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 Computer Applications

A wireless based Mobile Ad Hoc Network, which is referred as MANET relies infrastructure-less based network composed nodes which are connected through wireless medium that permit communication among mobile nodes without any central base station. The dynamic behavior and unique characteristics of MANETs, providing a reliable and high stability route for transmitting either data or control packet to a shared working group becomes challenging. This paper presents seeks to propose a fuzzy-logic and Cross-layer optimized Multicast Route finding Protocol (C-MRP) which addresses and overcomes the issue of adhoc multicast on-demand table-use routing protocol. We integrate a light weight fuzzy set of rules to select optimal multiple routes to a group of destination nodes using route stability, accessible network bandwidth and node-to-node delay. The vital simulation result reports that C-MRP outperforms than existing solutions in all aspects. © 2017, Springer Science+Business Media, LLC, part of Springer Nature.

World University root node

School of Information Technology and Engineering

Vellore Campus

Cluster Computing

Discovery of optimal multicast routes in MANETs using cross-layer approach and fuzzy logic support system

Machine Learning approaches has the ability to make the system to learn by itself to solve many real-world problems. In the present decade, it has changed the prospects of computing to apply every other field to work it as a transdisciplinary field. On the other side, medical applications are one of the critical areas that need to be dealt with utmost care and importance and always there are plenty of open issues that have to be addressed. Blood transfusion is a critical process as many times the survival of mankind requires this an emergency. To make this process a simple one, machine learning approaches can be utilized. In this paper, an automated blood donation system is proposed to ease the emergency situation. For choosing the best algorithm, various machine learning algorithms are considered and the best one will be chosen among that to make this system to work as perfect as possible. The final result comparison will be presented in this paper to justify the chosen algorithmic approach. © 2019 IEEE.

Department of Software and Systems Engineering

Department of Information Technology

2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2019

Automated Blood Donation System using Machine Learning Approach

Many organizations store data to the cloud in a manner that anyone within the organization may share data with a member within the organization or outside the organization. They may also want to restrict data access and the mechanisms to control shared data access. Here a simple, efficient and verified approach to ensure the integrity of data stored in the cloud is proposed. Here, grouping algorithm along with EDAC-MACS is used to provide effective security control with revoking and decryption being more efficient with different algorithms for encryption as well as decryption. In the encryption mechanism a method is introduced which enables metadata generation for verification (signatures). However, consequent to this, the data owner identity is preserved and is not exposed to any other member or user of the cloud. The owners and the users are pushed to different groups without the user or the owner's concern to which policy or which group they belong to. The distinctive feature of this method along with encryption and decryption is that grouping algorithm is used along with EDAC-MACS to provide effective security control and also provides revoking algorithm for users who are suspected. Security analyses prove that this scheme is secure, and experiment results demonstrate that it is efficient. © 2019 IEEE.

Multi Authority Privacy Preserving Protocol in Cloud Computing Authentication using Grouping Algorithm and EDAC-MAC

VIT Unitversity

2018 Tenth International Conference on Advanced Computing (ICoAC)

Abnormality Detection in Human based on Hybrid Model - NB3 AkMGA

This paper reports the dual solutions in the MHD flow of a Casson fluid over a porous shrinking surface. In this study, viscous dissipation and Newtonian heating boundary condition effects are investigated. The governing partial differential equations are transformed into dimensionless form by the similarity transformation and then numerically solved by the Runge–Kutta–Gill procedure together with the shooting method. The non-dimensional velocity, temperature are displayed graphically for different controlling parameters. Dual similarity solutions are obtained for dimensionless velocity and temperature profiles. It is observed that the thermal boundary layer thickness increases as the Newtonian heating parameter increases in the presence of magnetic field. © 2018 Informa UK Limited, trading as Taylor &amp; Francis Group.

Mechanical

School of Mechanical Engineering

Chennai Campus

International Journal of Ambient Energy

Dual solutions for MHD Casson fluid over a shrinking sheet with Newtonian heating

Twitter, the social network which evolving faster and regular usage by millions of people and who become addicted to it. So spam playing a major role for Twitter users to distract them and grab their attention over them. Spammers actually detailed like who send unwanted and irrelevant messages or websites and promote them to several users. To overcome the problem many researchers proposed some ideas using some machine learning algorithms to detect the spammers. In this research work, a new hybrid approach is proposed to detect the streaming of Twitter spam in a real-time using the combination of a Decision tree, Particle Swarm Optimization and Genetic algorithm. Twitter has given access to the researchers to get tweets from its Twitter-API for real-time streaming of tweet data which they can get direct access to public tweets. Here 600 million tweets are created by using URL based security tool and further some features are extracted for representation of tweets in real-time detection of spam. In addition, our research results are compared with other hybrid algorithms which a better detection rate is given by our proposed work. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Wireless Personal Communications

Detecting Streaming of Twitter Spam Using Hybrid Method

An Unified Power Quality Conditioner (UPQC), is a combination of shunt and series Active Power Filter (APF), has become a standard accepted solution in the area of current and voltage harmonics mitigation of a power system network. This paper furnishes a comparative analysis of the power quality enhancement and optimization in positioning for three different VSI based UPQC system configurations namely: 3P3W UPQC topology, 3P4W UPQC based on 2C shunt inverter topology and 3P4W UPQC based on 4L shunt inverter topology, all supported with the Fuzzy Logic Controller (FLC), placed at different locations in a standard IEEE 14 bus system. Simulation results using MATLAB/SIMULINK provides the optimal position of UPQC for mitigating the harmonic issues in the standard IEEE 14 bus system.

International Journal of Engineering & Technology

Optimal Placement of Fuzzy based UPQC Topologies with Distributed Generation for Power Quality Enhancement in IEEE 14 Bus System

Sustainable Cities and Society

A multi-criteria decision making approach for the urban renewal in Southern India

One of the major tasks for the modern business is learning to use all of the data available to them in a way that is both expressive and actionable. However, the potential for using data produced by a website is often left unfamiliar, and as a result, the objectives and feedbacks of individual digital customers can be unheeded. On the average website, there is a plenty of data to be collected about who interrelates with your site and how. By leveraging all of this statistics, we can gain perceptions into customer actions. Machine learning methods can be used to define which customers may be fascinated in achieving a result on your site.

Department of Software Systems

School of Computer Science and Engineering

Fulltext

Predicting User Behaviour on E-Commerce Site Using Ann

In the context of big data, a recommendation system has been put forth as an efficient strategy for predicting the consumer’s pref-erences while rating items. Organizations that are functioning with multiple branches are in the imperative need for analyzing their multi-source big data to arrive novel decisions with respect to branch level and central level. In such circumstances, a multi-state business organi-zation would like to analyze their consumer preferences and enhance their decision-making activities based on the taste/preferences obtained from diversified data sources located in different places. One of the problems in current Item-based collaborative filtering approach is that users and their ratings have been considered uniformly while recording their preferences about target items. To improve the quality of rec-ommendations, the paper proposes various weighting strategies for arriving effective recommendation of items especially when the sources of data are multi-source in nature. For a multi-source data environment, the proposed strategies would be effective for validating the active user rating for a target item. To validate the novelty of the proposal, a Hadoop based big data eco-system with aid of Mahout has been con-structed and experimental investigations are carried out in a benchmark dataset.