Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Department of Database Systems

Data products are designed to generate information for public or business policy, and research and public information [1]. When data mining and database techniques are advanced, the security of classifying sensitive data in a database becomes a primary issue when providing information to data snoopers. Association rule hiding [2] investigation is a ground-breaking and mainstream apparatus for finding connections covered up in expansive informational indexes. The relationships can be shown as random item-sets or association rules. Rules are categorized as sensitive and non-sensitive depending upon the type of information it can reveal to an adversary. The rule is liable if its probability of disclosure exceeds a given threshold. PPDM is an important tool that can be applied in different fields such as healthcare, e-commerce, product research. [3]The rule is liable if the probability of disclosure exceeds the given threshold. (PPDM)privacy preserving Data Mining is the mainframe that can be applied in different fields, for example, e-healthcare, product research, e-commerce etc. There is a need to hide sensitive rules of the association. The main approach is to decrease the rules’ support or confidence. This is achieved by altering the details of the transaction. This generates side effects such as the generation of new rules and non-sensitive rules are falsely hidden [4]. This article proposes an efficient algorithm to hide association rules using the new support formula. This formula has been tested on the value of range 20 to 500. Whenever the algorithm is applied, the range is checked and the corresponding value is entered [5]. © IJSTR 2020.

School of Computer Science and Engineering

Vellore Campus

World University root node

International Journal of Scientific and Technology Research

Optimized support balance model for sensitive rule hiding

The process of recognizing patterns, collecting knowledge from massive databases is called data mining. An object which does not obey and deviates from other objects by their characteristics or behavior are known as outliers. Research works carried out so far on outlier detection were focused only on numerical data, categorical data, and in single universal sets. The main goal of this article is to detect outliers significant in two universal sets by applying the intuitionistic fuzzy cut relationship based on membership and non-membership values. The proposed method, weighted density outlier detection, is based on rough entropy, and is employed to detect outliers. Since it is unsupervised, without considering class labels of decision attributes, weighted density values for all conditional attributes and objects are calculated to detect outliers. For experimental analysis, the Iris dataset from the UCI repository is taken to detect outliers, and comparisons have been made with existing algorithms to prove its efficiency. Copyright © 2020 IGI Global.

International Journal of Fuzzy System Applications

An intuitionistic fuzzy approach with rough entropy measure to detect outliers in two universal sets

Feature selection algorithms play a crucial role in any machine learning problem. Choice of the best algorithm yields optimal subset of features thereby increasing the accuracy and reducing the time required for training. In the case of high dimensional datasets it is also advantageous in removing the irrelevant features. This paper presents a novel approach of surveying the popular feature selection algorithms specifically used in medical data classification, by considering the following types of medical data—signals, images and numerical. This work shall be very useful to researchers in collecting first hand information since we have reviewed the various aspects such as—available medical datasets, feature selection techniques, choice of classifier, issues in identifying the feature selection technique, analysis of major feature selection methodologies and detailed mechanisms thereof. We have also performed sample experimentation on the standard medical datasets from UCI and analyzed the effects on time and performance by employing 12 popular classifiers. The results demonstrate improved accuracy and lowered computation times. © 2020, Springer Nature Singapore Pte Ltd.

Lecture Notes in Electrical Engineering

Feature Selection Algorithms in Medical Data Classification: A Brief Survey and Experimentation

With increasing data on the Internet, it is becoming difficult to analyze every bit and make sure it can be used efficiently for all the businesses. One useful technique using Natural Language Processing (NLP) is sentiment analysis. Various algorithms can be used to classify textual data based on various scales ranging from just positive-negative, positive-neutral-negative to a wide spectrum of emotions. While a lot of work has been done on text, only a lesser amount of research has been done on audio datasets. An audio file contains more features that can be extracted from its amplitude and frequency than a plain text file. The neutrosophic set is symmetric in nature, and similarly refined neutrosophic set that has the refined indeterminacies I1 and I2 in the middle between the extremes Truth T and False F. Neutrosophy which deals with the concept of indeterminacy is another not so explored topic in NLP. Though neutrosophy has been used in sentiment analysis of textual data, it has not been used in speech sentiment analysis. We have proposed a novel framework that performs sentiment analysis on audio files by calculating their Single-Valued Neutrosophic Sets (SVNS) and clustering them into positive-neutral-negative and combines these results with those obtained by performing sentiment analysis on the text files of those audio. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Fulltext

Symmetry

A novel framework using neutrosophy for integrated speech and text sentiment analysis

Single-valued neutrosophic sets (SVNSs) have been used in scientific problems but not in sociological analysis. This chapter provides a modern-day real-world application of Neutrosophy in sentiment analysis of the #MeToo movement. Sentiment analysis categorizes people's opinions as positive or negative, and the neutral part is generally ignored even in fuzzy sentiment analysis. To capture the prevailing indeterminate feelings, Neutrosophy is used. Over 400,000 tweets of the #MeToo movement were separately represented with positive, indeterminate, negative memberships as an SVNS, which gives an accurate evaluation of the tweets. Clustering of these tuples into three major clusters using a K-means algorithm displays indeterminate as the largest cluster. To increase the accuracy in predicting the indeterminate polarity, the data was further classified into eight classes. Training data was used to model k-nearest neighbor and support vector machine classifiers. A comparative analysis between the classifiers was done. © 2020 Elsevier Inc. All rights reserved.

Optimization Theory Based on Neutrosophic and Plithogenic Sets

Sentiment analysis of the #MeToo movement using neutrosophy: Application of single-valued neutrosophic sets

Sequence alignment is a problem in bioinformatics that involves arranging sequences of proteins, RNA or DNA so that similar regions between two or more sequences may be determined. The Smith-Waterman algorithm is a key algorithm for aligning sequences. This paper uses the OpenMP application-programming interface along with the Single-Instruction Multiple-Data (SIMD) instructions. Advanced Vector Instructions 2 (AVX2) is used to implement the SIMD paradigm. It utilizes both fine-level and coarse-level parallelism to improve resource utilization without requiring support from multiple nodes in a distributed memory system. The algorithm shows a multifold decrease in execution time in comparison to an implementation that is sequentially executed. © 2020, Springer Nature Singapore Pte Ltd.

Communications in Computer and Information Science

Time Improvement of Smith-Waterman Algorithm Using OpenMP and SIMD

An Intrusion Detection System (IDS) identifies whether the input traffic is normal or an intrusive activity. To improve the performance of the IDS this paper proposes a classifier fusion algorithm, based on Weighted Dempster-Shafer theory. The highlight of this classifier fusion algorithm is, it considers the input data also in the final decision process by taking the product of weighted score and anomaly score as the Basic Probability Assignment (BPA) value for Weighted Dempster-Shafer theory. The proposed system has a multiple IDS unit (MIU) with five IDS, each having different algorithms of anomaly-based and misuse-based detection techniques. The selected features of input traffic data are passed to each IDS in the MIU and it gives out five anomaly scores for five attack types. The maximum anomaly score got from the IDS and a new parameter called weighted score is used in the Weighted Dempster-Shafer theory to take the final decision. The weighted score is obtained by passing the maximum anomaly score and the selected input traffic to the Neural Network learner unit. NSL-KDD dataset with 41 features is used to evaluate the performance of the proposed system. The best 27 features out of 41 is selected using Genetic Algorithm to reduce the computation time. The proposed system outperforms in terms of detection rate and false alarm rate when compared with existing fusion methods. © 2019 SERSC.

Department of Analytics

Department of Digital Communications

School of Information Technology and Engineering

International Journal of Advanced Science and Technology

Network attack detection using weighted Dempster-Shafer evidence theory

Drought is a function of time as well as climate variables such as temperature and precipitation. The process of drought forming is slow and it manifests at different time scales, which adversely affects the economy of a country. The identification and characterization of droughts at various spatiotemporal scales are of great importance. It helps in the planning and management of water resources, policymaking and agribusiness industries. In the present paper, the Cauvery River basin is chosen as a study area to analyze the changes in the frequency distribution of extreme droughts and duration, with the combined effect of evapotranspiration and rainfall. The drought indices such as Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI) are implemented on monthly rainfall data and potential evapotranspiration of resolution 0.25° × 0.25° long./lat. for the period 1931–2010. The results reveal that the frequency of the extreme droughts over the basin has significantly increased over the post-era of global warming. The increased rate of extreme droughts is particularly evident in downstream of the basin, mainly due to the increase in temperature and deficit rainfall. Further, the implementation of continuous wavelet transform reveals that SPI at 3-(SPI-3) and 12-(SPI-12) month scale are associated with extended reconstruction of sea surface temperature (ERSST) in anti-phase and in-phase, respectively. It is concluded that the in-phase association of SPI-12 and ERSST enhances the drought situation compared to the anti-phase link of SPI-3 and ERSST.

Natural Hazards

Spatiotemporal characteristics of extreme droughts and their association with sea surface temperature over the Cauvery River basin, India

The proposed work is how to function a robotic arm using text command. That means if we write Left, Right, Front or Back on the screen or some other flat surface. The robot tries to recognize that text by using Optical Character Recognition (OCR) and performs the meaning of that text written. The operation of that text written is programmed in the robot so that whenever it detects the text, it performs the operation. © 2020 IEEE.

Department of Smart Computing

Department of Information Technology

International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2020

Text Message Based Robotic ARM Control Using OCR

The performance of a clustering algorithm is highly dependent on the quality and quantity of the training dataset. Deep learning is one of the most popular and successful technique for clustering of datasets with high quality. Typically, most of the datasets contain mixed numeric and categorical data attributes. The clustering of such different types of data is a complex issue. Deep learning methods, the state-of-the-art classifiers, with better learning procedures and computational resources, can fill these gaps. To improve the robustness of clusters, we propose a Constraint-Based Deep Convolutional Generative Adversarial Network (CB-DCGANs) framework for generating simulated data to augment the training set to improve the performance of the clustering algorithm. We evaluated the performance of an end-to-end Deep Convolutional Neural Network (DCNN) in detecting the clusters from given datasets. The results from CB-DCGANs with DCNN yielded baseline accuracies of 0.8853 for heart disease dataset. In chemoinformatics datasets proposed algorithm yielded accuracies of 0.965 for kaggle dataset, 0.987 for factors dataset, 0.952 for kinase dataset. This study shows that using generative adversarial networks for clustering augmentation can significantly improve performance, especially in real-life applications.