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

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.

School of Computer Science and Engineering

Vellore Campus

World University root node

Optimization Theory Based on Neutrosophic and Plithogenic Sets

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

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.

Chemometrics and Intelligent Laboratory Systems

Clustering of mixed datasets using deep learning algorithm

Neutrosophy is the study of neutralities, which is an extension of discussing the truth of opinions. Neutrosophiclogic can be applied to any field, to provide the solution for indeterminacy problem. Many of the real-world datahave a problem of inconsistency, indeterminacy and incompleteness. Fuzzy sets provide a solution for uncertainties,and intuitionistic fuzzy sets handle incomplete information, but both concepts failed to handle indeterminateinformation. To handle this complicated situation, researchers require a powerful mathematical tool, naming,neutrosophic sets, which is a generalised concept of fuzzy and intuitionistic fuzzy sets. Neutrosophic sets providea solution for both incomplete and indeterminate information. It has mainly three degrees of membership suchas truth, indeterminacy and falsity. Boolean values are obtained from the three degrees of membership by cut relationmethod. Data items which contrast from other objects by their qualities are outliers. The weighted density outlierdetection method based on rough entropy calculates weights of each object and attribute. From the obtained weightedvalues, the threshold value is fixed to determine outliers. Experimental analysis of the proposed method hasbeen carried out with neutrosophic movie dataset to detect outliers and also compared with existing methods to proveits performance.

Fulltext

CAAI Transactions on Intelligence Technology

Outlier detection in neutrosophic sets by using rough entropy based weighted density method

Mobile edge computing (MEC) becomes popular as it offers cloud services and functionalities to the edge devices, to enhance the quality of service (QoS) of end-users by offloading their computationally intensive tasks. At the same time, the rise in the number of internet of things (IoT) objectives poses considerable cybersecurity issues owing to the latest rise in the existence of attacks. Presently, the development of deep learning and hardware technologies offers a way to detect the present traffic condition, data offloading, and cyber-attacks in edge networks. The utilization of DL models finds helpful in several domains in which the MEC provides the decisive beneficiary of the approach for traffic prediction and attack detection since a large quantity of data generated by IoT devices enables deep models to learn better than shallow approaches. In this view, this paper presents a new DL based traffic prediction with a data offloading mechanism with cyber-attack detection (DLTPDO-CD) technique. The proposed model involves three major processes traffic prediction, data offloading, and attack detection. Initially, bidirectional long short term memory (BiLSTM) based traffic prediction to enable the proficient data offloading process. Then, the adaptive sampling cross entropy (ASCE) technique is executed to maximize the network throughput by making decisions related to offloading users to the WiFi system. Finally, a deep belief network (DBN) optimized by a barnacles mating optimizer (BMO) algorithm called BMO-DBN is applied as a detection tool for cyberattacks in MEC. Extensive simulation is carried out to ensure the proficient performance of the DLTPDO-CD model. The experimental outcome stated the superiority of the presented model over the compared methods under different dimensions. © 2019 IEEE Computer Society. All rights reserved.

IEEE Access

Deep learning enabled data offloading with cyber attack detection model in mobile edge computing systems

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

School of Information Technology and Engineering

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

Neutrosophic components (NC) under addition and product form different algebraic structures over different intervals. In this paper authors for the first time define the usual product and sum operations on NC. Here four different NC are defined using the four different intervals: (0, 1), [0, 1), (0, 1] and [0, 1]. In the neutrosophic components we assume the truth value or the false value or the indeterminate value to be from the intervals (0, 1) or [0, 1) or (0, 1] or [0, 1]. All the operations defined on these neutrosophic components on the four intervals are symmetric. In all the four cases the NC collection happens to be a semigroup under product. All of them are torsion free semigroups or weakly torsion free semigroups. The NC defined on the interval [0, 1) happens to be a group under addition modulo 1. Further it is proved the NC defined on the interval [0, 1) is an infinite commutative ring under addition modulo 1 and usual product with infinite number of zero divisors and the ring has no unit element. We define multiset NC semigroup using the four intervals. Finally, we define n-multiplicity multiset NC semigroup for finite n and these two structures are semigroups under + modulo 1 and(M(S),+,×) and(n-M(S),+,×)are NC multiset semirings. Several interesting properties are discussed about these structures. © 2020 by the authors.

Symmetry

Neutrosophic components semigroups and multiset neutrosophic components semigroups

Likert scale is the most widely used psychometric scale for obtaining feedback. The major disadvantage of Likert scale is information distortion and information loss problem that arise due to its ordinal nature and closed format. Real-world responses are mostly inconsistent, imprecise and indeterminate depending on the customers’ emotions. To capture the responses realistically, the concept of neutrosophy (study of neutralities and indeterminacy) is used. Indeterminate Likert scale based on neutrosophy is introduced in this paper. Clustering according to customer feedback is an effective way of classifying customers and targeting them accordingly. Clustering algorithm for feedback obtained using indeterminate Likert scaling is proposed in this paper. While dealing real-world scenarios, indeterminate Likert scaling is better in capturing the responses accurately. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

VIT School for Agricultural Innovations and Advanced Learning

Soft Computing

Indeterminate Likert scale: feedback based on neutrosophy, its distance measures and clustering algorithm

Internet of Vehicle (IoV) technology is a promising application of Internet of Things (IoT) employed in smart city environment for smart communication. Traffic handling and non-delay tolerant communication services are expected from the communicating vehicles to improve its application efficiency. With the consideration of IoV in healthcare applications, this manuscript introduces a rapid connection aware neighbor selection (RCNS) for improving the efficiency in handling medical information. The selection process is induced by an instantaneous reward based learning algorithm (RLA) for ease of neighbor selection. The multi-attributes of the neighbors in the information transmitting route are estimated with the knowledge of their position. Distance and reward of the neighbors in the transmission direction are assessed using a beacon-assisted vehicle discovery that minimizes the impact of collision. Different from the other neighbor selection schemes, this method is relies on renewed attribute estimated at the time of transmission. This aids the transmitter to decide transmission and modify transmission attributes. The performance of the proposed neighbor selection method is estimated through extensive simulation using the metrics: delay, delivery ratio, selection overhead and outage. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Department of Analytics

Peer-to-Peer Networking and Applications

A reward based connectivity-aware IoV neighbor selection for improving reliability in healthcare information exchange

Technology development has improved to its extent for mobile monitoring that mainly concentrates on a new trend that named as the Internet of Vehicles (IoV).A large number of researchers and organizations are attracted by this technological development that promises to be huge commercial interest. Several health monitoring devices were developed for the purpose of humans to identify themselves about their health problems and state of the situation about their health. This paper proposes a new architecture design for the healthcare that mainly concentrates on heart diseases using the IoV technology. Here, the continuous health monitoring device is used to monitor heart-related diseases that connected with IoV for obtaining a new way to provide and gather information about their health condition and updated automatically to the IoV based ambulance and the reports are updated to doctors using the same IoV technique. The architecture explains the several protocols that were used for communication, managing and analysis of data and transmitting from vehicle to device. © 2019, IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature.

Health and Technology

A new healthcare architecture using IoV technology for continuous health monitoring system

In the present paper, the contributions of dislocations and grain boundaries to the broadening of diffraction line profile have been delineated. Contribution of each factor is studied on single as well as poly crystalline ‘virtual samples’ containing different dislocation configurations generated by molecular dynamics and evaluating the simulated X-ray diffraction line profiles of these samples. Individual contributions to diffraction line profiles by different sample features, such as domain size, dislocations, grain boundaries, have been isolated by deconvoluting the evaluated diffraction line profiles. The diffraction line profiles exhibited significant asymmetry and peak splitting in the case of dislocations with predominantly edge character. It has been shown that splitting of diffraction peaks carries information about the magnitude of Burger's vectors of the dislocations. Application of the variance method based on single peak fitting and Wilkens method based on whole powder pattern fitting of line profile analysis on the computed diffraction line profiles, showed that while accurate domain size estimation was possible, there was an under estimation of dislocation density at the high dislocation density and small grain size regime considered in the present study. © 2019 Elsevier B.V.