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

“Media bias” or specifically “social media bias” has been one of the major concerns of social media. The most popular social networking sites are Facebook and Twitter in the recent years. With the rise of social media, there has been a noticeable advent of social media bias mainly on these Web sites. To measure bias in social media, the betweenness and closeness centrality measure are calculated. More the betweenness centrality, more quickly the news spreads in the network is a general conclusion. In this paper, we assess the social media bias of Facebook and Twitter with the help of centrality measures and machine learning algorithms to find which one is more politically bias and to predict which algorithm can be best used to classify news as biased or unbiased. © 2021, Springer Nature Singapore Pte Ltd.

World University root node

Department of Software Systems

School of Computer Science and Engineering

Vellore Campus

Lecture Notes in Electrical Engineering

Social Media Bias Using Centrality Measures and Machine Learning

Aim: To develop a prediction model grounded on Machine Learning using Support Vector Machine (SVM). Background: Prediction of workload in a Cloud Environment is one of the primary task in provisioning resources. Forecasting the requirements of future workload lies in the competency of predicting technique which could maximize the usage of resources in a cloud computing environment. Objective: To reduce the training time of SVM model. Methods: K-Means clustering is applied on the training dataset to form ‘n’ clusters firstly. Then, for every tuple in the cluster, the tuple’s class label is compared with the tuple’s cluster label. If the two labels are identical then the tuple is rightly classified and such a tuple would not contribute much during the SVM training process that formulates the separating hyperplane with lowest generaliza-tion error. Otherwise the tuple is added to the reduced training dataset. This selective addition of tu-ples to train SVM is carried for all clusters. The support vectors are a few among the samples in reduced training dataset that determines the optimal separating hyperplane. Results: On Google Cluster Trace dataset, the proposed model incurred a reduction in the training time, Root Mean Square Error and a marginal increase in the R2 Score than the traditional SVM. The model has also been tested on Los Alamos National Laboratory’s Mustang and Trinity cluster traces. Conclusion: The Cloudsim’s CPU utilization (VM and Cloudlet utilization) was measured and it was found to increase upon running the same set of tasks through our proposed model. © 2020 Bentham Science Publishers.

Department of Information Security

Recent Advances in Computer Science and Communications

A prediction based cloud resource provisioning using svm

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.

Institute for Industry and International Programme

Fulltext

International Journal of Communication Networks and Distributed Systems

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

VIT Unitversity

TENCON 2008 - 2008 IEEE Region 10 Conference

A SIP based VoIP application with an enhancement aiding hearing impaired

2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing

COS: A Frame Work for Clustered off-line Search