Jan - April 2014
Recent Trends in
Programming Languages (RTPL) conducted
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Recent Trends in Programming Languages Focus and Scope Covers † High - Level programming languages † Data Representation, Data Abstraction, Procedure activation † Programming Language C † IF- - THEN - ELSE, SWITCH, WHILE, FOR, DO † Parameter passing, Pointers, Arrays, Structures, C-Library †
Program transformation techniques
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STM JOURNALS
I take the privilege to present the print version for the [Volume 1 Issue (1)] of Recent Trends in Programming Languages. The intension of RTPL is to create an atmosphere that stimulates creativeness, research and growth in the area of Programming Languages. The development and growth of the mankind is the consequence of brilliant Research done by eminent Scientists and Engineers in every field. RTPL provides an outlet for Research findings and reviews in areas of Programming Languages found to be relevant for National and International recent developments & research initiative. The aim and scope of the Journal is to provide an academic medium and an important reference for the advancement and dissemination of Research results that support high level learning, teaching and research in the domain of Programming Languages. Finally, I express my sincere gratitude and thanks to our Editorial/ Reviewer board and Authors for their continued support and invaluable contributions and suggestions in the form of authoring write ups/ reviewing and providing constructive comments for the advancement of the journals. With regards to their due continuous support and co-operation, we have been able to publish quality Research/Reviews findings for our customers base. I hope you will enjoy reading this issue and we welcome your feedback on any aspect of the Journal.
Dr. Archana Mehrotra Director STM Journals
Recent Trends in Programming Languages
Contents
1. TextSum: An Automatic Text Summarization Tool using Bayesian Classification G. Rohini, P. Krishna Subba Rao, Ch. Avinash
1
2. Multiple Sequence Alignment Algorithms for a Protein Structure for a Biological Data using Dynamic Programming Method P. V. S. Lakshmi Jagadamba
4
Recent Trends in Programming Languages Volume 1, Issue 1 www.stmjournals.com
TextSum: An Automatic Text Summarization Tool using Bayesian Classification G. Rohini, P. Krishna Subba Rao*, Ch. Avinash Department of Computer Science and Engineering, Gayatri Vidya Parishad College of Engineering Visakhapatnam, India Abstract Automatic text summarization is the process of summarizing text from one or more documents in order to help the reader to determine if [s]he has to read the document(s) in full. Although automatic summarization is around for many years, there is a renewed interest in this area from the government, industry, and academia. The reason is the availability of large quantities of information due to world wide web and the necessity to summarize such information. Automatic summarization involves reducing text document(s) into shorter form which conveys the same meaning as the original documents. Summarization may be done by extractive methods or abstractive methods. This paper consists of an Automatic Text Summarization Tool which takes into account several features, including sentence position, positive and negative key words, sentence centrality etc., and employs the Bayesian classification algorithm to identify the sentences that are to be included in the summary.
Keywords: Automatic text summarization, bayesian classification
RTPL (2014) Š STM Journals 2014. All Rights Reserved
Recent Trends in Programming Languages Volume 1, Issue 1 www.stmjournals.com
Multiple Sequence Alignment Algorithms for a Protein Structure for a Biological Data using Dynamic Programming Method P. V. S. Lakshmi Jagadamba* Department of Computer Science Engineering School of Engineering, G.V.P. College for Degree and P.G Courses, Technical Campus Abstract Many algorithms use dynamic programming to align two sequences. In this paper a new algorithm is proposed to perform multiple Sequence alignment. This algorithm is developed by taking the inspiration of dynamic programming and is implemented on protein structures of any length. It uses particle swarm optimization (PSO) techniques to get the best alignment. The proposed approach tries to improve the sequence alignment obtained by Needleman Wunsch algorithm.
Keywords: Dynamic Programming, Pair wise Alignment, Particle Swarm Optimization, Machine Learning.
RTPL (2014) Š STM Journals 2014. All Rights Reserved