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

In the last decade, opinion mining and sentiment analysis have been the subject of fascinating interdisciplinary research. Alongside the evolution of social media networks, the sheer volume of social media text available for sentiment analysis has increased multi-fold, leading to a formidable corpus. Sentiment analysis of tweets have been carried out to gauge public opinion on breaking news, various policies, legislations, personalities and social movements. Fuzzy logic has been used in the sentiment analysis of twitter data, whereas neutrosophy which factors in the concept of indeterminacy has not been used to analyse tweets. In this paper, the concept of multi refined neutrosophic set (MRNS) with two positive, three indeterminate and two negative memberships is proposed. Single valued neutrosophic set (SVNS), triple refined indeterminate neutrosophic set (TRINS) and MRNS have been used in the sentiment analysis of tweets on ten different topics. Eight of these topics chosen for sentiment analysis are related to Indian scenario and two topics to international scenario. A comparative analysis of the methods show that the approach with MRNS provides better refinement to the indeterminacy present in the data. © 2019 Elsevier B.V.

School of Computer Science and Engineering

Vellore Campus

VIT School for Agricultural Innovations and Advanced Learning

World University root node

Computers in Industry

Sentiment analysis of tweets using refined neutrosophic sets

Cell phones have become a necessity for many people throughout the world. The ability to keep in touch with family, business associates, and access to email are only a few of the reasons for the increasing importance of cell phones. However, the mobile-phones in early times were bulky, restrictive to only some features and worked only in areas where there was a good connection. All these problems were resolved by integrating a processor within a cell-phone. The processor is the central hub of your smartphone. It receives and executes every command, performing billions of calculations per second. The effectiveness of the processor directly affects every application you run, whether it's the camera, the music player, or just a simple email program. In the following journal, we have discussed the distinctive features, merits, and demerits of the latest mobile phone processors of different Tech companies. © 2020 IEEE.

2020 7th International Conference on Smart Structures and Systems, ICSSS 2020

A comparative study on the recent smart mobile phone processors

Wireless sensor networks are used in various fields, such as military, healthcare, agriculture, environment monitoring, Monitoring and preparation of critical infrastructure. However, due to some of the restrictions of WSNs on storage, power consumption, communication, computation, scalable, security and privacy; people are looking forward cloud environment with large storage framework for the processing and storing of real time data. The cloud environment is providing large storage space and better services to the people in scalable manner. Sensor-cloud infrastructure is popular, providing an open, flexible and reconfigurable platform for many monitoring and control applications. In this work presenting a sensor-cloud infrastructure for storing data related to agriculture, Infrastructure that shows how to transfer data from WSNs to the cloud. In addition we addressed malicious attacks, issues and challenges in sensor cloud infrastructure. The idea of our proposed work is to collect all the samples for the soil. We choose the most favored plants around the India, then, select three plants, namely rice, sugarcane and wheat. After selecting the plants we collect data related to agriculture irrigation from different types of sensors such as soil moisture, temperature, humidity, and soil PH, CO2 and light sensors. Based on the data collected, our proposed system advice to farmers that usage of pesticides, fertilizers which are most suitable to their lands at the same time, we integrate to improve agriculture productivity in India. © 2019 by Advance Scientific Research.

Journal of Critical Reviews

A survey on security issues in sensor cloud environment for agriculture irrigation management system

Cryptocurrency such as Bitcoin, Ethereum etc. is increasingly well known nowadays among cryptocurrency miners and enthusiasts. Our goal is to estimate the various cryptocurrency accurately considering various parameters that impact the cost. We use various deep learning and machine learning algorithms to be able to recognize the value pattern on the closing value thus giving us the predicted price. The point of this is to determine the precision of cryptocurrency values utilizing various AI algorithms and predict their estimated prices. The dataset that we will be using contains consolidated financial information for the top 10 cryptocurrencies sorted by Market Cap which also have various attributes for each type of cryptocurrency such as open, close, high, low, value and date. Therefore, we are going to use Linear Regression, SGD Regression, Support Vector Regression, and LSTM algorithms to predict the various cryptocurrencies prices. © 2020, World Academy of Research in Science and Engineering. All rights reserved.

International Journal of Advanced Trends in Computer Science and Engineering

An approach to cryptocurrency price prediction using deep learning

Purpose: Erythemato-squamous disease (ESD) is one of the complex diseases related to the dermatology field. Due to common morphological features, the diagnosis of ESDs become stringent and leads to inconsistency. Besides, diagnosis has been done on the basis of inculcated visible symptoms pertinent with the expertise of the physician. Hence, ontology construction for ESD is essential to ensure credibility, consistency, to resolve lack of time, labor and competence and to diminish human error. Design/methodology/approach: This paper presents the design of an automatic ontology framework through data mining techniques and subsequently depicts the diagnosis of ESD using the available knowledge- and rule-based system. Findings: The rule language (Semantic Web Rule Language) and rule engine (Jess and Drools) have been integrated to explore the severity of the ESD and foresee the most appropriate class to be suggested. Social implications: In this paper, the authors identify the efficiency of the rule engine and investigate the performance of the computational techniques in predicting ESD using three different measures. Originality/value: Primarily, the approach assesses transfer time for total number of axioms exported to rule engine (Jess and Drools) while the other approach measures the number of inferred axioms (process time) using the rule engine while the third measure calculates the time to translate the inferred axioms to OWL knowledge (execution time). © 2020, Emerald Publishing Limited.

International Journal of Intelligent Unmanned Systems

A comparative study on the performance of rule engines in automated ontology learning: a case study with erythemato-squamous disease (ESD)

The microarray technology permits simultaneous monitoring of several thousand gene expressions for each sample. However, classifying such samples into their distinct classes may be quite challenging owing to the higher number of genes (the features) over the actual samples. As a consequence, there is a need to make an investigation of the new and the robust techniques of machine learning that can accurately classify the microarray data. The B cells further give some humoral immunity by means of distinguishing into the Plasma Cells (PCs) that are antibody-secreting which is a process that needs a cellular division linked to the DeoxyRibonucleic Acid (DNA) hypomethylation. Classification of cancer is another critical issue in the analysis of cancer treatment. A very effective method in the classification of cancer is its gene selection. But choosing the gene subset that increases the accuracy may be a Non-Deterministic Polynomial (NP)-hard problem. For this work, a methodology was proposed for choosing an ideal set of genes which permit classification of the class of disease based on gene expression along with a good accuracy. The proposed method was compared with feature selection method that employed the Genetic Algorithm (GA).The feature selection of the proposed work is using the Artificial Bee Colony (ABC) algorithm with feature selection that is Levy search based. The results have proved that this new technique had an increase in accuracy of classification which was compared to the Genetic Algorithm and also the Artificial Bee Colony based technique. This also had a very fast convergence. © 2020, World Academy of Research in Science and Engineering. All rights reserved.

Hybrid artificial bee colony based feature selection in bone marrow plasma cell gene expression data

New Trends in Computational Vision and Bio-inspired Computing

Big Data Oriented Fuzzy Based Continuous Reputation Systems for VANET

Multi-faceted and Multi-algorithmic Framework (MFMA) for Finger Knuckle Biometrics

Erythemato-squamous disease (ESD) is one of the complex diseases in the dermatology field, the diagnosis of which is challenging, due to common morphological features and often leads to inconsistent results. Besides, diagnosis has been done on the basis of inculcated visible symptoms pertinent with the expertise of the physician. Hence, ontology construction for prediction of erythemato-squamous disease through data mining techniques was believed to yield a clear representation of the relationships between the disease, symptoms and course of treatment. However, the classification accuracy required to be high in order to obtain a precise ontology. This required identifying the correct set of optimal features required to predict ESD. This paper proposes the Gain based Relative-Non-Redundant Attribute selection approach for diagnosis of ESD. This methodology yielded 98.1% classification accuracy with Adaboost algorithm that executed J48 as the base classifier. The feature selection approach revealed an optimal feature set comprising of 19 selected features. Copyright © 2020 Inderscience Enterprises Ltd.

International Journal of Business Intelligence and Data Mining

An automated ontology learning for benchmarking classifier models through gain-based relative-non-redundant feature selection: A case-study with erythemato-squamous disease

Data mining approaches have been tried in e-learning systems for information optimization and knowledge extraction to make decisions. In recent years, the recommendation system has gained popularity in every field be it e-commerce, entertainment, sports, healthcare, news, etc. However, in e-learning system, the recommender systems were not effectively utilized in comparison to other domains and thus emerged as a bottleneck for almost all e-learning systems for not offering flexible delivery of the learning resources. Current e-learning systems lack personalization features, and the information is presented in a static way despite their varying learning objectives and needs. The aim of recommender system is to personalize the information with respect to learner interest. The objective of this study is to highlight various algorithmic techniques that can be used to improve information retrieval process to provide effective recommendations to learners for improving their performance and satisfaction level.