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

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.

World University root node

School of Computer Science and Engineering

Vellore Campus

International Journal of Fuzzy System Applications

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

The wireless network technology with its gradual advancements is adapted by many domains like banking, governance, health services, etc., where most of their transactions include customer’s sensitive data. Along with advancement in the network technologies, digital crime is also increased at high rate. This may cause loss of sensitive data through malicious activities in the network. This concern of network security system can be effectively handled through the strong authentication mechanism. It can be observed that multiple authentication mechanisms that are currently used in the wireless digital network are vulnerable towards various advanced malicious techniques. Hence, there is a need to strengthen the existing authentication mechanisms or to design a stronger authentication mechanism. This paper considers the vulnerabilities identified in a two-factor authentication scheme for online medical services proposed by Li et al. and further proposes an enhanced two-factor authentication that overcomes the vulnerabilities analysed from Li et al. mechanism. Copyright © 2020 Inderscience Enterprises Ltd.

International Journal of Internet Manufacturing and Services

An enhanced two factor authentication for e-healthcare system

Mobile ad hoc Network (MANET) consists of a group of mobile nodes and the communication among them is done without any supporting centralised structure. Routing in MANETs is a difficult because of their dynamic features such as high mobility, constrained bandwidth and link failures due to energy loss. The objective of this work is to implement an intelligent routing protocol by selecting efficient nodes for routing the information. To provide good throughput in the network, Ant Colony Optimisation (ACO) based enhanced energy-efficient intelligent routing is proposed. Selecting the best intermediate hop for intelligent routing includes ACO technique which greatly reduces the network delay and link failures by validating the coordinator nodes. Coordinator nodes are picked by validating their metrics and Best Coordinator Nodes (BCN) are selected among them as good intermediate hops for intelligent routing. The performance is evaluated for the proposed protocol and the metrics considered for evaluation are delivery and loss rate of sent data, throughput and lifetime of the network, delay and energy consumption. © 2020 Inderscience Enterprises Ltd.

Department of Computer Applications

School of Information Technology and Engineering

International Journal of Grid and Utility Computing

ACO-based enhanced energy-efficient intelligent routing protocol for MANET

Performing secure and robust embedding and extracting in real time voice streams without deteriorating the voice quality is a great challenge. This paper aims on hiding the secret data bits in the voice packets without modifying any data in the cover thereby improving the embedding transparency and becomes robust against the steganalysis attacks using coverless approach. Initially a hash array is built with the frame size. The cover bit position is identified from the hashing function. The hash array is marked with a flag value to indicate that the particular sample consist of the secret message bit. The hash array is attached with the VoIP samples, at the receiver side the hash table is separated, and the secret bits are extracted based on the hash array. The experimental results conducted on a VoIP prototype proved to be simpler and effective in terms of the computational complexity, undetectability and voice quality at both sender and receiver end. © 2020 Sciendo. All rights reserved.

Department of Software and Systems Engineering

Fulltext

Cybernetics and Information Technologies

Coverless VoIP steganography using hash and hash

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

VIT Unitversity

Emerging Research in Data Engineering Systems and Computer Communications Advances in Intelligent Systems and Computing

Privacy-Preserving Data Mining in Spatiotemporal Databases Based on Mining Negative Association Rules

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

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

International Journal of Advanced Science and Technology

Network attack detection using weighted Dempster-Shafer evidence theory

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

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.