iosr-JCE   Volume-17 ~ Issue-4 ~ July-August 2015

Abstract: Speech is a medium through which emotions are expressed by human being. In this paper, a mixture of MFCC and LPCC has been proposed for audio feature extraction. One of the greatest advantage of MFCC is that it is capable of identifying features even in the existence of noise and henceforth it is combined with the advantage of LPCC which helps in extracting features in low acoustics. Two databases have been considered namely Berlin Emotional Database and a SAVEE database.

[1]. Igor Bisio, Alessandro Delfino, Fabio Lavagetto, Mario Marchese, And Andrea Sciarrone, "Gender-Driven Emotion Recognition Through Speech Signals For Ambient Intelligence Applications", Ieee Transactions On Emerging Topics In Computing, Digital Object Identifier 10.1109/Tetc.2013.2274797, 21 January 2014.
[2]. http://www.expressive-speech.net/, Berlin emotional Speech database
[3]. http://personal.ee.surrey.ac.uk/Personal/P.Jackson/SAVEE/, SAVEE Database
[4]. Nitin Thapliyal, Gargi Amoli, "Speech based Emotion Recognition with Gaussian Mixture Model‟, International Journal of Advanced Research in Computer Engineering & Technology, Volume 1, Issue 5, July 2012, ISSN: 2278 1323.
[5]. Inma Mohino-Herranz, Roberto Gil-Pita, Sagrario Alonso-Diaz and Manuel Rosa-Zurera, "MFCC Based Enlargement Of The Training Set For Emotion Recognition In Speech", Signal & Image Processing : An International Journal (SIPIJ) Vol.5, No.1, February 2014.

Abstract:In essence of fuzzy inference system (FIS) for classification, Genetic algorithm (GA) which is an optimal searching technique is used for generation rules in the proposed work. This paper develops an FIS with rules generated using GA, the GA is developed with rule importance as fitness criteria. The rule importance of each rule is calculated by its rule support in each rule class. Encoding of rules is using the fuzzy membership set no for antecedent and consequent. Also the stopping criteria is a combination ofgenerations specified and Minimum rules fired count. The Proposed system using GA approach for rule generation giving consistent results with optimal rules.

[1]. Johannes A. Roubos a, MagneSetnes b, Janos Abonyi c, "Learning fuzzy classification rules from labeled data", Information Sciences 150 , Elsevier Publication (2003) pg:77–93
[2]. HisaoIshibuchi, Ken Nozaki, Naohisa Yamamoto, Hideo Tanaka, " Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms", Fuzzy Sets and Systems 65 Elsevier Publication (1994) pg: 237-253
[3]. UjjwalMaulik, SanghamitraBandyopadhyay,"Genetic algorithm-based clustering technique", Pattern Recognition 33, A Journal of Pattern Recognition Society, Published by Elsevier Science (2000) 1455-1465
[4]. DinabandhuBhandari, C. A. Murthy, Sankar K. Pal, "Variance as a Stopping Criterion for Genetic Algorithms with Elitist Model",FundamentaInformaticae 120 (2012) 145–164
[5]. AnsafSalleb-Aouissi, ChristelVrain, Cyril Nortet, "QuantMiner: A Genetic Algorithm for Mining Quantitative Association Rules", Proceeding of International Joint Conference on Artificial Intelligence,pg 1035-1040, 2007.
[6]. SoumadipGhosh, SushantaBiswas, DebasreeSarkar, ParthaPratimSarkar, "Mining Frequent Itemsets Using Genetic Algorithm",International Journal of Artificial Intelligence & Applications (IJAIA), Vol.1, No.4,pg. 133-144, October 2010.

Abstract: Major online "Question and Answer" forums have proven to be of enormous help to programmers and developers from all parts of the world. One such important forum is the Stack Overflow community. In this paper, we explore and analyze the title of a question posted on the Stack Overflow community and check whether the title abides by the set of rules and guidelines defined by the Stack Overflow community [1] and [3]. We also carry out sentiment analysis on the title to judge the virality [2] quotient of the question. We present an application (or tool) developed using the Natural Language Toolkit (NLTK) and Py-stackexchange API (Application Programming Interface) of Stack Overflow in Python.

[1]. How do I write a good title? http://meta.stackexchange.com/questions/10647/writing-a-good-title

[2]. Berger, Jonah, and Katherine L. Milkman. "What makes online content viral? "Journal of marketing research 49.2 (2012):192-205.[pdf]Can we prevent titles with an unnecessary tag in them? http://meta.stackexchange.com/questions/103563/can-we-prevent-titles-with-an-unnecessary-tag-in-them

[3]. Squire, Megan, and Christian Funkhouser. "" A Bit of Code": How the Stack Overflow Community Creates Quality Postings." System Sciences (HICSS), 2014 47th Hawaii International Conference on. IEEE, 2014.[pdf]

Abstract: This paper investigates a framework of search-based face annotation (SBFA) by mining weakly labeled facial images that are freely available on the World Wide Web (WWW). One challenging problem for search-based face annotation scheme is how to effectively perform annotation by exploiting the list of most similar facial images and their weak labels that are often noisy and incomplete. To tackle this problem, we propose an effective unsupervised label refinement (ULR) approach for refining the labels of web facial images using machine learning techniques.

1]. Social Media Modeling and
[2]. Computing, S.C.H. Hoi, J. Luo, S. Boll, D. Xu, and R. Jin, eds. Springer, 2011.
[3]. D. Wang, S.C.H. Hoi, and Y. He,
[4]. "Mining Weakly Labeled Web Facial Images for Search-Based Face Annotation," Proc. 34th Int'l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), 2011.
[5]. P. Belhumeur, J. Hespanha, and D.

Abstract: One of the most widely discussedtechnologiesare theInternet and itsassociated environment theWorldWide Web.Web technologyhasa broad popular supportamong entrepreneursand technicians likewise. The web environment is owned and managed by the corporation. It may be outsourced,but in most cases, the Web is a normal part of computer operations, and is often used as a center for the integration of business systems. An interaction occur when the Web create a transaction to execute a client order, for example. The transaction isformatted and sent to the corporate systems, where it is processed as any other order. In this sense, the Web is not just another source of business transactions that is entered

[1]. Lawrence Steve; Lee Giles C., "Accessibility of information on the web"New York, NY, USA, ACM, Volume 11, Issue 1, (2000), pp: 32-39
[2]. Joachims T., "Optimizing search engines using click through data"Ithaca, NY USA, Cornell University, Department of Computer Science, (2002).
[3]. Yuan Wang, David J. Devitt, "Computing PageRank in a Distributed Internet Search System" Toronto, Canada, VLDB Endowment, (2004).
[4]. Inmon, William H. "An Architecture for Managing Click Stream Data," BILLINMON.COM, (March, 2001), 5-7, 10-11
[5]. Jesper Andersen, Anders Giversen, Allan H. Jensen, Rune S. Larsen, Torben Bach Pederson, JanneSkyt. "Analyzing clickstreams using subsessions", Proceedings of the 3rd ACM international workshop on Data warehousing and OLAP, pp. 25-32, ACM Press, (2000).

Abstract: Companies tends towards more availability, less cost, managed risk, agility- all of which are providing by cloud computing. The cloud computing is a way to deliver IT services on demand and pay per usage, and it can stores huge amount of data. But until now many companies don't wish to use cloud computing technology due to concerns about data secrecy and protection. This paper aims to provide a secure, effective, and flexible method to improve data storage security in cloud computing. By using IBC the key management complexity will decrease and not need to certificate issued, also the use of ECC provides data confidentiality and use ECDS provides data integrity.

Keywords: Cloud computing, Cryptography, Elliptic Curve, Data storage.

[1]. Jeffrey Voas and Jia Zhang, "Cloud Computing: New Wine or Just a New Bottle?", Published by the IEEE Computer Society, 2009. [2]. Sameeh A. Jassim, MSc thesis, "Mediated IBC-Based Management System of Identity and Access in Cloud Computing", College of Computer, University of Anbar, 2013.
[3]. SameeraAbdulrahmanAlmulla and Chan YeobYeun, "Cloud Computing Security Management", Engineering Systems Management and Its Applications (ICESMA), Presented at 2nd IEEE International Conference, 30 march 2010.
[4]. Mohamed Abdelhamid, PhD thesis, "Privacy-preserving Personal Information Management", School of Computer Science, McGill University, Montreal, August 2009.
[5]. Syam Kumar P and Subramanian R, "An Efficient and Secure Protocol for Ensuring Data Storage Security in Cloud Computing", IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 6, No 1, November 2011.

Abstract: Web search engine has long become the most important portal for ordinary people looking for useful information on the web. User might experience failure when search engine return irrelevance information due to enormous variety of user's context and ambiguity of text. The Existing System failed to resist ambiguity of text. Our Proposed System aim at removing ambiguity of text and provide the relevance information to the User. We learn privacy protection in PWS applications that model user preferences as hierarchical user profiles (via taxonomy Structure).

[1]. Lidan Shou, He Bai, Ke Chen, and Gang Chen,Feb 2014"Supporting Privacy Protection in Personalized Web Search," vol . 26, no. 2.
[2]. Z. Dou, R. Song, and J.-R. Wen, 2007"A Large-Scale Evaluation and Analysis of Personalized Search Strategies," Proc. Int‟l Conf. World Wide Web (WWW), pp. 581-590.
[3]. J. Teevan, S.T. Dumais, and E. Horvitz,2005 "Personalizing Search via Automated Analysis of Interests and Activities," Proc. 28th Ann. Int‟l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR), pp. 449-456.
[4]. M. Spertta and S. Gach, 2005"Personalizing Search Based on User Search Histories," Proc. IEEE/WIC/ACM Int‟l Conf. Web Intelligence (WI).
[5]. B. Tan, X. Shen, and C. Zhai, 2006"Mining Long-Term Search History to Improve Search Accuracy," Proc. ACM SIGKDD Int‟l Conf. Knowledge Discovery and Data Mining (KDD).

Abstract: This paper presents a method to detect copy-move image forgery using CS-LBP (centre symmetric local binary pattern), an extension of basic local binary pattern. In the proposed method, firstly gray level conversion is performed on the given image. Later, the image will be decomposed into fixed sized non-overlapping blocks. Feature extraction can be done using CS- LBP, as this technique is invariant to illumination and rotation. Then distance between the extracted feature vectors is calculated and are sorted lexicographically, and retained only those pairs of blocks that have minimal distances between them. Those minimal distances compared with predefined threshold value serve as the purpose of the copied regions. In the end, post-processing is done using morphological operation and mask is generated for the region detected as forged region.

Keywords: Copy-move forgery detection, CS-LBP, feature extraction, Image Tampering.

[1]. J. Fridrich, B. D. Soukal, and A. J. Lukas, "Detection of copy move forgery in digital images," in Proceedings of the Digital Forensic Research Workshop, Cleveland, Ohio, USA,August 2003.
[2]. Mohammad Farukh Hashmia et al, "Copy-move Image Forgery Detection Using an Efficient and Robust Method Combining Un-decimated Wavelet Transform and Scale Invariant Feature Transform" 2014 AASRI Conference on Circuit and Signal Processing (CSP 2014).
[3]. G. Muhammad, M. Hussain, G. Bebis, "Passive copy move image forgery detection using undecimated dyadic wavelet transform," Digital Investigation, vol. 9 (1), pp. 49-57, 2012.
[4]. Ning Zheng, Yixing Wang and Ming Xu, "A LBP Based Method for Detecting Copy-Move Forgery with Rotation," Springer Science, pp. 261-267,2013.
[5]. B. L. Shivakumar and Lt. Dr. Santhosh. "Detecting copy-move forgery in Digital images: A survey and analysis of current methods," Global Journal of Computer Science and Technology, vol. 10, no. 7, 2010.

Abstract: Image processing is one of the most researched areas in computer science and it finds numerous applications in various fields like, medical research and diagnosis, geological research, crime investigation, and so on. In most of the institutions, authentication of members is carried out by facial recognition, fingerprint or retina scan, etc. Recently image mining has gained interest of innumerable researchers because of its widespread applications and so little development in the field. This paper focusses on studying about some of the important techniques researched and developed successfully to support image mining with considerable accuracy. In later sections, a comparative study of the same is carried out, which tries to identify the best method of all.

Keywords: Feature Extraction, Image Mining, Image retrieval, Object Recognition, Image Clustering

[1]. Feature Extraction for Image Mining Patricia G. Foschi Romberg Tiburon Center for Environmental Studies San Francisco State University Deepak Kolippakkam*, Huan Liu and Amit Mandvikar Department of Computer Science & Engineering Arizona State University, Tempe, Arizona, USA
[2]. Color Image Clustering using Block Truncation Algorithm
[3]. Dr. Sanjay Silakari 1, Dr. Mahesh Motwani 2 and Manish Maheshwari 3
[4]. 1 Department of Computer Science and Engineering Rajiv Gandhi Technical University Bhopal, M.P., India
[5]. 2 Department of Computer Science and Engineering Jabalpur Engineering College Jabalpur, M.P., India
[6]. 3 Department of Computer Science & Application Makhanlal C. National University of Journalism and Communication Bhopal, M.P., India

Abstract: Identification of blood vessels in medical images plays a vital role in explaining many practical applications pertaining to the diagnosis of the blood vessels. Vessel segmentation algorithms are the important elements of automated radiological diagnostic systems. Based on the quality of image, domain of application and method applied (automatic or semi-automatic), segmentation methods may change. Some images may have artifacts such as noise and these require image pre-processing depending on the quality of image. Depending on the image quality and the general image artifacts such as noise, some methods may require image pre-processing. Blood vessel extraction is important for the diagnosis of various ophthalmic disorders like glaucoma, diabetic retinopathy and also brain tumour.

[1]. Bob Zhang , XiangqianWu, JaneYou , QinLi , Fakhri Karray, Detection of microaneurysms using multi-scale correlation coefficients, 2010 Elsevier Pattern Recognition 43 (2010) 2237–2248.
[2]. Marios Vlachos , Evangelos Dermatas, Multi-scale retinal vessel segmentation using line tracking, 2009 Elsevier Computerized Medical Imaging and Graphics 34 (2010) 213–227.
[3]. Ana Maria Mendonça, Aurélio Campilho, Segmentation of Retinal Blood Vessels by Combining the Detection of Centerlines and Morphological Reconstruction, IEEE Transactions On Medical Imaging, Vol. 25, No. 9, September 2006; 1200-1212.
[4]. Joes Staal, Michael D. Abràmoff, Meindert Niemeijer, Max A. Viergever and Bram van Ginneken, Ridge-Based Vessel Segmentation in Color Images of the Retina, IEEE Transactions On Medical Imaging, Vol. 23, No. 4, April 2004.
[5]. Frédéric Zana and Jean-Claude Klein, Segmentation of Vessel-Like Patterns Using Mathematical Morphology and Curvature Evaluation, IEEE Transactions On Image Processing, Vol. 10, No.7, July 2001, 1010-1019.
[6]. Mohammed Al-Rawi, Munib Qutaishat, Mohammed Arrar, An improved matched filter for blood vessel detection of digital retinal images, 2006 Elsevier Computers in Biology and Medicine 37 (2007) 262 – 267.

Abstract: The current state of the art techniques for robot swarm navigation use communication assisted local interactions to achieve a cooperative solution to the problem. While this solution provides optimum, near-shortest paths in swarms of considerable population, it fails to scale well with reduction in population. We propose a solution to this shortcoming by incorporating cardinality in the swarm and utilizing it to impose constraints on the robots' possible movements. We have tested our solution using time and population as parameters of performance. We compare the performance with existing communication assisted algorithm to accentuate the improvement in performance and outline possible future work in the area.

Keywords: Cardinality, Communication assisted navigation, Cooperative control, Robot swarm navigation, Sparsely populated swarms

[1]. Xing P Guo, Editor, Robotics Research Trends, Nova, 2008.
[2]. M. Dorigo and E. Sahin, Guest editorial: Swarm robotics, Autonomous Robotics, vol. 17, no. 2-3, 2004.
[3]. T. De Wolf and T. Holvoet, Emergence vs. self-organisation: Different concepts but promising when combined, Engineering Self-Organising Systems, pages 1-15, Springer Berlin Heidelberg, 2005.
[4]. S. Camazine, J.-L. Deneubourg, N. R. Franks, J. Sneyd, G. Theraulaz, E. Bonabeau, Self-Organization in Biological Systems, PrincetonStudies in Complexity. Princeton University Press, Princeton, NJ, 2001.
[5]. R. Sharpe, B. Webb, Simulated and situated models of chemical trail following in ants, Proceedings of the International Conference on Simulation of Adaptive Behavior, pages 195–204, 1998. [6]. Hoff, Nicholas R., et al. Two foraging algorithms for robot swarms using only local communication, Robotics and Biomimetics (ROBIO), 2010 IEEE International Conference on. IEEE, 2010.

Abstract: The information over the internet is growing at rapid rate, so web data extraction systems are required to extract the required information. One such technique is web wrapper, which is a supervised learning approach in which a template (program) is developed by the programmer to extract some specific data. This research paper provides a web wrapper known as Relational Web Wrapper which extracts related information of the webpage. Finally the performance evaluation of this web wrapper on the basis of time to extract the data and accuracy provided are shown in results. The results shows this web wrapper provides efficient results.

Keywords- Web data extraction, web wrapper ,relational data.

[1] Muslea I, Minton S and Knoblock CA, "Hierarchical wrapper induction for semi structured information sources", Journal of autonomous agents and multi-agent systems, vol. 4, pp. 93-114, 2001.

[2] R. Baumgartner, S. Flesca and G. Gottlob. "Declarative information extraction, Web crawling, and recursive wrapping with Lixto". In Proc. of Int. Conf. on Logic Programming and Nonmonotonic Reasoning, Vienna, Austria, 2001.

[3] R. Baumgartner, S. Flesca and G. Gottlob. "Visual web information extraction with Lixto". in the VLDB Journal, pp. 119–128, 2001.

[4] Senellart, P, Mittal A, Muschick D, Gilleron, R and Tommasi M, "Automatic wrapper induction from hidden-web sources with domain knowledge", WIDM, pp. 9-16, 2008.

[5] Soderland S, Fisher D, Aseltine J and Lehnert W, "CRYSTAL: Inducing a conceptual dictionary". Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI), 1995.

Abstract: Distributed computing systems are of current interest due to the advancement of microprocessor technology and computer network. The prime function of effective utilization of distributed system is accurately mapping of task and tasks their scheduling on different processors for reducing their total finish time. Total runtime is taken time for all module with the runtime of tasks and their communication cost among tasks. In Distributed processing system, partitioning of a task into modules and proper allocation of module among processors are more important factor for efficient utilization of resources.

[1]. Introduction to distributed software enginnering . SHATZ, S/M.AND WANG,J.P. 2, s.l. : computer socity , 1987.
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[5]. Reliability Evaluation of Distributed System Based on Failure Data Analysis,. Yadav.P.K, Bhatia K,and Gulati Sagar. s.l. : International Journal of Computer Engineering, 2010, Vol. 2.
[6]. An efficient algortihm for multiprocessor scheduling with dynamic reassignment . V.Kumar, M.P Singh and P.K Yadav. s.l. : national seminar on Theoretical Computer Science, 1996, Vol. 1.

Abstract: Traditional approaches for classification of sentiments depend on lexical or syntax based feature or on both. Different methods for sentiments classifications are described .The main goal of analysis of the sentiments is to obtain the writer's feelings whether +ve or -ve. Sentiment Analysis is having its importance because in this world of Internet the opinion of public is very important.So the need for analysis of sentiment is increasing heavily.

Keywords: Corpus, WordNet, Sentiments, Synset, Disambiguation

[1]. HarnessingWordNet Senses for Supervised Sentiment Classification",Balamurali A,Aditya Joshi,Pushpak Bhattacharyya IITB-Monash Research Academy, IIT Bombay Dept. of Computer Science and Engineering, IIT Bombay.
[2]. A systematic Approach towards the Solution of the Polysemy Problem in Natural Language Processing" ,Abed Alhakim Freihat April 2011.
[3]. Sentiment Classification of Reviews Using SentiWordNet", Ohana, B., Tierney, B.: Sentiment classification of reviews using SentiWordNet. 9th. IT&T Conference, Dublin Institute of Technology, Dublin, Ireland.
[4]. Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches",Pimwadee Chaovalit Department of Information SystemsUniversity of Maryland, Baltimore County Lina Zhou Department of Information SystemsUniversity of Maryland, Baltimore County.
[5]. Sentiment Classification in Movie Reviews",An Approach Using Subjectivity FilteringDaniel Pomerantz, McGill University.

Abstract: Sentiment analysis and opinion mining field concentrates on automatically classifying sentiments of documents. But detecting mere sentiments does not provide one the sufficient knowledge concealed in the text. In this paper, we have evaluated joint sentiment and topic detection model (JST) to detect sentiment and topic simultaneously from text. JST is evaluated for movie reviews and product reviews with the help of domain independent prior information. AS no labeled data is required for training JST, it becomes highly portable to any domain. JST provides user more information regarding the text than mere sentiments.

Keywords: sentiment analysis; topic detection; Latent Dirichlet Allocation (LDA); opinion mining; Joint sentiment and Topic (JST)

[1] Lin, Chenghua; He, Yulan; Everson, Richard and R ̈uger, Stefan (2012). ―Weakly-supervised joint sentiment-topic detection from text‖. IEEE Transactions on Knowledge and Data Engineering, 24(6), pp. 1134–1145.
[2] P.D. Turney, ―Thumbs Up Or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews‖, Proc. Assoc. for Computational Linguistics (ACL ‗01), pp. 417-424, 2001.
[3] B. Pang, L. Lee, and S. Vaithyanathan, ―Thumbs Up? Sentiment Classification Using Machine Learning Techniques‖, Proc. ACL Conf. Empirical Methods in Natural Language Processing (EMNLP) pp. 79-86, 2002.
[4] B. Pang and L. Lee, ―A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts‖, Proc. 42th Ann. Meeting on Assoc. for Computational Linguistics (ACL), pp. 271-278, 2004.
[5] C. Whitelaw, N. Garg, and S. Argamon, ―Using Appraisal Groups for Sentiment Analysis,‖Proc. 14th ACM Int'l Conf. Information and Knowledge Management (CIKM), pp. 625-631, 2005.

Abstract: The major interest of the authors in surveying cloud computing based healthcare of diabetes is to make a thorough check in the patient's blood glucose control at remote areas. The improvement in the technology and combined research can bring out the best medication and diagnosis for any disease. Today, the most challenging syndrome across the globe is diabetes mellitus. In the latest survey, the world's 65% of the population is suffering from either Type 1 or Type 2 diabetes mellitus. But in most of the cases, the patient's blood glucose level is not the same 24x7 hours and medication 24x7 hours is impossible. Thus cloud based healthcare is the one and only solution.

[1] Yan Hu, Fangjie Lu, Israr Khan, Guohua Bai, A Cloud Computing Solution for Sharing Healthcare Information, The 7th International Conference for Internet Technology and Secured Transactions (ICITST), IEEE, 2012, London.
[2] G.Nikhita Reddy, G.J.Ugander Reddy, Study of Cloud Computing in HealthCare Industry.
[3] Yan Hu and Guohua Bai, A systematic literature review of cloud computing in e-health, Health Informatics, An InternationalJournal (HIIJ) Vol.3, No.4, pp.11-20, November 2014.
[4] Sanjay P. Ahuja1, Sindhu Mani1 & Jesus Zambrano1, A Survey of the State of Cloud Computing in Healthcare, Network and Communication Technologies; Vol. 1, No. 2; pp.12-19, 2012, Published by Canadian Centre of Science and Education.
[5] AtiyaParveen, Sobia Habib, Waseem Ahmad, The cloud changing the Indian healthcare system, International Journal of Computer Science and Mobile Computing, IJCSMC, Vol. 2, Issue. 5, May 2013, pg.238 – 243.

Abstract: Sentiment analysis is very perplexing and massive issue in the field of social data mining. Twitter is one of the mostly used social media where people discuss on various issues in a dense way. The tweets about a particular topic give peoples' views, opinions, orientations, inclinations about that topic. In this work, we have used pre-labeled (with positive, negative and neutral opinion) tweets on particular topics for sentiment classification. Opinion score of each tweet is calculated using feature vectors. These opinion score is used to classify the tweets into positive, negative and neutral classes. Then using various machine learning classifiers the accuracy of predicted classification with respect to actual classification is being calculated and compared using supervised learning model. Along with building a sentiment classification model, analysis of tweets is being carried out by visualizing the wordcloud of tweets using R. Keywords: Sentiment analysis, Machine Learning, Twitter, Opinion score, R packages, Wordclouds.

[1]. Kishori K. Pawar, Pukhraj Shrishrimal, R. R. Deshmukh, Twitter Sentiment Analysis: A Review, International Journal of Scientific & Engineering Research, 6(2), 2015, 957-964.
[2]. Bo Pang, Lillian Lee, Opinion mining and sentiment analysis, Foundations and Trends in Information Retrieval ,2(1-2), 2008, 1–135.
[3]. Bing Liu, Sentiment analysis and opinion mining, Synthesis Lectures on Human Language Technologies, no. 1, 2012, (1-167).
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[5]. http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6010, sited on April 1, 2015.
[6]. http://mpqa.cs.pitt.edu/lexicons/arg_lexicon/, sited on April 1, 2015

Abstract: The objective of this paper is to optimize AO* algorithm with a depth limited search. AO* is one of the major AND-OR graph search algorithms. According to AO* algorithm it is not exploring all the solution paths once it has got the solution. Here the modified method is providing better heuristic value for the solution if the search space is unstable. It is also considering interacting sub problems in the in the search space considering the loop structures.

Keywords - AND-OR graph, best first search, depth limited search, heuristic.

[1]. Elaine Rich, Kevin knight, Shivashankar b. Nair , Artificial Intelligence (New Delhi,The Tata McGraw-Hill Companies,2009).
[2]. Ethan Burns, Sofia Lemons, Wheeler Ruml, Rong Zhou, Best-First Heuristic Search for Multicore Machines, Journal of Artificial Intelligence Research, 39, 2010, 689–743.
[3]. Eric A. Hansen, Rong Zhou, Anytime Heuristic Search, Journal of Artificial Intelligence Research, 28,2007, 267-297.
[4]. Richard E. korf, Depth limited search for real time problem solving, The journal of real time systems,2,1990,7-24.
[5]. Eric A. Hansen, Shlomo Zilberstein, LAO*: A heuristic search algorithm that finds solutions with loops, Artificial Intelligence, 129, 2001, 35–62.