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 Software Systems

VIT Unitversity

School of Computer Science and Engineering

Vellore Campus

2016 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC)

3D face view generation from human drawn sketch: A review

World University root node

2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

A semiautomated approach using GUI for the detection of red blood cells

ExSep: An exon separation process using Neural Skyline Filter

Department of Information Technology

School of Information Technology and Engineering

A real time system for denial of service attack detection based on multivariate correlation analysis approach

Optik

Effect of Dy3+ ions concentration on optical properties of lead borosilicate glasses for white light emission

Dynamic performance analysis of AGC of multi-area power system considering Proportional-Integral-Derivative controller with different cost functions

Requirement engineering is thought to be one of the crucial and meanwhile troublesome assignments among analysis and design process. The methodology of requirement engineering habitually starts beginning with no outside help and depends upon all that much the analysts' mastery. In this way, there is a need of solution environment to specialists for recurring problems in the domain. In this regard, requirements patterns help reusing the information of catching obliged functionalities and properties of a framework. To enhance context aware system, we introduce new regularly utilized requirements patterns with context properties. The Health Level 7 (HL7) standards and driving edge information technology will be the starting ventures for moving towards an interoperable social healthcare environment. Our aim is to create a new pattern template for context aware system, namely, CaRePa (pronounced as Care-Pa) and using this template, we have identified seven patterns which are used in CaRePa methodology. The strategy that CaRePa technique incorporates constant evaluation of identified requirement patterns. This methodology evaluated with patient monitoring system as a case study. Finally, the assessment of proposed ideas by the method for directing survey and the results of the different stakeholders are presented.

Department of Information Security

Far East Journal of Electronics and Communications

CONTEXT AWARE REQUIREMENT PATTERNS (CaRePa) METHODOLOGY AND ITS EVALUATION

Spam emails have become an increasing difficulty for the entire web-users.These unsolicited messages waste the resources of network unnecessarily. Customarily, machine learning techniques are adopted for filtering email spam. This article examines the capabilities of the extreme learning machine (ELM) and support vector machine (SVM) for the classification of spam emails with the class level (d). The ELM method is an efficient model based on single layer feed-forward neural network, which can choose weights from hidden layers,randomly. Support vector machine is a strong statistical learning theory used frequently for classification. The performance of ELM has been compared with SVM. The comparative study examines accuracy, precision, recall, false positive, true positive.Moreover, a sensitivity analysis has been performed by ELM and SVM for spam email classification.

International Journal of Engineering Research in Africa

Classifying Spam Emails Using Artificial Intelligent Techniques

The current and future computer industry is changing from single core CPU to Multicore Processors. The next generation computing systems are focusing on parallel computing to solve the problem in fast and high speed using parallel programming concepts. By running more than one task at the same time with multiple processors concurrently or parallel we can achieve high speed in our computing applications. Here the performance improvement is measured in terms of increase in the number of cores per machine and is analyzed for better optimal work load balance. The parallel computers follow different workload scheduler. In this paper we investigate how to tune the performance of threaded applications with balanced load for each core or processor. The focus here is the process of comparative analysis of single core and multicore systems to run an application program for faster execution time and optimize the scheduler for better performance

International Journal of Grid and Distributed Computing

Performance Evaluation and Analysis of Parallel Computers Workload

Background/Objectives: In Software Engineering discipline, requirement phase plays vital role in success of a project. To make the requirement analysis process more effective, developers adopt several methods like goal-oriented, aspectoriented, pattern-oriented etc. In the current scenario recurring requirements can be addressed effectively with requirement patterns rather applying a common procedure to all projects. Methods/Statistical Analysis: While adopting common procedure to all project results in increasing cost of quality or even project failure due to the fact that every project is not having similar characteristics. To perform requirement process effectively, it is necessary to select a suitable requirement pattern for a project. Selecting relevant, applicable and reliable pattern is challengeable and prior knowledge is required. Findings: The proposed framework groups project characteristics and the pattern sets into k-clusters based on the clustering algorithm. The best suitable pattern will be filtered from the available pattern clusters by applying the filtering algorithm. The suggested patterns along with the situational constraints for a given situation will be stored in the data store. The stack holder can add new pattern details or can update the details of existing patterns Applications/ Improvements: Finds the analysis patterns that match with the given situational characteristics and requirement of a project. Clustering enhanced with fuzzy to bring the improvement.

Indian Journal of Science and Technology

Requirement Pattern Extraction using Cluster Based Framework - RAPID