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

Institute for Industry and International Programme

Vellore Campus

Department of English

School of Social Sciences & Languages

International Journal of Advanced Trends in Computer Science and Engineering

Classification of Errors in the Project Reports of Engineering Aspirants

Recent year sentiment analysis plays a vital role to understand the user emotions. As comprehension of the conclusion falls into place without any issues for a human, the genuine challenge is to prepare a machine to have the capacity to do that. The massive volume of data present in the web-based services today accomplishes continuous, persistent information about any item. This information as a precious asset because for every one of the individuals who are worried about general behavior, feelings, or perspectives. The paper presented the semantic meaning of the feature extraction method using low learning method of word embedding techniques. Word2vec technologies increase the similarities between opinion words and aspect words. The CNN (Convolution Neural Network) method classify sentiment polarity with prior knowledge of words. The classification accuracy and time complexities compared with existing machine learning methods. © 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved.

World University root node

Journal of Advanced Research in Dynamical and Control Systems

Aspect level sentiment analysis in deep learning technique using CNN

The smartphones that we utilize have changed the way the content consumption of a user from web pages to native mobile app. The mobile application ecosystem has been growing rapidly with millions of app and many are using the mobile devices as the primary source for reaching the data. We are presenting a technique for crawling the content from the mobile applications for presenting the mobile user a more relevant data and to enhance the user experience. In this, a procedure to crawl the sandbox native mobile app is proposed to empower the app data to be discoverable. The sandbox native mobile app data can be crawled by using the combination technique of Static analysis, GUI based navigation and scraping the screens. Here the frameworks, which get the data, from the native mobile application for Android and iOS smartphone. The result obtains would be the app data available for the app data indexing which a searching engine can consume. © 2018 American Scientific Publishers.

Department of Information Security

School of Computer Science and Engineering

Journal of Computational and Theoretical Nanoscience

A Method for Empowering Native Mobile App Data Discovery

Sensor cloud is the combination of wireless sensor networks and Cloud computing which performs real-time data acquisition and processing in a distributed environment. The infrastructure enables collection, processing, sharing, visualisation, archival and searching of large amounts of sensor data. It makes it possible for sharing computing resources and data, on an unprecedented scale, among an infinite number of geographically distributed groups. The paper provides an implementation of efficient web based technology for processing large, heterogeneous set of data based on Open Geospatial Consortium Standards. The proposed interface uses the sensor cloud middleware and wireless sensors for distributed applications that process large amounts of data. Sensor cloud middleware in the proposed work uses Open Stack and Hadoop framework along with utilities like generic modelling environment, HBase and Zabbix. The user interface with features such as viewing sensor data, scheduling sensors for data collection and alerting users have also been discussed.

Department of Software Systems

Fulltext

International Journal of Communication Networks and Distributed Systems

Web user interface based on OGC standards for sensor cloud using big data

This paper aims at implementing an efficient sensor middleware for maintaining and managing big data and analysing the efficiency of such a sensor middleware. The work focused on designing of sensor middleware and performance analysis of the sensor middleware. The Hadoop framework, which was used as a sensor middleware, was responsible for maintaining and managing the distributed sensor environment. A prominent monitoring tool such as Zabbix was used to monitor the Hadoop processes and provide alerts for custom trigger events. Performance analysis for the sensor middleware such as Hadoop was done using tools such as collectl and colplot. Performance analysis of Hadoop cluster was performed using an evaluation metric such as total runtime of the job. A brief discussion on the Hadoop tuning tool such as starfish, that makes the Hadoop cluster efficient through configuration changes, is also presented.

VIT Unitversity

International Journal of Information and Communication Technology

Analysis of sensor middleware using big data as a cloud computing utility

Assessing the attitude towards research is the primary problem of this research. Attitude towards research has multiple dimensions. Comprehending the attitudes of the students towards research would bridge the gap between the research supervisor and his ward. Assessing the attitude and diagnosing the areas that needs to be addressed is the need of the hour to find solution to various problems of doctoral students. The study aims to assess the attitude towards research among the doctoral students and find the various dimensions of attitude. This study also explores the top reasons that have high level of importance in their decision to join doctoral course. It also aims to find various factors that have influenced the students to join doctoral program. Acquiring sufficient knowledge to carry forward the research is a key aspect in the research progress. This research also identifies the areas that need trainings at different levels for the students to meet the requirements to complete the program successfully. The methodology to carry forward this research is a self-developed questionnaire to assess the attitude towards research. The sample of this study consists of 159 students who have completed one year of course work in their doctoral program. The factor analysis yielded 6 factors of attitude towards research. The factors are love for research, research fear, research usefulness, difficulties in research, importance of research and benefits of research. Top three reasons that have high level importance in their decision to join doctoral program are interest in subject domain, wanting to work in a first grade research group and availability of an interesting project. Top three factors that influence the student towards doctoral course are teachers, an enthusiastic mentor and friends. Three important training areas are in article writing and publications, identifying journals for publication and communication skills. © IAEME Publication.

Department of Digital Communications

School of Information Technology and Engineering

International Journal of Civil Engineering and Technology

A study on attitude towards research among the doctoral students

In the era of advanced technology in healthcare system,data are produced in huge amount from varied sources like clinical,e-health record,prescription,tests report and test image records. The greatest challenging is to extract knowledge from raw data and provide useful information to the medical researchers and practitioners,who in turn put it to work in real life scenario which will benefit a common man. Big Data analytics in healthcare is used to predict disease from historical data such that it predict epidemics disease,adverse drug reaction from social media improves quality of life and avoid preventable decease in earlier. In traditional system handling varied health care data for collection,storage and processing is complex,and this complication leads more opportunities for big data analytics. The main objective of this paper is to focus on various types of analysis and techniques to solve the big data problem in healthcare system. © 2016,International Journal of Pharmacy and Technology. All rights reserved.

International Journal of Pharmacy and Technology

Big data analytics in healthcare system for diverse perspectives

Migration of the healthcare applications to cloud computing needs certain features to be fulfilled for complete user satisfaction. The users quench for satisfaction can be fulfilled by attaining the cloud computing characteristics such as data storage, accessibility, information sharing, interoperability, security, service availability, scalability, resiliency, resource management, and monitoring. The work is a survey on the cloud computing characteristics and its implementation in healthcare applications.

Health and Technology

Survey on HealthCloud characteristics

Distributed Data Mining is an interesting research community with respect to next generation of computing platform such as SOA, Grid and Cloud etc. and important paradigms such as Peer to Peer. There are mainly three types of Distributed Data Mining algorithms: DDM based parallel data mining agent, DDM based on mete learning, DDM based on Grid. In this paper analyses the key issues in Distributed Data Mining and a detailed survey on three kinds of DDM algorithms, its performance and its various pros and cons. The paper finally comes with an essential characteristics needed for a good DDM algorithm. © 2006-2014 Asian Research Publishing Network (ARPN).