PAZHANIKUMAR K
@sthinducollege.com
Head of Computer Science
S.T.Hindu College
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Software
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Rajakumar Raja Aswathi, Kolappanpillai Pazhani Kumar, and Bagavathiperumal Ramakrishnan
Conscientia Beam
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.
K. Pazhanikumar and S. Nithya KuzhalVoiMozhi
Springer Science and Business Media LLC
M. Masthan, K. Pazhanikumar, Meena Chavan, Jyothi Mandala, and Sanjay Nakharu Prasad Kumar
Informa UK Limited
ABSTRACT 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.
K. Pazhanikumar and S. Arumugaperumal
IEEE
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.
K. Pazhani Kumar and S. Arumugaperumal
Springer International Publishing
S. Arumugaperumal, B. Sivagami, and K. Pazhani Kumar
IEEE
Minutiae extraction for automatic Fingerprint identification system is one of the most important steps however, the performance of minutiae extraction relies heavily on an enhancement algorithm. There are a lot of things that affect the quality of the fingerprint; one of them is scratches occurred in the fingerprint. Scratches generate the cutting in ridges. This affects the performance of the minutiae extraction. There are many algorithms for fingerprint enhancement, but very limited papers considering the enhancement by scratch removal in the fingerprint. In this paper we present a new effective method for making scratch free fingers. This proposed method can be used to detect and reduce the scratches in the fingerprint. The power of the proposed method is made by morphological operations and inpaint operations. Morphology concepts are used to detect the scratches and exemplar inpaint method is used to fill the scratched locations. Experimental results show a significant improvement of the fingerprint enhancement in the scratch-affected — area.