disease management, a comprehensive review of the literature and industry practices was conducted. A conceptual framework for litchi disease classification was outlined, which uses a structured approach combining professional visual interpretation, pathological analysis, and feature extraction using convolutional neural networks. The diseases in the litchi plant were identified, described, and divided into groups for broad classification. The next step is to recommend a course of treatment for the related litchi plant disease and provide contact information of a pathologist for further queries and suggestions. The aim is to create a user-friendly interface to offer farmers affordable, simple, and quick assistance.
Identification of Various Bamboo Diseases Using Deep Learning Approach Khushi Kumar, Sachin Sharma, Piyush Pandey, Himanshu Rai Goyal 2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2022, 2022 Bamboos with their enormous adaptations according to the environment, are used in almost all parts of the world. People often find these to be recognized as plants but instead, they belong to the family of grass. From ancient cultures to modern customization, they are always used. With that being said, these are the natural habitats that too suffer from diseases. Earlier papers were not available easily to the independent researcher and even there is less research on this field. The sole agenda of this paper is to provide all the answers related to the diseases which occur in bamboos. Firstly, we will check whether there is any disease in the sample taken, and then we will try to come up with some models to detect that disease. Here, we are taking the help of machine learning to determine the kind of disease. The Convolutional Neural Network model is used here for detection. Images have been used here as the data input for the training of the model, which Artificial Intelligence (AI) can easily process. This paper also represents the basic characteristics or properties of the diseases that can occur and how those will be distinguished. After all the citations of the earlier projects under this category, we have tried to come up with a solution that will be implemented accurately and efficiently.
IoT Enabled Crop Detection System using Soil Analysis Khushi Kumar, Sachin Sharma, Piyush Pandey, Himanshu Rai Goyal 7th International Conference on Communication and Electronics Systems Icces 2022 Proceedings, 2022 Soil monitoring enabled by the Internet of Things allows farmers to predict which crops will grow based on soil factors. Our goal is to create a crop predicting tool for a particular soil type. Rainfall, pH, temperature, moisture, humidity, NPK and other variables can all be monitored using Internet of Things sensors. Data from IoT sensors can then be transferred to a central location (or the cloud) for pattern detecti on, processing, and visualisation. Machine learning, one of the most powerful crop prediction technologies, is being used in this research study to help newbie farmers seed the proper crops.
An algorithm for mining closed weighted sequential patterns with flexing time interval for medical time series data K. Pazhanikumar, S. Arumugaperumal Proceedings 2015 International Conference on Computers Communications and Systems Icccs 2015, 2016 In this paper, an algorithm for mining the nonredundant closed weighted sequential patterns with flexible time intervals for the medical time series data is proposed. Initially, the sequence weight for each sequence is calculated based on the time interval between the itemsets and subsequently the candidate sequences are generated with flexible time intervals. The next step is, computation of frequent sequential patterns with the aid of proposed support measure and subsequently the frequent sequential patterns are subjected to closure checking process which leads to filter the closed sequential patterns with flexible time intervals. Finally, the proposed methodology produces a necessary sequential patterns which is proved. The proposed methodology constructs closed sequential patterns which are 23.2% lesser than the sequential patterns.
An analytical study on frequent itemset mining algorithms K. Pazhani Kumar, S. Arumugaperumal Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2013
Swafv: Secure Web Authentication Using Multi-Finger Vein Biometrics With Spoof Detection And Betl PK Vathsala V IJDDT 16 (38), 456-466 , 2026 2026
SIAMESE FUSION U-NET FOR FINGER VEIN BIOMETRIC RECOGNITION PK Vathsala V The Bioscan 21 (2), 424-470 , 2026 2026
Deep Learning DKPKV Vathsala 2025
Advances in Finger Vein Recognition: A Comprehensive Review of Techniques and Applications DKP V.Vathsala International Journal of Research Publication and Reviews 6 (9), 3881-3887 , 2025 2025
A hybrid framework for heart disease prediction using multi‐scale feature map reconstruction with inception in DenseNet and AHP‐VIKOR feature selection model KP Raja Aswathi R Network Modeling Analysis in Health Informatics and Bioinformatics 14 , 2025 2025
An innovative attention infused- BiRecurrenTwin network assisted hybrid segmentation technique for accurate heart disease prediction BR R. Raja Aswathi * , K. Pazhani Kumar Engineering Applications of Artificial Intelligence, www.elsevier.com/locate … , 2024 2024 Citations: 3
Feature Selection with PSO and Convolutional Neural Network with Long Short-Term Memory for Medical Application RR ASWATHI, KP KUMAR, B RAMAKRISHNAN 2024 Citations: 1
Utilizing ANN in an Advanced Machine Learning Framework KPRR Aswathi International Conference on Advances in Modern Age Technologies for Health … , 2024 2024
A Multi-Folded Dynamic Regularized Dual Crossed CNN with A Self-Adaptive Metaheuristic Aware Cardiovascular Disease Prediction KP Raja Aswathi R Nanotechnology Perceptions 20 (5), 1629–1653 , 2024 2024
Remote sensing image classification using modified random forest with empirical loss function through crowd-sourced SNKVM Pazhanikumar Multimedia Tools and Applications , 2023 2023 Citations: 17
Whale optimization approach for early heart disease prediction based on FCM using DCNN RR Aswathi, KP Kumar, B Ramakrishnan Review of Computer Engineering Research 10 (4), 150-164 , 2023 2023 Citations: 2
Heart Disease Prediction Using Various Classification Models KPK B. Ramakrishnan R. Raja Aswathi1 International Journal of Advanced Trends in Engineering and Management … , 2023 2023
SCSLnO-SqueezeNet: Sine Cosine-Sea Lion Optimization enabled SqueezeNet for intrusion detection in IoT SNPK M. Masthan , K. Pazhanikumar , Meena Chavan , Jyothi Mandala NETWORK: COMPUTATION IN NEURAL SYSTEMS , 2023 2023 Citations: 27
Classification Based Neural Network Modelling with Continuous and Sequential data RR Aswathi, J Jency, B Ramakrishnan, KP Kumar Microprocessors and Microsystems, 104601 , 2022 2022 Citations: 5
Performance of Naïve Bayes, C4. 5 and KNN using Breast Cancer, Iris and Hypothyroid Datasets [J] KP Kumar, RR Aswathi International Journal of Innovative Technology and Exploring Engineering … , 2020 2020 Citations: 2
An Extended C4. 5 Classification Algorithm using Mathematical Series RR Aswathi, KP Kumar, B Ramakrishnan Science & Technology Journal 7 (2), 54-59 , 2019 2019 Citations: 2
An Algorithm for Generating Non - Redundant Sequential Rules for Medical Time Series Data K Pazhanikumar, DS Aruugaperual International Journal on Future Revolution in Computer Science … , 2017 2017
An algorithm for mining closed weighted sequential patterns with flexing time interval for medical time series data A Pazhanikumar ieee explore , 2015 2015 Citations: 2
An Effectual Frequent Pattern Mining (FPM) Approach Using Clustering and Varied Sliding Window for Streaming of Data KPKDSA Perumal Australian Journal of Basic and Applied Sciences, 462-469 , 2014 2014
An analytical study on frequent itemset mining algorithms KP Kumar, S Arumugaperumal Mining Intelligence and Knowledge Exploration: First International … , 2013 2013 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Association Rule Mining and Medical Application: A Detailed Survey AS Pazhanikumar K 2013 Citations: 30
SCSLnO-SqueezeNet: Sine Cosine-Sea Lion Optimization enabled SqueezeNet for intrusion detection in IoT SNPK M. Masthan , K. Pazhanikumar , Meena Chavan , Jyothi Mandala NETWORK: COMPUTATION IN NEURAL SYSTEMS , 2023 2023 Citations: 27
Remote sensing image classification using modified random forest with empirical loss function through crowd-sourced SNKVM Pazhanikumar Multimedia Tools and Applications , 2023 2023 Citations: 17
Classification Based Neural Network Modelling with Continuous and Sequential data RR Aswathi, J Jency, B Ramakrishnan, KP Kumar Microprocessors and Microsystems, 104601 , 2022 2022 Citations: 5
An innovative attention infused- BiRecurrenTwin network assisted hybrid segmentation technique for accurate heart disease prediction BR R. Raja Aswathi * , K. Pazhani Kumar Engineering Applications of Artificial Intelligence, www.elsevier.com/locate … , 2024 2024 Citations: 3
Whale optimization approach for early heart disease prediction based on FCM using DCNN RR Aswathi, KP Kumar, B Ramakrishnan Review of Computer Engineering Research 10 (4), 150-164 , 2023 2023 Citations: 2
Performance of Naïve Bayes, C4. 5 and KNN using Breast Cancer, Iris and Hypothyroid Datasets [J] KP Kumar, RR Aswathi International Journal of Innovative Technology and Exploring Engineering … , 2020 2020 Citations: 2
An Extended C4. 5 Classification Algorithm using Mathematical Series RR Aswathi, KP Kumar, B Ramakrishnan Science & Technology Journal 7 (2), 54-59 , 2019 2019 Citations: 2
An algorithm for mining closed weighted sequential patterns with flexing time interval for medical time series data A Pazhanikumar ieee explore , 2015 2015 Citations: 2
Feature Selection with PSO and Convolutional Neural Network with Long Short-Term Memory for Medical Application RR ASWATHI, KP KUMAR, B RAMAKRISHNAN 2024 Citations: 1
An analytical study on frequent itemset mining algorithms KP Kumar, S Arumugaperumal Mining Intelligence and Knowledge Exploration: First International … , 2013 2013 Citations: 1
An advanced scratch removal method for Fingerprint biometrics S Arumugaperumal, B Sivagami, KP Kumar 2011 3rd International Conference on Electronics Computer Technology 4, 196-200 , 2011 2011 Citations: 1
Swafv: Secure Web Authentication Using Multi-Finger Vein Biometrics With Spoof Detection And Betl PK Vathsala V IJDDT 16 (38), 456-466 , 2026 2026
SIAMESE FUSION U-NET FOR FINGER VEIN BIOMETRIC RECOGNITION PK Vathsala V The Bioscan 21 (2), 424-470 , 2026 2026
Deep Learning DKPKV Vathsala 2025
Advances in Finger Vein Recognition: A Comprehensive Review of Techniques and Applications DKP V.Vathsala International Journal of Research Publication and Reviews 6 (9), 3881-3887 , 2025 2025
A hybrid framework for heart disease prediction using multi‐scale feature map reconstruction with inception in DenseNet and AHP‐VIKOR feature selection model KP Raja Aswathi R Network Modeling Analysis in Health Informatics and Bioinformatics 14 , 2025 2025
Utilizing ANN in an Advanced Machine Learning Framework KPRR Aswathi International Conference on Advances in Modern Age Technologies for Health … , 2024 2024
A Multi-Folded Dynamic Regularized Dual Crossed CNN with A Self-Adaptive Metaheuristic Aware Cardiovascular Disease Prediction KP Raja Aswathi R Nanotechnology Perceptions 20 (5), 1629–1653 , 2024 2024
Heart Disease Prediction Using Various Classification Models KPK B. Ramakrishnan R. Raja Aswathi1 International Journal of Advanced Trends in Engineering and Management … , 2023 2023
