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 Information Security

Cloud is rising as a service paradigm for expeditiously death penalty large vary distributed workflows with Brobdingnagian multiplication and data transmit wants. The heterogeneousness in cloud resources makes it exigent for planning work flow errands expeditiously. The aim is to seek out associate optimized mapping connecting individual work flow jobs and cloud resources, supported a range of feature of services. The dependencies in workflows create it troublesome for planning job with preference constraints. The roles need to be planned in such method that it reduces the makespan and finishing price for work flow. This paper proposes a work flow planning formula supported cuckoo hunt for finding a schedule for work flow jobs. the roles area unit side to execution list once prioritizing them consistent with the length of finishing and dependencies. Cuckoo search formula is changed to seek out a best mapping of work flow jobs on cloud resources. The work flow is dead inside a user outline aim whereas optimizing the makespan. The outcomes area unit evaluated with the meta-heuristic population based mostly Particle Swarm improvement and heuristic technique mixed Earliest end Time. The outcomes of the projected formula indicate that it outperforms the move toward thought-about for analysis. Copyright © 2018 American Scientific Publishers.

World University root node

School of Computer Science and Engineering

Vellore Campus

Journal of Computational and Theoretical Nanoscience

Implementing cuckoo search in heterogeneous cloud workflows

Department of Thermal and Energy Engineering

School of Mechanical Engineering

Cognitive Computing for Big Data Systems Over IoT Lecture Notes on Data Engineering and Communications Technologies

Recent Trends in Deep Learning with Applications

The purpose of this chapter is to observe the 3D asset development and product development process for creating real-world solutions using augmented and virtual reality technologies. To do this, the authors create simulative software solutions that can be used in assisting corporations with training activities. The method involves using augmented reality (AR) and virtual reality (VR) training tools to cut costs. By applying AR and VR technologies for training purposes, a cost reduction can be observed. The application of AR and VR technologies can help in using smartphones, high performance computers, head mounted displays (HMDs), and other such technologies to provide solutions via simulative environments. By implementing a good UX (user experience), the solutions can be seen to cause improvements in training, reduce on-site training risks and cut costs rapidly. By creating 3D simulations driven by engine mechanics, the applications for AR and VR technologies are vast ranging from purely computer science oriented applications such as data and process simulations to mechanical equipment and environmental simulations. This can help users further familiarize with potential scenarios.

VIT Unitversity

Big Data Analytics for Satellite Image Processing and Remote Sensing Advances in Computer and Electrical Engineering

Research Analysis of Development Pipelines in Augmented and Virtual Reality Technologies

Cloud computing is an evolving computing technology that provides many services such as software and storage. With the introduction of cloud storage, the security of outsourced data has become a major issue in cloud computing. Data storage in cloud computing environment needs to be secured in order to provide a safe and foolproof security for data outsourcing of the cloud service users. This paper presents a model for security of data in public cloud storage environment which successfully detects the unauthenticated access or any anomaly in the data. The proposed authentication model along with the data security model presented in this paper shows that this model is the best model suitable for securing the data in cloud computing environment. © 2018 SCPE.

Scalable Computing

Enhanced data security for public cloud environment with secured hybrid encryption authentication mechanisms

Wireless Sensor Networks (WSNs) are large-scale and high-density networks that typically have coverage area overlap. In addition, a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area, which leads to coverage holes in WSNs. Thus, coverage control plays an important role in WSNs. To alleviate unnecessary energy wastage and improve network performance, we consider both energy efficiency and coverage rate for WSNs. In this paper, we present a novel coverage control algorithm based on Particle Swarm Optimization (PSO). Firstly, the sensor nodes are randomly deployed in a target area and remain static after deployment. Then, the whole network is partitioned into grids, and we calculate each grid's coverage rate and energy consumption. Finally, each sensor nodes' sensing radius is adjusted according to the coverage rate and energy consumption of each grid. Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption. © 2018 Tech Science Press.

Computers, Materials and Continua

A PSO based energy efficient coverage control algorithm for wireless sensor networks

Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications covers timely topics, including the neural network (NN), particle swarm optimization (PSO), evolutionary algorithm (GA), fuzzy sets (FS) and rough sets (RS), etc. Furthermore, the book highlights recent research on representative techniques to elaborate how a data-centric system formed a powerful platform for the processing of cloud hosted multimedia big data and how it could be analyzed, processed and characterized by CI. The book also provides a view on how techniques in CI can offer solutions in modeling, relationship pattern recognition, clustering and other problems in bioengineering. It is written for domain experts and developers who want to understand and explore the application of computational intelligence aspects (opportunities and challenges) for design and development of a data-centric system in the context of multimedia cloud, big data era and its related applications, such as smarter healthcare, homeland security, traffic control trading analysis and telecom, etc. Researchers and PhD students exploring the significance of data centric systems in the next paradigm of computing will find this book extremely useful. © 2018 Elsevier Inc. All rights reserved.

Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications

Computational intelligence for multimedia big data on the cloud with engineering applications

Emerging research concerns about the authenticated cloud service with high performance of security and assuring trust for distributed clients in a smart city. Cloud services are deployed by the third-party or web-based service providers. Thus, security and trust would be considered for every layer of cloud architecture. The principle objective of cloud service providers is to deliver better services with assurance of trust about clients' information. Cloud's users recurrently face different security challenges about the use of sharable resources. It is really difficult for Cloud Service Provider for adapting varieties of security policies to sustain their enterprises' goodwill. To make an optimistic decision that would be better suitable to provide a trusted cloud service for users' in smart city. Statistical method known as Multivariate Normal Distribution is used to select different attributes of different security entities for developing the proposed model. Finally, fuzzy multi objective decision making and Bio-Inspired Bat algorithm are applied to achieve the objective.

Cyber Security and Threats

Configuring a Trusted Cloud Service Model for Smart City Exploration Using Hybrid Intelligence

Spatial data is freely available as part of production and geographic information. Spatial Data Mining (SDM) is a way to mine new information from large spatial data sets. Large volumes of spatial data come from various domains, such as agriculture, medical, and educational sources. The SDM technique is designed to work with organized relationships to discover patterns from large set of data. Data which is correlated to business or market is called big data. It is a term for mixture of information when its volume, variability and velocity create challenges in technologies or tools like relational databases. So there is a demand to analyze the data with the help of parallel processing using Hadoop cloud framework. This paper defines the spatial Hadoop cloud system with efficient indexing and spatial operations with privacy preserving scheme for cloud storage. The core system contains traditional spatial indexing scheme with enhanced scheme that includes R tree, Hilbert R tree, and enhanced Bloom filter tree. We improve the query search using Spatial Join, Range query, KNN and Max KNN queries. With the ubiquity of outsourced data and services to encrypted cloud and using Multi-authority attribute based encryption, we show that our. proposed system is more efficient in terms of data retrieval, data encryption and search in spatial cloud. Copyright © 2018 American Scientific Publishers All rights reserved.

Privacy Preserving in Geospatial Cloud Framework with Enhanced Indexing and Query Processing

The human intelligence modeling by brain components simulation, such as neurons and their connections, is part of leading smart decision computing paradigms. In Health, artificial neural networks (ANN) have the capacity to adapt to uncertainty situations and learn even with inaccurate data. This paper presents the modeling and performance evaluation of an ANN-based technique, named multilayer perceptron (MLP), for gestational diabetes mellitus (GDM) prediction that is responsible for several severe complications and affects 3 to 7% of pregnancies worldwide. Results show that this approach reached a precision of 0.74, Recall 0.741, F-measure 0.741, and ROC area 0.779. These indicators show that this method is an excellent predictor of this disease. This contribution offers a computational intelligence (CI) tool capable of identifying risk cases during pregnancy and, thus, reduce possible sequels for both pregnant woman and fetus. © 2018, Springer Nature Singapore Pte Ltd.

Lecture Notes in Electrical Engineering Frontier Computing

Multilayer Perceptron Application for Diabetes Mellitus Prediction in Pregnancy Care

With the advent of the big data era, the amount of data computation is getting larger and larger, and the computational load of clients is also increasing day by day. The advent of clouds allows clients to outsource their data to the cloud for computing services. Outsourced computation has greatly reduced the computational burden of clients, but also brings the issue of trust. Because the cloud is not trustworthy, clients need to verify the correctness of the remote computation results. In this paper, we mainly study the common norm operations, and propose two publicly verifiable schemes for 1-norm and 2-norm operations respectively, any client can publicly verify these two common norm operations under single-key setting by using our schemes. © Springer International Publishing AG 2018.