Senthamil Selvi R
@saranathan.ac.in
Computer Science and Engineering
Saranathan college of Engineering
RESEARCH INTERESTS
Big data classification, Data Mining, Machine Learning
Scopus Publications
Scopus Publications
R. Senthamil Selvi, V. Sankari, N. Ramya, and M. Selvi
World Scientific Pub Co Pte Ltd
A number of authors have focused on this study to examine how huge data are perceived. A novel big data classification paradigm is introduced by the work’s preprocessing, feature extraction and classification techniques. Data normalization is carried out at the preprocessing stage. The MapReduce framework is then utilized to manage the massive data. Statistical features (mean, median, min/max and SD), higher-order statistical features (skewness, kurtosis and enhanced entropy), and correlation-based features are all extracted prior to classification. The Bi-LSTM and deep maxout hybrid classification model classifies the data during the reduction stage. To assure classification accuracy, training will also be deployed by the new Hybrid Butterfly Positioned Coot Optimization (HBPCO) algorithm. The proposed method’s accuracy of 97.45% beats the methods of NN (85.13%), CNN (83.78%), RNN (78.37%), Bi-LSTM (82.43%) and SVM (87.83%).
S. A. Sahaaya Arul Mary, Lakshmi Kanthan Narayanan, S. Mohana, R. Senthamil Selvi, R. Karthik, and N. Ramya
Springer Nature Singapore
J.C. Miraclin Joyce Pamila, R. Senthamil Selvi, P. Santhi, and T.M. Nithya
Elsevier BV
Ramar Senthamil Selvi, Muniyappan Lakshapalam Valarmathi, and Prathima Devadas
Wiley
Many fields function with large databases constitute a high number of features. Feature selection strategies seek to exclude the features that are distracting, repetitive, or unnecessary, as they can degrade the classification results. Existing approaches lack the scalability needed to handle the datasets with millions of instances and they do not obtain favorable results in a timely manner. This study uses a unique feature selection approach based on an upgraded optimization model and deep machine learning‐based data classification. “(a) Feature extraction, (b) optimal feature selection, and (c) classification” are the three stages of the proposed model. Initially, the extracted big‐datasets are efficiently handled by the parallel pool map‐reduce architecture. Several features from the input big‐data are extracted using feature extraction (FE) approaches such as the suggested Tri‐Kernel principal component analysis (TK‐PCA), linear discriminant analysis, and linear square regression. Furthermore, the data obtained characteristics may contain data that is irrelevant, out‐of‐date, or noisy. The computing cost rises due to the larger feature space. As a result, the best features are selected using a new optimization technique known as Levy Adapted SLnO (LA‐SLnO), which is a superior variant of the original SLnO algorithm. This selection of appropriate features improves the classification accuracy. For classification, Convolutional Neural Network is used in this work. Finally, a comparative evaluation is undergone to validate the efficiency of the proposed model.
Suja Cherukullapurath Mana, G. Kalaiarasi, Yogitha R, L Suji Helen, and R. Senthamil Selvi
IEEE
Health care field is facing a lot of challenges due to the huge volume of people need medical support. The pandemic situation has created a lot of challenges to the healthcare field. This paper analyses how advancement in machine learning can be best utilized in improving health care services. Machine learning techniques are based on the idea of how systems can learn from the already existing data and can work with minimal human supervision. Thus machine learning has huge scope in healthcare. Machine learning algorithms can be effectively utilized for disease prediction, disease detection, providing personalized healthcare etc. These models can effectively predict the presence of diseases and also helps in detecting the diseases at earlier stage itself. Both supervised and unsupervised algorithms will be helpful in this field. Personalized healthcare applications aim to provide patient oriented healthcare services. Machine learning in combination with internet of things technologies made the personalized health care possible. The data collected from wearable devices and sensors can be effectively processed using machine learning algorithms and effective predictions can leads to quality of life improvements. In this chapter authors studies some of the existing applications of machine learning in healthcare field. Authors also propose a model that will add value to the existing applications.
J. C. Miraclin Joyce Pamila, Ajithkumar. A. K, and R.Senthamil Selvi
IEEE
Online communities collaborate and users share their views using online forums. The experience and ideas shared by the users in the forum are rich but finding relevant forum posts is laborious and frustrating. This research is targeted towards comparing a post at hand to find forum posts related to it. The conventional methods for identifying text similarity are not as efficient as they do not conceptualize the short text and lead to poor performance in finding related content. This paper proposes a novel scheme for the identification of related short forum posts in discussion forums. Contrary to the use of fixed vocabulary sets in the existing schemes, the proposed method uses distinct words in the forum post pair to form a joint word set dynamically. The knowledge base is used for deriving a raw semantic vector for each forum post. Further, the two semantic vectors are used for the computation of semantic similarity. The proposed framework uses inverted indexing to improve the efficiency of retrieving relevant forum posts by reducing the search space with synonyms of the forum post at hand. It is proven to be efficient in finding related forum posts in discussion forums with a recall of 90% through a set of tests conducted. It is also observed that precision can be improved with the Named Entity Recognition method.
R. Senthamil Selvi and M.L. Valarmathi
Wiley
Big data is the emerging trend in modern science that deals with datasets larger and more complex that cannot be dealt by the traditional data processing techniques. This seems to be the core of current technology and business. In practice, many criteria should be considered in the implementation of this technique. The way of the search space for finding potential subsets of features and prediction performance of classifiers are major important issue. To solve this issue, feature selection methods are introduced in the recent work. In the feature selection algorithm, Non‐deterministic Polynomial (NP) Hard, and searching the space has been becomes more difficult task. To solve this problem, this work provides a new approach toward feature selection based on Vertical Split Group FireFly (VSGFF) algorithm. FF algorithm gets its inspiration from social aspects of real fireflies. At the same time, VSGFF is proposed with the principle of multiple clusters to avoid privacy problem. Finally, Naïve Bayes (NB), K Nearest Neighbor (KNN), and Multi‐Layer Perceptron Neural Network (MLPNN) classification algorithms are proposed for big data classification. Experimental outcomes depicts that proposed technique improves classification accuracy by 4% compared to traditional vertical split firefly algorithm.
Ramar Senthamil Selvi and Muniyappan Lakshapalam Valarmathi
Mary Ann Liebert Inc
In this article, the proposed method develops a big data classification model with the aid of intelligent techniques. Here, the Parallel Pool Map reduce Framework is used for handling big data. The model involves three main phases, namely (1) feature extraction, (2) optimal feature selection, and (3) classification. For feature extraction, the well-known feature extraction techniques such as principle component analysis, linear discriminate analysis, and linear square regression are used. Since the length of feature vector tends to be high, the choice of the optimal features is complex task. Hence, the proposed model utilizes the optimal feature selection technology referred as Lion-based Firefly (L-FF) algorithm to select the optimal features. The main objective of this article is projected on minimizing the correlation between the selected features. It results in providing diverse information regarding the different classes of data. Once, the optimal features are selected, the classification algorithm called neural network (NN) is adopted, which effectively classify the data in an effective manner with the selected features. Furthermore, the proposed L-FF+NN model is compared with the traditional methods and proves the effectiveness over other methods. Experimental analysis shows that the proposed L-FF+NN model is 92%, 28%, 87%, 82%, and 78% superior to the state-of-art models such as GA+NN, FF+NN, PSO+NN, ABC+NN, and LA+NN, respectively.