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

Internet of underwater things (IoUT) is an emerging technique with widespread applications. In such type of systems, efficient and secure use of network resources remains to be a greater challenge. This is due to the reason that it works on the basis of a network of smart interconnected objects. This creates the necessity for energy-aware and secure communication techniques across IoUT environment. In order to solve this issue, in this paper, a secure cloud-based solution for real-time monitoring and management of IoUT across smart cities is provided. The proposed system mainly focuses on secure data transmission with lesser energy requirements. This makes smart cities to go green and efficient. The major contribution of the proposed system is divided into two parts: First, an energy-aware efficient framework for IoUT system is defined. Next, we define an enhanced attribute-based encryption scheme with effective user attribute management for secure data transmission across IoUT systems. The simulations are performed, and the results state that the proposed approach provides better security and privacy measures than the existing system. In future, this work could be extended to deal with increasing amount of complexity measures with increased user attributes. © 2018, The Natural Computing Applications Forum.

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

VIT Unitversity

Department of Smart Computing

School of Information Technology and Engineering

Mathematics

School of Advanced Sciences

Chennai Campus

Neural Computing and Applications

A secure cloud-based solution for real-time monitoring and management of Internet of underwater things (IOUT)

Today total populace is seven billion or more, and it will be nine billion by 2050. Over 53% are ladies who are encountering broadened circumstance from destined to death. Being ladies, they face challenges in treatment decision and henceforth ladies are ignored and secluded from performing social duty regarding the purpose of purported defenseless ladies’ wellbeing. In numerous created nations lady's wellbeing, training, sustenance and financial power have shown that ladies are as yet substandard compared to men. Ladies live in rustic territories, are in charge of a hugepart of the residential work without temperate effect examination that is done in provincial regions. Ladies in urban communities can't progress encourage in assembling work. In this paper, savagery against ladies (VAW) and furthermore unique medical problems of ladies have been discussed. Also wplanned skeleton of an easy to use gateway named Women Empowerment which will contain distinctive laws identified with VAW and furthermore contains diverse wellbeing tips for ladies, which will help the provincial and also urban ladies. It incorporates crisis call framework, which will be dynamic by the casualty ladies when they are in threat. © IAEME Publication

World University root node

International Journal of Civil Engineering and Technology

Women abandon system

This paper considers an improvisation in software fault prediction research area using supervised classification algorithms and it mainly focuses to increase the performance of fault prediction. In this paper, we propose a hybrid prediction model using Naïve Bayes and k-nearest neighbour classification algorithm with vote ensemble method; in short it called as hNK. The goal of this model is to predict the best classification algorithm for software fault prediction based on the metrics and attributes of datasets. In the work, we have applied training sets and testing sets in hNK model with ensemble vote and we proposed the model to identify a suitable classification algorithm for fault prediction based on the accuracy and precision. We have achieved better results using hNK model for classifying supervised algorithms with different dataset. Copyright © 2018 Inderscience Enterprises Ltd.

International Journal of Intelligent Systems Technologies and Applications

Image pre-processing of icing transmission line based on fuzzy clustering

In present days the world has become vulnerable and unprotected from the surrounding environment. Every individual needs their own private protection and surveillance. Taking this point as inspiration, the authors’ have designed a surveillance robot which is affordable for both common man and industrialist or whoever it may be. Although existing approaches can achieve a reasonable precision, they rely on fixed narrow band of frequencies which can easily be jammed. In this article, a novel method has been proposed to use Bluetooth using an embedded system to send information to users. © IAEME Publication.

Secure wireless communication and surveillance system

2017 International conference of Electronics, Communication and Aerospace Technology (ICECA)

Diagnosis of thyroid disorder using infrared thermography

International Journal of Engineering Research

Survey on MR image segmentation using fuzzy C-Means algorithm

This paper presents the comprehensive view on the techniques used for predicting faults in various software developments. Using several classification methodologies we can produce the reliable software by reducing faults and failures. Prediction helps to identify the faults in upcoming modules using past results and training data and it reduce time for debugging. Method: In this study, we delivers the view about various techniques and methodologies used in fault prediction using soft computing techniques like artificial neural networks, fuzzy logic, genetic algorithm and machine learning algorithm like naïve bayes, random forest, and decision tree. Additionally we summarize the strength and weakness of those methods. Results: In this paper we acknowledged studies in soft computing and in machine learning algorithms. From this survey we further planned to produce a suitable hybrid algorithm for better fault prediction and to improve the quality in advanced object oriented software systems. © International Science Press.

International Journal of Control Theory and Applications

A literature review on software fault prediction using machine learning and soft computing techniques

Soft computing mainly used to find the solution which is an approximate and inexact solution. In this paper, we discussed various soft computing and data mining tools used in different applications to achieve better predictions. We initiated with techniques in soft computing that helps to the human as computational intelligence and the tools applied in various areas. This paper gives the idea about the tools and merits of using those data mining and soft computing techniques. © International Science Press.

A survey on data mining and soft computing tools

Tannery industries have been a major source of chromium contamination in and around vellore district, Tamil Nadu, India, which has led to a serious environmental threat to the inhabitants. Our present study focuses on the investigation of accumulation and distribution of chromium in the root and shoot of Mimusops elengi; a commonly found plant in and around the vicinity of tannery industries. The plant samples including whole plant and rhizosphere soil were collected from various distances from the origin point of the industry. All the samples were acid digested and analyzed through Atomic Absorption Spectrophotometery (AAS-Model Varian C). The accumulation and distribution of chromium was detected and it was observed that the concentration of chromium was higher in the roots and shoots of plants growing at a distance of 250m and 500m which was quiet above the permissible limit. © RJPT All right reserved.

Department of Bio-Sciences

School of Bio Sciences and Technology

Research Journal of Pharmacy and Technology

A Survey on the Accumulation of Chromium in Plants near Various Tannery Industries of Vellore District, T.N.

Background: Software fault prediction is an important task to improve the quality of software. It reduces the time and complexity between modules. Future software faults depend on previous faulty data. Statistical Analysis: The prediction models are created using method level metrics available from NASA datasets of structure oriented CM1 and PC1 datasets and object oriented KC1 and KC2 datasets. These metrics were applied in different classifier like Naive Bayes, J48, K-Star and Random forest to identify the best classifier for small dataset and large dataset based on both structure and object oriented method level metrics. Findings: This paper gives the study and analysis of various methodologies used for predicting faults in both structure and object oriented software. Based on the study, Naïve Bayes is utmost suitable for small datasets and random forest is suitable for large datasets based on the evaluation done by us using various methodologies driven by WEKA tool while equating precision, recall and accuracy. Application: This process of software fault prediction can be applied to E-Commerce applications, where accuracy requires much importance.

Fulltext

Indian Journal of Science and Technology

An Approach for Software Fault Prediction to Measure the Quality of Different Prediction Methodologies using Software Metrics

Software fault prediction is the most efficient methodology to improve the quality of the products. To improve the quality it is essential to find the error or fault as quick as possible. To determine and advance the development of product there are different prediction approaches are available like correction cost prediction, test effort prediction, software fault prediction (SFP), security prediction and so on. This paper survey the variety of methods and metrics used to improve the quality of the object oriented software. We compared different prediction models and proposed the methodology with the datasets they used for easy understanding. Software fault prediction using different techniques to improve the quality and the error free software delivery. This paper gives overview about the prediction models for software in design and implementation phase and also it discuss trends which are currently used software fault prediction.