iosr-JCE   Volume-18 ~ Issue-3 ~ May-June 2016

Abstract: As the social media has gained more attention from users on the Internet, social media has been one of the most important information sources in the world. And, with the increasing popularity of social media, data which is posted on social media sites are rapidly becoming popular, which is a term used to refer to new media that is replacing traditional media. In this paper, we concentrate on geotagged tweets on the Twitter site. These geotagged tweets are known to as georeferenced documents......

Keyword: Spatiotemporal clustering, Density-based clustering, Social media, Naive Bayes, Burst detection.

[1]. Kaneko T, Yanai K (2013) Visual event mining from geo-tweet photos. In: Multimedia and Expo Workshops (ICMEW) 2013 IEEE International Conference On. IEEE, San Jose, CA, USA. pp 1–6.
[2]. Tamura K, Kitakami H (2013) Detecting location-based enumerating bursts in georeferenced micro-posts. In: roceedings of the 2013 Second IIAI International Conference on Advanced Applied Informatics. IIAI-AAI '13. IEEE Computer Society, Los Alamitos, CA, USA. pp 389–394.
[3]. Tamura K, Ichimura T (2013) Density-based spatiotemporal clustering algorithm for extracting bursty areas from georeferenced documents. In: Proceedings of The 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013. IEEE Computer Society, Los Alamitos, CA, USA. pp 2079–2084.
[4]. Abdelhaq H, Sengstock C, Gertz M (2013) Eventweet: Online localized event detection from twitter. Proc VLDB Endow 6(12):1326–1329.

Abstract: In this modern era, as information technology is growing so far from the computing to communication, home automation is becoming a crucial area in research. In this proposed work, focus has been given on the design and development of an integrated platform for providing the facility to control the home appliances not only locally, but remotely also in an efficient way........

[1] Hirakjyoti Sarma, Dr. Manoj Kumar Deka," Design of an Integrated Platform for GSM and Web Based Home Automation System ","2nd International Conference on Emerging Trends in Computer Science, Communication and Information Technology" (CSCIT2015) February 9-11, 2015, organized by Department of Computer Science and Information Technology, Yeshwant Mahavidyalaya, Nanded-431 602. (M.S.) India"
[2] Armando Roy Delgado, Rich Picking and Vic Grout, "Remote-Controlled Home Automation Systems with Different Network Technologies", Centre for Applied Internet Research (CAIR), University of Wales, NEWI, Wrexham, UK
[3] Inderpreet Kaur , "Microcontroller Based Home Automation System With Security", (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 1, No. 6, December 2011
[4] Ali Ziya Alkar, Umit Buhur, "An Internet Based Wireless Home Automation System for Multifunctional Devices.

Abstract: Image Properties are used for the analysis of quality of the given image. The Intensity images, Contrast images, Weibull images and Fractal images are extracted from the input images. Eight basic image properties are calculated for these images. Analysis is made for the different Properties. Clustering is made on different types of images based on discriminative properties. Images are classified by using K-means method. Then analysis is made on the different clusters.

Keywords: Clustering, Image Property, K-means method, Normalization.

[1] Robert M Haralick, K Shanmugam and Its'hak Dinstein, Textural Features for Image Classification, IEEE Transactions on Systems, man and Cybernetics, 3, 1973, 610-621.
[2] Anne H. Schistad Solberg and Anil K. Jain, Texture Fusion and Feature Selection Applied to SAR Imagery, IEEE Transactions on GeoScience and Remote Sensing, 35, 1997, 475-479.
[3] Valery V Starovoitov, Sang-Yong Jeong and Rae-Hong Park, Texture Periodicity Detection: Features, Properties, and Comparisons, IEEE Transactions on Systems, man and Cybernetics, 28, 1998, 839-849.
[4] Jianguo Zhang and Tieniu Tan, Brief review of invariant texture analysis methods, International Journal Pattern Recognition, 35, 2002, 735–747.
[5] Cheung Ming Lai, Kin Man Lam and Wan-Chi Siu, A Fast Fractal Image Coding Based on Kick-Out and Zero Contrast Conditions, IEEE Transactions on Image Processing, 12, 2003, 1398-1403.

Abstract: Solanum lycopersicum economically important crop world wide, intensively investigated and model system for genetic studies in plant ,variability is a measure spread of data set. Genome analysis and annotation using genome from the libraries, automatic annotation using the blast (basic local alignment search tools )low complexity sequence have unusual composition that can create a problem in sequence similarity searching the color bars in the graphic summarize the BLAST tools. Blast have been developed to provided the sequence in the form of customized data extraction utilities for some of customized data extraction utilities for some of commonly used database such as NCBI,FASTA ,BLAST,ORF ,NEB Cutter........

Keyword: Bioinformatics ---Biological database--- Customized data retrieved—Sequence analysis—Data compiled---Expectation Value.

[1]. Baxevanis D.Andreas,Quellete Fracis B.F. ,A Practical guide to the Analysis of gene and Proteins.,3rd Eddition October 2004,Published by Wiley, john and Sons

[2]. Brudno M, Malde2.S, Poliakov A, Do CB, Couronne O, Dubchak I, Batzoglou S (2003)."Glocal alignment: finding rearrangements during alignment". Bioinformatics. 19. Suppl 1 (90001): i54–62. doi:10.1093/bioinformatics/btg1005. PMID 12855437.

[3]. Casey, R. M. (2005). "BLAST Sequences Aid in Genomics and Proteomics". Business Intelligence Network.

[4]. Eddy SR; Rost, Burkhard (2008). Rost, Burkhard, ed. "A probabilistic model of local sequence alignment that simplifies statistical significance estimation". PLoS Comput Biol4 (5): e1000069. doi:10.1371/journal.pcbi.1000069. PMC 2396288.PMID 18516236

[5]. Higgins Des,Taylor Willie 2000,Bioinformatics:Sequence structure and database practical approach‖ 1st Eddition october 2000,Published by oxford University Press.

Abstract: The Intelligent text extraction system automatically identifies and extracts the text present in different types of images. The growth of digital world Detection and Extraction of text regions in an image are well known problems in the area of image processing. The growth of digital world and the usage of multimedia generated a new era with a classic problem of pattern recognition.......

Keyword: DWT, image processing, morphological operations, Otsu, segmentation, text extraction, text recognition

[1] Xin Zhang, Fuchun Sun, Lei Gu "A Combined Al-gorithm for Video Text Extraction" Seventh Internation-al Conference on Fuzzy Systems and Knowledge Discov-ery,2010.
[2] Ohya, A. Shio, and S. Akamatsu, "Recognizing Characters in Scene Images", IEEE Transactions on Pat-tern Analysis and Machine Intelligence, 1994, 214-224.
[3] Gllavata, R. Ewerth, and B. Freisleben, "A robust algorithm for text detection in images" Proceedings of the 3rd International Symposium on Image and Signal Pro-cessing and Analysis, pp.611– 616, ISPA, 2003.
[4] Zhong, Yu.,Karu, K., and Jain, A.K." Locating text in complex color images" Proceedings of the Third Inter-national Conference on Document Analysis and Recogni-tion1995, 1, 14-16: 146-149.
[5] WonjunKim,Changick Kim, "A New Approach for Overlay Text Detection and Extraction From Complex Video Scene," IEEE Transactions on Image Processing, V.18 , No.2, pp. 401 – 411, 2009.

Abstract: Apache Hadoop is open source software that allows for the processing of large data set sin nodes. This work involves non-functional testing of Hadoop application in order to get the observable performance of an application. This work also includes the study and observations of performance analysis of Hadoop application for various machine architectures like Intel Core 2 Duo, and Intel i3.
Keywords: BigData, Split files, Fuunctional testing, Hadoop throughput and performance, Mappers, Reducers.

[1] Paul C. Zikopoulos, Chris Eaton, Dirk deRoos, George Lapis, Thomas Deutsch,Understanding Big Data McGraw-Hill Companies 2012.
[2] Lam,Chuck,Hadoop in Action, Manning Publications Co. 2010, Pages 21-25.
[3] Dryman,Carpediem, Hadoop Performance Tuning Best Practices, 241-242, ACM,2014.
[4] Zikopoulos, Paul and Eaton, Chris and others, Understanding big data: Analytics for enterprise class hadoop and streaming data, McGraw-Hill Osborne Media 2011.
[5] Vinaykumar Chandrashekar, VinyasShrinivasShetty, Testing in a Big DataWorld, Manning EuroSTAR 2013.

Abstract: We introduced a new method for Text-To-Speech (TTS) synthesis for Pali language. We discuss the efforts in collecting speech database of Pali language and relevant design issues in development of TTS system. This system is based on unit selection concatenative speech synthesis using phonemes, syllables and words as an elementary unit for Pali speech synthesis. The speech units picked by the selection algorithm optimally.......

Keyword: Text-To-Speech synthesis, Pali Speech Database, Unit selection, Speech Analysis

[1]. Archana Balyan, S.S. Agrwal and Amita Dev, Speech Synthesis: Review, International Journal of Engineering Research and Technology, ISSN 2278-0181 Vol. (2), 2013, 57 – 75.
[2]. Jonathan Williams, Prosody in Text-to-Speech Synthesis Using Fuzzy Logic, M.S. diss., West Virginia University, USA, 2007.
[3]. D.D. Pande, M. Praveen Kumar, A Smart Device for People with Disabilities using ARM7, International Journal of Engineering Research and Technology, ISSN 2278-0181 Vol.(3), 2014, 614 – 618.
[4]. Sami Lemmetty, Review of Speech Synthesis Technology, M.S. diss., Helsinki University of Technology, Finland, 1999.
[5]. Mark Tatham and Katherine Morton, Developments in Speech Synthesis (John Wiley & Sons, Ltd. ISBN: 0-470-85538-X, 2005)

Abstract: The term Data Warehouse represents huge collection of historical data which are subject-oriented, non-volatile, integrated, and time-variant and such data is required for the business needs [1]. Data warehouses and on-line analytical processing (OLAP) tools have become essential elements of decision support systems. Traditionally, data warehouses are refreshed periodically (for example, nightly) by extracting, transforming, cleaning and consolidating data from several operational data sources........

Keyword: Data Warehouse, Business Intelligence(BI), OLAP, OLTP, ETL

[1] Arora, R.; Pahwa, P.; Bansal, S."Alliance Rules for Data Warehouse Cleansing".2009 International Conference on Signal Processing Systems15-17 May 2009. Pages: 743– 747.

[2] Prasad, K.H.; Faruquie, T.A.; Joshi, S.; Chaturvedi, S.; Subramaniam, L.V.; Mohania, M."Data Cleansing Techniques for Large Enterprise Datasets". SRII Global Conference (SRII), 2011 Annual March 29 2011-April 2 2011.Page 135-144.

[3] Savitri, F.N.; Laksmiwati, H." Study of localized data cleansing process for ETL performance improvement in independent datamart". Electrical Engineering and Informatics (ICEEI), 2011 International Conference on 17-19 July 2011. Pages: 1 – 6

[4] Xingquan Zhu; Peng Zhang; Xindong Wu; Dan He; Zhang, C.; Yong Shi." Cleansing Noisy Data Streams". Data Mining, 2008. ICDM '08. Eighth IEEE International Conference on 15-19 Dec. 2008. Pages: 1139 - 1144

[5] Das, A.C.; Mohanty, S.N.; Pani, S.K.;"A Comparative Study on Data analytics and Big Data Analytics", IJCSITR, VOL 4, Issue 1, 2016, Pages: 67-75.

Abstract: The information system security is nowadays paramount, it is for what we are focused in our research to talk about a basic of security which is cryptography, and specially about RSA algorithm, a system of coding with public key developed by Rivest, Shamir and Adleman (R.S.A in 1978).We will discuss tree points in our project, the first step is to understand operation of RSA; (preparation of the key,.......

Keyword: Cryptography; Genetic algorithm (GA); Rivest Shamir and Adleman (RSA), algorithm of KARATSUBA

[1] Alin Bostan, Algorithmes rapides pour les polynomes, series formelles et matrices, Vol. 1, n° 2 (2010), p. 75-262.
[2] Abderrahmane Nitaj, Laboratoire de Math_ematiques Nicolas Oresme, Universit_e de Caen, France, Version du 28 juin 2009.
[3] RSA et les grands nombres, IN 261 ENSTA, http://www.di.ens.fr/~pointche/enseignement/ensta2/.
[4] Algorithmus der Woche, publiée à l'occasion de l'Année de l'informatique (Informatikjahr) 2006.
[5] Techniques de cryptanalyse de RSA, Christophe Grenier, 28 janvier 2009.

Abstract: The World Wide Web is growing at an astonishing rate. This has resulted in enormous increase in online communication. The online communication data consist of feedback, comments and reviews that are posted on internet by internet users. To analyze such opinionated data sentiment analysis is required. Sentiment analysis is a natural language processing technique which classifies the data into positive, negative and neutral........

Keyword: Fuzzy Logic, Lexicon Based Technique, Machine learning, Natural Language Processing, Sentiment analysis.

[1] Salinca A., Business Reviews Classification Using Sentiment Analysis, 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) IEEE, Sep 201, pp. 247-250.
[2] Ghag, Kranti Vithal, and Ketan Shah., Comparative analysis of effect of stop words removal on sentiment classification, Proc. IEEE Conf. In Computer, Communication and Control (IC4), 2015, pp. 1-6.
[3] Indhuja, K. and Raj PC Reghu., Fuzzy logic based sentiment analysis of product review documents, In Proc. Computational Systems and Communications (ICCSC), 2014 First International Conference on IEEE, 2014, pp. 18-22.
[4] Kumar, Vipin, and Sonajharia Minz, Mood classification of lyrics using SentiWordNet, Proc. IEEE Conf. in Computer Communication and Informatics (ICCCI), 2013, pp. 1-5.

Abstract: With the advent of internet digital media has been improving with massive advancement in recent years. The distribution of unauthorized copies of media content has also been increasing day by day. Because of easy access to digital content, online purchasing and distribution becomes easier. Thus there is an urgent need to provide protection for digital content........

Keyword: Digital watermarking, Hadamard Transform, Breadth-first search technique

[1] Anthony T.S. Ho, J. Shen, Andrew K.K. Chow, J. Woon, Robust Digital Image-in-Image Watermarking Algorithm Using the Fast Hadamard Transform, Proc. of IEEE International Symposium on Circuit and system (ISCAS '03), vol. 3, pp. 826-829, 2003.
[2] S. Saryazdi, H. Nezamabadi-pour, A Blind Digital Watermark in Hadamard Domain, Proc. of World Academy of Science, Engineering and Technology, vol. 3, 2005.
[3] K. Deb, M.S. Al-Seraj, M.M. Hoque, M.I.H. Sarker, Combined DWT DCT Based Digital Image Watermarking Technique for Copyright Protection, Proc. of IEEE International Conference on Electrical & Computer Engineering (ICECE), 2012.
[4] A. Al-Haj, Combined DWT-DCT Digital Image Watermarking,Journal of Computer Science, vol. 3, no. 9, pp. 740-746, 2007.
[5] F. Husain ,A Survey of Digital Watermarking Techniques for Multimedia Data,MIT International Journal of Electronics and Communication Engineering, vol. 2, no. 1, pp. 37-43, 2012

Abstract: Searching in computer science is the method which finds the required item in the given data. Linear Search is the technique for extracting a particular item in the list that checks each part in series until the preferred element is found or the list is exhausted. The sequence needs not to be in some order. The complexity for the linear search is θ (n).In this paper, the algorithm is discussed takes less time with respect to linear search. Time taken by the DN Search algorithm is equal to time taken by Binary Search Algorithm, but this algorithm also works on unsorted data.

[1] Thomas H Cormen, Charles H Leiserson, Ronald L Rivest, Clifford Stein, "Introduction to Algorithm", 3rd Edition, Prentice Hall of India Pvt Ltd., Chapter 1 and Chapter 3.
[2] Ellis Horowitz, Suraj Sahni, Sanguthevar Rajasekaran, "Fundament of Computer Algorithms", Galgotia Publication, Chapter 1.3.
[3] Seymour Lipshutz, Schaum‟s Outline "Data Structures", Tata McGraw Hill, Chapter 4.7 and Chapter 4.8.
[4] Seymour Lipshutz, Marc Lars Lipson, Varsha H Patil, Schaum‟s Outline "Discrete Structure", Third Edition, Tata McGraw Hill, Chapter 3.9

Abstract: We are introducing the discrete wavelet transform based joint methodology with the existing Adaptive Fractal Image Compression technique. By developing this method we will get the better quality of the image w.r.t peak signal to noise ratio and improvement in the compression ratio. As we have previous FIC methods in usage we are going to compare our results with them sequentially by applying image metrics to show quality of the decompressed image after reconstruction........

Keyword: Fractal, range block, Discrete Wavelet Transform, image compression, Peak Signal to noise ratio and compression ratio

[1] R. L. White, "High-performance compression of astronomical images," presented at the NASA conference publication, 1993.
[2] Y. Fisher, "Fractal Image Compression: Theory and Application," ed New York: Springier Verlage, 1994.
[3] G. M. Bernstein, C. Bebek, J. Rhodes, S. Stoughton, R. A. Vanderveld, and P. Yeh, "Noise and Bias In Square-Root Compression Schemes," TheUniversity of Chicago Press on behalf of the Astronomical Society of the Pacific, vol. 122 (889), pp. 336-346, 2010.
[4] T. T. Barbara Penna, Enrico MagliE, and Gabriella Olmo, "Transform Coding Techniques for LossyHyperspectral Data Compression," IEEETRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, vol. 45, pp.1408-1421, 2007.
[5] G.M.Padmaja, P.Nirupama, and G.M.Padmaja, "Analysis of Various Image Compression Techniques," ARPN Journal of Science and Technology, vol. 2 (4), pp. 371-376, 2012.

Abstract: Changes are common for software process today and hence research is required for change-oriented software engineering. Increase in maintenance costs have become a major concern for developers and users of software systems. Changeability is an important aspect of maintainability, especially in environments where software changes are frequently required........

Keyword: Change oriented software engineering, software quality, maintenance, change impact, design and software metrics.

[1] Peter Ebraert, Jorge Vallejos, Pascal Costanza, Ellen Van Paesschen, Theo D'Hondt,, "Change-Oriented Software Engineering",
ACM International Conference Proceeding Series; Vol. 286, Pages 3-24, 2007.
[2] T. M. Pigoski. Practical Software Maintenance. John Wiley & Sons, New York, 384 pages, 1997.
[3] Romain Robbes and Michele Lanza, "Change-based Approach to Software Evolution", Electronic Notes in Theoretical Computer
Science (ENTCS) archive, Vol. 166 , Pages 93-109, 2007.
[4] Dave Thomas, "Agile Programming: Design to Accommodate Change", IEEE, Vol. 22, No. 3, May/June 2005.
[5] Laurie Williams, "A Survey of Agile Development Methodologies", pp 209-227, 2007.

Abstract: With the enhancement in the banking sector lots of people are applying for bank loans but the bank has its limited assets which it has to grant to limited people only, so finding out to whom the loan can be granted which will be a safer option for the bank is a typical process. So in this paper we try to reduce this risk factor behind selecting the safe person so as to save lots of bank efforts and assets. This is done by mining the Big Data of the previous records of the people to whom the loan was granted before and on the basis of these records/experiences the machine was trained using the machine learning model which give the most accurate result........

Keyword: Loan, Machine Learning, Training, Testing, Prediction.

[1]. Rattle data mining tool: available from http://rattle.togaware.com/rattle-download.html
[2]. Aafer Y, Du W &Yin H 2013, DroidAPIMiner: 'Mining API-Level Features for Robust Malware Detection in Android', in:
Security and privacy in Communication Networks Springer, pp 86-103 .

[3]. Ekta Gandotra, Divya Bansal, Sanjeev Sofat 2014, 'Malware Analysis and Classification: A Survey'available from http:// www.scirp.org/journal/jis
[4]. K. Hanumantha Rao, G. Srinivas, A. Damodhar, M. Vikas Krishna: Implementation of Anomaly Detection Technique Using Machine Learning Algorithms: Internatinal Journal of Computer Science and Telecommunications (Volume2, Issue3, June 2011).
[5]. J. R. Quinlan. Induction of Decision Tree. Machine Learning, Vol. 1, No. 1. pp. 81-106., 1086.

Abstract: In social media, users have the privilege of connecting with people and extensively communicate, share information, discuss topics of recent trends. Friendster, LinkedIn, Instagram, Twitter are some media through which users can perform the activities as mentioned earlier. Twitter is a well-known microblog which allows user to express their opinion or sentiment in the form of tweets within maximum length of 140 characters. The sentiment of the user can be analyzed and interpreted using the concept called Sentiment Analysis (SA).Twitter is widely used in almost all parts of the world thus ensures the presence of multilingual tweets in expressing their sentiments.........

Keyword: Bilingual, Path Length Similarity, Sentiment Analysis, Support Vector Machine, Word Sense Disambiguation.

[1] M.Albanese,A.Chianese,A.D"Acierno, V. Moscato, & A.Picariello, A multimedia recommender integrating object features and user behavior Multimedia Tools and Applications, 4(Springer,2010)563–585.
[2] Akshay Java, Xiaodan Song, Tim Finin, Belle Tseng , Why We Twitter: Understanding Microblogging Usage and Communities.
[3] Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis,,2007,56-65.
[4] Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon, What is Twitter, a Social Network or a News Media?,Proceedings of the 19th International Conference on World Wide Web,2007,591-600.
[5] Manju Venugopalan, Deepa Gupta,Exploring sentiment analysis on twitter data,Proceedings of Eighth International Conference on Contemporary Computing,2015,241-243.