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Papers by Professor, CSE Veltech, Chennai

Learning and Analytics in Intelligent Systems, 2019

Recently, Industries are focusing on cultivar prediction of customer classes for the promotion of... more Recently, Industries are focusing on cultivar prediction of customer classes for the promotion of their product for increasing the profit. The prediction of customer class is a time consuming process and may not be accurate while performing manually. By considering these aspects, this paper proposes the usage of machine learning algorithms for predicting the customer cultivar of Wine Access. This paper uses multivariate Wine data set extracted from UCI machine learning repository and is subjected to the feature selection methods like Random Forest, Forward feature selection and Backward elimination. The optimized dimensionality reduced dataset from each of the above methods are processed with various classifiers like Logistic Regressor, K-Nearest Neighbor (KNN), Random Forest, Support Vector Machine (SVM), Naive Bayes, Decision Tree and Kernel SVM. We have achieved the accurate cultivar prediction in two ways. Firstly, the dimensionality reduction is done using three feature selection methods which results in the existence of reasonable components to predict the dependent variable cultivar. Secondly, the prediction of customer class is done for various classifiers to compare the accuracy. The performance analysis is done by implementing python scripts in Anaconda Spyder Navigator. The better cultivar prediction is done by examining the metrics like Precision, Recall, FScore and Accuracy. Experimental Result shows that maximum accuracy of 97.2% is obtained for Random Projection with SVM, Decision Tree and Random Forest Classifier.

Ingénierie des Systèmes d Inf., 2021

Digital Technology is becoming increasingly essential to organizations. Related knowledge is impo... more Digital Technology is becoming increasingly essential to organizations. Related knowledge is important for a company to allow optimal use of its IT services. The use of Big Data is relatively new to this field. Handling Big data is not, at this stage, a problem for large business organizations in particular; it has also become a challenge for small and medium-sized businesses. Although Semantic Web analysis is largely focused on fundamental advances that are expected to make the Semantic Web a reality, there has not been much work done to demonstrate the feasibility and effect of the Semantic Web on business issues. The infrastructure of electronic information executives and business types has provided various enhancements for companies, such as the automated process of buying and selling products. Nevertheless, undertakings are checked for the multifaceted nature of the extension required to deal with an ever-increasing number of electronic details and procedures. This paper sugges...

Traitement du Signal, 2021

The advent of social networking and the internet has resulted in a huge shift in how consumers ex... more The advent of social networking and the internet has resulted in a huge shift in how consumers express their loyalty and where firms acquire a reputation. Customers and businesses frequently leave comments, and entrepreneurs do the same. These write-ups may be useful to those with the ability to analyse them. However, analysing textual content without the use of computers and the associated tools is time-consuming and difficult. The goal of Sentiment Analysis (SA) is to discover client feedback, points of view, or complaints that describe the product in a more negative or optimistic light. You can expect this to be a result based on this data if you merely read and assess feedback or examine ratings. There was a time when only the use of standard techniques, such as linear regression and Support Vector Machines (SVM), was effective for the task of automatically discovering knowledge from written explanations, but the older approaches have now been mostly replaced by deep neural netw...

The International Arab Journal of Information Technology, 2022

In the ubiquitously connected world of IT infrastructure, Intrusion Detection System (IDS) plays ... more In the ubiquitously connected world of IT infrastructure, Intrusion Detection System (IDS) plays vital role. IDS is considered as a critical component of security infrastructure and is implemented either through hardware or software devices and can detect malicious activities in a networked environment. To detect or prevent network attacks, Network Intrusion Detection (NID) system may be equipped with machine learning algorithms to achieve better accuracy and faster detection speed. Analyzing different attacks effectively through Dimensionality Reduction Algorithms is an efficient mechanism. The significance of these algorithms is they improvise feature selection from huge datasets. Also through this the learning speed is enhanced. Speed is a crucial parameter in the success of network intrusion detection systems for defending reactions. In this paper open source datasets Knowledge Discovery in Databases (KDD CUP) dataset and 10% KDD CUP dataset are employed for experimentation. The...

Eurasip Journal on Image and Video Processing, Feb 11, 2014

Telemedicine integrates information and communication technologies in providing clinical services... more Telemedicine integrates information and communication technologies in providing clinical services to health professionals in different places. Medical images are required to be transmitted for diagnosis and opinion as part of the telemedicine process. Thus, telemedicine challenges include limited bandwidth and large amount of diagnostic data. Content-based image retrieval is used in retrieving relevant images from the database, and image compression addresses the problem of limited bandwidth. This paper proposes a novel method to enable telemedicine using soft computing approaches. In the present study, images are compressed to minimize bandwidth utilization, and compressed images similar to the query medical image are retrieved using a novel feature extraction and a genetic optimized classifier. The effectiveness of compressed image retrieval on magnetic resonance scan images of stroke patients is presented in this study.

Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in v... more Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different solutions for incorporating the appearance and motion information. We study a number of ways of fusing ConvNet towers both spatially and temporally in order to best take advantage of this spatio-temporal information. We make the following findings: (i) that rather than fusing at the softmax layer, a spatial and temporal network can be fused at a convolution layer without loss of performance, but with a substantial saving in parameters; (ii) that it is better to fuse such networks spatially at the last convolutional layer than earlier, and that additionally fusing at the class prediction layer can boost accuracy; finally (iii) that pooling of abstract convolutional features over spatiotemporal neighbourhoods further boosts performance. Based on these studies we propose a new ConvNet architecture for spatiotemporal fusion of video snippets, and evaluate its performance on standard benchmarks where this architecture achieves state-of-the-art results. Our code and models are available at