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On the Web and other document repositories the amount of content stored and shared keeps increasing steadily and fast that results in well known difficulties and problems when it comes to finding and properly managing information in massive volumes. In the last decade with the development of search engine technologies Striking progress has been achieved, which collect, store and pre-process information worldwide to return relevant resources instantly in response to users’ needs. However, users still miss or need considerable effort sometimes to reach their targets, even if the sought information is present in the search space. Currently consolidated content description and query processing techniques for Information Retrieval are based on keywords, and therefore provide limited capabilities to grasp and exploit the conceptualizations involved in user needs and content meanings are the common caust. This involves limitations such as the inability to describe relations between search terms to solve the limitations of keyword based , the idea of concept based information retrieval ,conceptual search, understood as searching or retrieving by meanings rather than keyword or literal strings, has been the focus of a wide body of research in the information retrieval field. The semantic technologies such as XML and ontology can play important role for the development of semantic based information retrieval. This paper is an attempt to develop semantic based Information Retrieval for the exploitation of domain knowledge to support semantic information retrieval search capabilities in large document repositories more intelligently; it explores the use of semantic technologies such as xml and ontology to support more expressive queries and more accurate results. for this we have collected the documents from the different domains and design the tree structures of the documents in the form of xml and ontology and data mining technique such as clustering and then retrieve the information from this structure based on user interest that provide the concept based.

Information retrieval,ontology,datamining,semantic web, K-Mean,search

[1] Susan T. Dumais. George W. Furnas. Thomas K. Landaue” Indexing by Latent Semantic Analysis” Bell Communications Research 1990
[2] Gonzalo, Verdejo, Chugur, & Cigarrán “Sense clusters for information retrieval” Proceedings of the COLING/ACL`98 Workshop on Usage of WordNet for NLP, Montreal, 1998
[3] Bruce Croft w.”Boolean queries and term dependencies in probabilistic retrieval” 1986
[4] C. J. van “Information Retrieval”, ACM sigir Forum, v.17 n.4, 1979
[5] Thomas R.Gruber “A Translation Approach to. Portable Ontology Specifications” . Knowledge Acquisition, 5(2):199-220,1993
[6] T. Berners-Lee, J. Hendler, and O. Lassila. “The semantic web”. Scientific American, 284(5):28–37, 2001
[7] OWL Web Ontology Language. http://www.w3.org/TR/owl-ref/.
[8] Blanco, E., Cankaya, H. & Moldovan, D. “Commonsense Knowledge Extraction Using Concepts” Properties. Proceedings of the Twenty-Fourth International Florida Artificial Intelligence Research Society Conference. 2011
[9] Li, H., Tian, Y., Ye, B. & Cai, Q. ” Comparison of Current Semantic Similarity Methods in WordNet”. 201O International Conference on Computer Application and System Modeling (ICCASM 2010). 978-1 -4244-7237-6/10, IEEE.
[10] Nidelkou, E., Papastathis, V., Papadogiorgaki, M., Kompatsiaris, I., Bratu, B., Ribiere, M. & Waddington, S. ” User Profile Modeling and Learning”. In Encyclopedia of Information Science and Technology”, Second Edition. DOI: 10.4018/978-1-60566-026- 4.ch627. 3934-3939. IGI Global.
[11] Harb, H., & Fouad, K.” Semantic web based Approach to learn and update Learner Profile in Adaptive E-Learning.” Al-Azhar Engineering Eleventh International Conference, December 23-26 2010
[12] J. Han and M. Kamber. “Data Mining: Concepts and Techniques”. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2005.
[13] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. “From data mining to knowledge discovery in databases”. AI magazine, 17(3):37, 1996.
[14] N. Khasawneh and C.-C. Chan. “Active user-based and ontology-based web log data preprocessing for web usage mining”. In Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pages 325–328, 2006.
[15] S. J. Russell and P. Norvig. “Artificial Intelligence: A Modern Approach.” Pearson Education, 2 edition, 2003.
[16] D.Perez-Rey, A. Anguita, and J.Crespo.“Ontodataclean: Ontologybased integration and preprocessing of distributed data. In Biological and Medical Data Analysis”, pages 262–272. Springer, 2006.
[17] N. Balcan, A. Blum, and Y. Mansour.”Exploiting ontology structures and unlabeled data for learning”. In Proceedings of the 30th International Conference on Machine Learning, pages 1112–1120, 2013.
[18] A. Bellandi, B. Furletti, V. Grossi, and A. Romei.” Ontology-driven association rule extraction: A case study”. Contexts and Ontologies Representation and Reasoning, page 10, 2007.
[19] C. Marinica and F. Guillet.”Knowledge-based interactive postmining of association rules using ontologies”. Knowledge and Data Engineering”, IEEE Transactions on, 22(6):784–797, 2010.
[20] G. Mansingh, K.-M. Osei-Bryson, and H. Reichgelt.”Using ontologies to facilitate post-processing of association rules by domain experts”. Information Sciences, 181(3):419–434, 2011.
[21] D. C. Wimalasuriya and D. Dou.”Components for information extraction: Ontology-based information extractors and generic platforms”. In Proceedings of the 19th ACM Conference on Information and Knowledge Management (CIKM), pages 9–18, 2010.
[22] S. J. Russell and P. Norvig. “Artificial Intelligence: A Modern Approach”. Pearson Education, 2 edition, 2003.
[23] T. R. Gruber. “Toward principles for the design of ontologies used for knowledge sharing” International journal of human-computer studies, 43(5):907–928, 1995.
[24] R. Studer, V. R. Benjamins, and D. Fensel.”Knowledge engineering: principles and methods”. Data & knowledge engineering, 25(1)`:161– 197, 1998.
[25] The gene ontology consortium. “Creating the gene ontology resource: design and implementation”. Genome Res., 11(8):1425–1433, August 2001.
[26] D. Lindberg, B. Humphries, and A. McCray.”The Unified Medical Language System”. Methods of Information in Medicine, 32(4):281–291, 1993.
[27] The National Center for Biomedical Ontology. http://www.bioontology.org/.
[28] T. Berners-Lee, J. Hendler, and O. Lassila. “The semantic web”. Scientific American, 284(5):28–37, 2001.
[29] OWL Web Ontology Language. http://www.w3.org/TR/owl-ref/.
[30] N. Balcan, A. Blum, and Y. Mansour.” Exploiting ontology structures and unlabeled data for learning”. In Proceedings of the 30th International Conference on Machine Learning, pages 1112–1120, 2013.
[31] F. Gutierrez, D. Dou, A. Martini, S. Fickas, and H. Zong.” Hybrid ontology-based information extraction for automated text grading”. In Machine Learning and Applications (ICMLA), 2013 12th International Conference on, volume 1, pages 359–364. IEEE, 2013.
[32] [31] D. C. Wimalasuriya and D. Dou.”Ontology-based information extraction:” An introduction and a survey of current approaches”. Journal of Information Science, 36(3):306–323, 2010.

Muqeem Ahmed, "Semantic Based Intelligent Information Retrieval through Data mining and Ontology," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.210-217, 2017.

Since, the last three or four years, the field of “big data” has appeared as the new frontier in the wide spectrum of IT-enabled innovations and favorable time allowed by the information revolution. Today, there is a raise necessity to analyses very huge datasets, that have been coined big data, and in need of uniqueness storage and processing infrastructures. MapReduce is a programming model the goal of processing big data in a parallel and distributed manner. In MapReduce, the client describes a map function that processes a key/value pair to procreate a set of intermediate value pairs & key, and a reduce function that merges all intermediate values be associated with the same intermediate key. In this paper, we aimed to demonstrate a close-up view about MapReduce. The MapReduce is a famous framework for data-intensive distributed computing of batch jobs. This is over-simplify fault tolerance, many implementations of MapReduce materialize the overall output of every map and reduce task before it can be consumed. Finally, we also discuss the comparison between RDBMS and MapReduce, and famous scheduling algorithms in this field.

Big Data, MapReduce, Scheduling, Processing Layer, Indexing, Data Layout

[1]. Kim, G.-H., Trimi, S., & Chung, J.-H. (2014). Big-data applications in the government sector. Communicationsof the ACM, 57(3), pp 78–85.
[2]. Dr. Yusuf Perwej, “An Experiential Study of the Big Data,” for published in the International Transaction of Electrical and Computer Engineers System (ITECES), USA, ISSN (Print): 2373-1273 ISSN (Online): 2373-1281, Vol. 4, No. 1, page 14-25, March 2017, DOI:10.12691/iteces-4-1-3.
[3]. R. Murugesh, I. Meenatchi, "A Study Using PI on: Sorting Structured Big Data In Distributed Environment Using Apache Hadoop MapReduce", International Journal of Computer Sciences and Engineering, Vol.2, Issue.8, pp.35-38, 2014.
[4]. “Apache Hadoop,” Apache. [Online]. Available: http://hadoop.apache.org/. [Accessed: 18-Feb-2015].
[5]. M. Khan, P. M. Ashton, M. Li, G. A. Taylor, I. Pisica, and J. Liu, “Parallel Detrended Fluctuation Analysis for Fast Event Detection on Massive PMU Data,” Smart Grid, IEEE Trans., vol. 6, no. 1, pp. 360–368, Jan. 2015.
[6]. K. Parimala1 G. Rajkumar, A. Ruba, S. Vijayalakshmi, "Challenges and Opportunities with Big Data", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.16-20, 2017.
[7]. Lee, D., Kim, J.-S., & Maeng, S. “Large-scale incremental processing with MapReduce”, Future Generation Computer Systems, 36, pp 66–79, (2014), doi:10.1016/j.future.2013.09.010.
[8]. M. Khan, M. Li, P. Ashton, G. Taylor, and J. Liu, “Big data analytics on PMU measurements,” in Fuzzy Systems and Knowledge Discovery (FSKD), 2014 11th International Conference on, 2014, pp. pp 715–719.
[9]. Qi, C., Cheng, L., & Zhen, X. (2014). Improving mapreduce performance using smart speculative execution strategy. IEEE Transactions on Computers, Vol. 63(4), pp 954–967. Doi:10.1109/TC.2013.15.
[10]. J. Kwon, K. Park, D. Lee, S. Lee, PSR: Pre-computing Solutions in RDBMS for Fast Web services Composition Search, in: Proceedings of the 2nd International Conference on Web Services, Salt Lake City, Utah, USA, ICWS 2007, pp. 808-815.
[11]. Yan, F., Cherkasova, L., Zhang, Z., & Smirni, E. (2014). Heterogeneous cores for mapreduce processing: Opportunity or challenge? Paper presented at the proceedings of IEEE/IFIP NOMS.
[12]. Chen, R., & Chen, H. , Tiled-MapReduce: Efficient and flexible MapReduce processing on multicore with tiling. ACM Transactions on Architecture and Code Optimization (TACO), Volume 10 Issue 1, April 2013, pp 3.
[13]. Dean, J. & S. Ghemawat (2004). Mapreduce: simpli_ed data processing on large clusters. In Proceedings of the 6th conference on Symposium on Opearting Systems Design & Imple-mentation - Volume 6, OSDI`04, Berkeley, CA, USA, pp. 10-10. USENIX Association.
[14]. Lee, K.-H., Y.-J. Lee, H. Choi, Y. D. Chung, & B. Moon “Parallel data processing with mapreduce: a survey”, SIGMOD Rec. vol 40 (4), pp 11-20. (2012).
[15]. S. Sakr, A. Liu, A. Fayoumi, "The family of mapreduce and large-scale data processing systems", ACM Computing Surveys, vol. 46, no. 1, pp. 1-44, 2013.
[16]. Q. He, Q. Tan, X. Ma, Z. Shi, "The high-activity parallel implementation of data preprocessing based on MapReduce", Proc. of the 5th International Conference on Rough Set and Knowledge Technology, 2010.
[17]. Google developers: Web metrics - size and number of resources,” https:// developers.google.com/speed/articles/web-metrics, accessed: 11/04/2013.
[18]. Mapreduce:Chainingjobs,http://developer.yahoo.com / hadoop/ tutorial/ module4.html#chaining, accessed: 11/04/2013.
[19]. Richter, S., J.-A. Quian_e-Ruiz, S. Schuh, & J. Dittrich. “Towards zero-overhead adaptive indexing in hadoop”, (2012), CoRR abs/1212.3480.
[20]. Dittrich, J., J.-A. Quian_e-Ruiz, A. Jindal, Y. Kargin, V. Setty, & J. Schad (2010, September). Hadoop++: making a yellow elephant run like a cheetah (without it even noticing). Proc. VLDB Endow.vol 3 (1-2), 515-529.
[21]. Eltabakh, M. Y., F. Ozcan, Y. Sismanis, P. J. Haas, H. Pirahesh, & J. Vondrak (2013). Eagleeyed elephant: Split-oriented indexing in hadoop. In Proceedings of the 16th International Conference on Extending Database Technology, EDBT `13, New York, NY, USA, pp. 89-100. ACM.
[22]. Ailamaki, A., D. J. DeWitt, M. D. Hill, & M. Skounakis (2001). Weaving relations for cache performance. In Proceedings of the 27th International Conference on Very Large Data Bases, VLDB `01, San Francisco, CA, USA, pp. 169-180. Morgan Kaufmann Publishers Inc.
[23]. https://hadoop.apache.org/docs/r1.2.1/fair_scheduler.html
[24]. Xingwu Zheng, Zhou Zhou, Xu Yang, Zhiling Lan, Jia Wang, "Exploring Plan-Based Scheduling for Large-Scale Computing Systems", Cluster Computing (CLUSTER) 2016 IEEE International Conference on, pp. 259-268, 2016, ISSN 2168-9253.
[25]. Y. Tao Y, Q. Zhang, L. Shi and P. Chen, “ Job scheduling optimization for multi-user MapReduce clusters ”, In: The fourth international symposium on parallel architectures, algorithms and programming. IEEE; 2011. pp 213–17.
[26]. Nikolaos D. Doulamis, Panagiotis Kokkinos, Emmanouel Varvarigos, "Resource Selection for Tasks with Time Requirements Using Spectral Clustering", Computers IEEE Transactions on, vol. 63, pp. 461-474, 2014, ISSN 0018-9340.
[27]. Mohammad Hammoud, M. Suhail Rehman, Majd F. Sakr, "Center-of-Gravity Reduce Task Scheduling to Lower MapReduce Network Traffic", IEEE, 2012, pp.49–58. doi:10.1109/CLOUD.2012.92
[28]. P. Nguyen, T. Simon, M. Halem, D. Chapman and Q. Le, “ A hybrid scheduling algorithm for data intensive workloads in aMapReduce environment”, In: Proceedings of the 2012 IEEE/ ACM fifth international conference on utility and cloud computing. Washington, DC, USA: IEEE computer society; UCC`12, 2012, pp 161-168.

Yusuf Perwej, Md. Husamuddin, Fokrul Alom Mazarbhuiya, "An Extensive Investigate the MapReduce Technology," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.218-225, 2017.

Dataset collected from multiple sources is often inconsistent and generates different label of decisions for the same conditional attribute values. A method for handling inconsistency has been proposed here using Kohonen Self organizing neural network, an unsupervised learning approach. After removing inconsistency, the minimum subset of attributes in the dataset called reducts are selected using Rough Set Theory, which effectively reduces dimensionality of the dataset. Unlike most of the existing reduct generation algorithms where all attributes are examined, here evaluation of all attributes is not required and therefore, time complexity has been improved considerably. In the next step, considering core attribute as root node of a decision tree, all possible rules are generated which are pruned based on information entropy and coverage of the rule set. The classifier is built using the reduced rule set demonstrating comparable results with the classifier consisting of all attributes.

Inconsistency, Rough Set, Unsupervised Neural Network

[1]. Zdzislaw Pawlak, “Rough sets”, International Journal of Computer and Information Sciences, 11, 341-356, 1982.
[2]. I. Düntsch, and G. Gediga, “Algebraic aspects of attribute dependencies in information systems”, Fundamental Informaticae, Vol. 29, 1997, pp. 119-133.
[3]. A. Øhrn, “Discernibility and rough sets in medicine: tools and applications”, PhD thesis, Department of Computer and information science, Norwegian University of Science and Technology, 1999.
[4]. J.G. Bazan, M.S. Szczuka, and J. Wroblewski, “A new version of rough set exploration system,” Lecture notes in artificial intelligence, Vol.2475, 2002, pp. 397-404.
[5]. N.Ttow, D.R. Morse, and D.M. Roberts, “Rough set approximation as formal concept,” Journal of advanced computational intelligence and intelligent informatics, Vol.10, No.5, 2006, pp. 606-611.
[6]. P.Guo, and H. Tanaka, “Upper and lower possibility distributions with rough set concepts,”, In Rough set theory and granular computing, Springer, 2002, pp. 243-250.
[7]. Kohonen, T. “Self-Organizing Maps”, 3rd edition, Berlin: Springer-Verlag, 2001.
[8]. Zhangyan Xu, Liyu Huang, Wenbin Qian, Bingru Yang, “Quick Attribute Reduction Algorithm Based on Improved Frequent Pattern Tree”.
[9]. Ganesan G, Raghavendra Rao C., Latha D., “An overview of rough sets”, proceedings of the National Conference on the emerging trends in Pure and Applied Mathematics, Palayamkottai, India, pp: 70-76, 2005
[10]. [Han, 2001] Han J and Kamber M, “Data Mining: Concepts and Techniques”, Morgan Kaufmann, 2001, 279-325.
[11]. C.R.Rao and P.V.Kumar. “Functional Dependencies through Val”,
[12]. ICCMSC ’99, India, TMH publications 116-123, 1999.
[13]. Pawlak,1991] Zdzislaw Pawlak, “RoughSets- Theoretical Aspects and Reasoning about Data”, Kluwer Academic Publications, 1991
[14]. Quinlan J.R. “Induction of decision trees”. Machine Learning 181-106.
[15]. Ramadevi Y, C.R.Rao, “Knowledge Extraction Using Rough Sets –Gpcr – Classification”,International conference on Bioinformatics and diabetes mellitus, India, 2006.
[16]. [ [Starzyk, 1999] Starzyk J, Nelson D.E., SturtzK, “Reduct Generation in Information Systems”,Bulletin of International Rough Set Society, 3(1/2), 1999.

Vairaprakash Gurusamy, K. Nandhini, "Designing a Classifier Using Unsupervised Learning and Rough Set Theory," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.226-230, 2017.

A powered wheel chair is a mobility-aided device for persons with moderate/severe physical disabilities or chronic diseases as well as the elderly. In order to take care for different disabilities, various kinds of interfaces have been developed for powered wheelchair control such as joystick control, head control and sip-puff control. Many people with disabilities do not have the ability to control powered wheel chair using the above mentioned interfaces. The proposed model is a possible alternative. In this project, we have considered mainly the patients with the problem of Quadriplegia. These patients have paralysis below neck region. In this, we use the optical-type eye tracking system to control powered wheel chair. User‘s eye movement are given as input in the form of a image to MATLAB software. When user looks at appropriate angle, software will provide command based on the angle of position of pupil i.e., when user moves his eyes balls, left (move left), right (move right), up (move forward) and down (stop or reverse). In all other cases wheel chair will proceed straight. Once the image has been processed it moves onto the second part, Arduino. These will take the output from the laptop via a Bluetooth device and convert the signal into command signals that will be sent to the wheelchair motor circuit for movement. This project mainly focuses on wheel chair that process based on the movement of the eye. Adding to this, the wheel chair can also be used to people who has finger movements by using the touch control via mobile.

Quadriplegia, Arduino

[1] S. Tameem sultana and N. Kali Saranya, “Implementation of Head and Finger Movement Based Automatic Wheel Chair”, Bonfring International Journal of Power Systems and Integrated Circuits, vol. 1, Special Issue, pp 48-51, December 2011.
[2] Tabasum Shaikh, Naseem Farheen Sayyed, Shaheen Pathan, “Review of Multilevel Controlled Wheelchair”, 4th National Conference On Electronic Technologies, pp. 275-279, April 2013.
[3] Gneo, M., Severini, G., Conforto, S., Schmid, M., & D` Alessio, T. (2011). “Towards a Brain-Activated and Eye-Controlled Wheelchair”. international Journal of Bioelectromagnetism, Vol. 13, No. 1, pp. 44- 45, 2011.
[4] Lin, c.-S., Ho, C.-W., Chen, W.-c., Chiu, c.-c., & Yeh, M.-S. (2006) “Powered Wheelchair controlled by eye-tracking system”. Optica Applicata, Vol XXXVI, No 2-3, 2006.
[5] Ding Q, Tong K and Li G, “Development of an EOG (ElectroOculography) based human-computer interface”, In Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society, pp 6829–6831, EMBS, 2005.
[6] Kohei Arai and Ronny Mardiyanto, “Eyes Based Eletric Wheel Chair Control System”, International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 2, No. 12, 2011.
[7] Poonam S. Gajwani & Sharda A. Chhabria, “Eye Motion Tracking for Wheelchair Control”, International Journal of Information Technology and Knowledge Management, Volume 2, No. 2, pp. 185-187, JulyDecember 2010.
[8] M. Reitbauer, “Keep an Eye on Information Processing: Eye Tracking Evidence for the Influence of Hypertext Structures on Navigational Behaviour and Textual Complexity, LSP and Professional Communication”, Vol. 8, No. 2 (16), Winter 2008.
[9] DW Hansen and P Majaranta, “Basics of camera-based gaze tracking, In: Majaranta P et al (eds) Gaze interaction and applications of eye tracking”: advances in assistive technologies, Medical Information Science Reference, Hershey, pp 21–26, 2012.
[10] J.Millanetal, “Noninvasive brain-actuated control of a mobile robot by human EEG,” IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 1026–1033, June 2004.
[11] Kazuo Tanaka, Kazuyuki Matsunaga, and Hua O. Wang, “Electroencephalogram-Based Control of an Electric Wheelchair”, IEEE Transactions on Robotics, Vol. 21, no. 4, August 2005.
[12] Gunda Gautam, Gunda Sumanth, Karthikeyan K C, Shyam Sundar and D. Venkataraman “Eye Movement Based Electronic Wheel Chair for Physically Challenged Persons”, International Journal of Scientific & Technology Research, volume 3, issue 2, February 2014.
[13] Siri.T.Bhat1, B. Surekha2 and Shreesha Raghavan, “Sparsh Glove: A Gesture-Based Hardware Control for a Multipurpose Wheelchair” International Journal of Computer Sciences and Eng International Journal of Computer Sciences and Engineering, Volume-4, Special Issue-3, May 2016.

R. Darwin, "Eye Position Based Wheel Chair Control for Physically Challenged," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.231-234, 2017.

Online Social Networks become largest platform to express people feelings, opinions, views and real time events such as live tweets etc. Example Twitter has 315 million monthly active users, eighty two percent of active users on mobile and millions of tweets are being circulated through twitter every day. Various organizations as well as companies are interested in twitter data for finding the views of various people towards their products or events. Sarcasm refers to expressing negative feelings using positive words. To detect sarcasm among those tweets is comparatively more difficult. This paper discussed various approaches to find sarcasm on twitter. With the help of sarcasm detection, companies could analyze the feelings of user about their products. This is helpful for companies, as the companies could improve their quality of product.

Sarcasm, Sarcasm detection, Twitter

[1] Mukherjee S, Bala PK, “Sarcasm detection in microblogs using Naïve Bayes and fuzzy clustering “.Technology in Society, Volume 48,page no:19-27, February, 2017.
[2] Shubhodip Saha, Jainath Yadav and Prabhat Ranjan ,”Proposed approach for sarcasm detection in twitter”, Indian Journal of Science and Technology, Vol 10, Issue 25, July 2017.
[3] Ashwin Rajadesingan, Reza Zafarani, and Huan Liu , “Sarcasm detection on twitter: A behavioral model approach”, WSDM’15 Proceedings of 8th ACM International Conference on Web Search and Data Mining Conference,PP:97-106, February, 2015, Shanghai, China.
[4] Erik Cambria1, Soujanya Poria, Federica Bisio, Rajiv Bajpai, and Iti Chaturvedi,”The CLSA Model: A novel framework for concept-level sentiment analysis”, Springer International Publishing, pp 3-22,2015.
[5] Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan, Sune Lehmann, “Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm”, Association for Computational Linguistics, pp 1616-1626, Oct 2017.
[6] David Bamman and Noah A. Smith, “Contextualized Sarcasm Detection on Twitter”, Proceedings of the Ninth International AAAI Conference on Web and Social Media, UK, 2015.
[7] , Li Huang , Francesca Gino , Adam D. Galinsky, “The highest form of intelligence: Sarcasm increases creativity for both expressers and recipientsOrganizational Behavior and Human Decision Processes”, Vol 131, pp 162–177, 2015.
[8] Aditya Joshi, Pushpak Bha.acharyya, and Mark J Carman. “Automatic Sarcasm Detection: A Survey”, ACM Comput. Surv. 2017.
[9] M.-S. Yang, “A survey of fuzzy clustering”, Math. Comput. Model. Vol 18, Issue 11, pp 1—16, 1993.
[10] S. Chattopadhyay, D. Pratihar, S. Sarkar, ” A Comparative Study of Fuzzy C-Means Algorithm and Entropy-Based Fuzzy Clustering Algorithms”, Comput. Informatics. Vol 30, pp 701–720, 2012.
[11] Mukherjee, B. Liu, “Improving gender classification of blog authors”, EMNLP `10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Pages 207-217, 2010.
[12] S. Argamon, M. Koppel, J.W. Pennebaker, J. Schler, “Mining the Blogosphere: Age, gender and the varieties of self-expression”, First Monday, Vol 12, 2007
[13] Tayal DK, Yadav S, Gupta K, Rajput B. “Polarity detection of sarcastic political tweets”, Computing for Sustainable Global Development (INDIACom), 2014 International Conference, pp 605-628, 2014.
[14] Utsumi A.Verbal , “irony as implicit display of ironic environment: Distinguishing ironic utterances from nonirony”, Journal of Pragmatics, Vol 32, Issue 12, pp 1777-1806, 2000.
[15] Filatova E, “Irony and sarcasm: Corpus generation and analysis using crowd sourcing”, LREC, European Language Resources Association, pp 392-398, 2012.

K.Ranganath, MD.Sallauddin, Shabana , "Recent Trends In Sarcasm Detection on Online Social Networks," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.235-239, 2017.

Radio Frequency Identification (RFID) is a technology that can be used to automate waste management by providing details about the waste and sending massage to the system about the collected bins. In this paper, we are proposing a smart bin application based on RFID tags which contain information of each waste item. The wastes are tracked by smart bins using a RFID-based system without requiring the support of an external information system. Because of this system, the user is helped in the application of selective sorting and smart waste management system will come to know the status of the bin using ultrasonic sensors. Automatic Waste Management System helps to keep track of empty bins and also will give information about the filled bins to collective vehicles which will help them to collect filled bins with proper management automatically reduce the human efforts. This system will surely keep society hygiene and reduce air pollution spread by waste.

Waste management, RFID, Ultrasonic Sensors, RFID tags

[1] Y. Gloche, P. Couderc, “A Smart Waste Management with Self Describing Objects”, in the Proceeding of 2013 International conference on Smart Systems, Devices & Technologies, pp 63-70, 2013.
[2] F. Achmin F Olianto, Y ong Sheng Low, Wai Leong Yeow, “Smartbin: Smart Waste Management System” in the proceeding of 2015 Internatinal Conference on Intelligent Sensors, Sensor Networks & Information Processing(ISSNIP) Demo & Video, Singapore, pp 1-2, 2015.
[3] Demers M N, “Fundamentals of Geographic Information Systems”, Wiley, March 2009.

M. V. Amritkar, "Automatic Waste Management System with RFID and Ultrasonic Sensors," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.240-242, 2017.

In matters which involves life and death there is God and there are doctors. Doctors indeed are God to many of us. But looking at the other side of the coin many a time we see cases wherein a life is lost due to wrong diagnosis, which is too much a cost paid at the altar of human error. Then again we have cases where in a life is lost owing to the lack of awareness about certain fatal symptoms. Our approach here is to design a platform which caters to facilitate the aforesaid deficits. ‘Sadhyati’ shall serve as an E-community, which serves these deficits particularly in places where they lack. An easy to access and user friendly system that shall serve as the first step in terms of medical assistance. A platform for doctors as well as patients to interact irrespective of the socio-economic barriers that tends to exist.

Symptom Checker, , E-community, Dynamic Database

[1]D Biswas, S Bairagi, N Panse, N Shinde, Disease Diagnosis System, International Journal of Computer Science and Informatics, Volume-I, Issue II, 2011
[2] E Rich, K Knight, S B Nair, Artificial Intelligence, The McGraw-Hill Companies, Third Edition.
[3] T J Ross, Fuzzy Logic with Engineering Applications, Willey, Third Edition.
[4] D K Pratihar, Soft Computing Fundamentals and Application, Alpha Science.
[5] S.Chatterjee, K Gupta, A Comparative Study of E-Commerce: Review, International Journal Of Computer Science And Engineering, Volume:4, Special Issue:6.

Sumanta Chatterjee, Aniket Santra, Bishal Chakraborty, Nirmal Chandra Saha , "‘SADHYATI’: An Integrated Platform for Medical Assistance," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.243-246, 2017.

The notion of traditional public-key encryption by keyword search does not hold any hidden structure between the public-key encryption by keyword search cipher-texts; respectively, its semantic security is just defined for the keywords. We are concerned in provision of efficient search performance devoid of sacrificing semantic security within public-key encryption by keyword search. In our work we introduce searchable public-key cipher-texts by hidden structures in support of keyword search as fast as feasible devoid of sacrificing semantic security regarding encrypted keywords. Our structure is inspired by quite a lot of interesting observations based on the mechanisms of on Identity-Based Key Encapsulation. In the proposed system, the entire keyword-searchable cipher-texts that are structured by hidden relations, and by search trapdoor that corresponds to a keyword, minimum data of relations is revealed to a search algorithm as management to discover the entire matching cipher-texts resourcefully.

Public-key encryption by keyword search, Semantic security

[1] Ducas L.: “Anonymity from Asymmetry: New Constructions for Anonymous HIBE” In: Pieprzyk J. (ed.) CT-RSA 2010. LNCS, vol. 5985, pp. 148-164. Springer, Heidelberg (2010).
[2] Abdalla M., Catalano D., Fiore D.: “Verifiable Random Functions: Relations to Identity-Based Key Encapsulation and New Constructions. Journal of Cryptology”, 27(3), pp. 544-593 (2013)
[3] Freire E.S.V., Hofheinz D., Paterson K.G., Striecks C.: “Programmable Hash Functions in the Multilinear Setting”. In: Canetti R., Garay J.A. (eds.) Advances in Cryptology - CRYPTO 2013. LNCS, vol. 8042, pp. 513-530. Springer, Heidelberg (2013)
[4] Bellovin S. M., Cheswick W.R.: “Privacy-Enhanced Searches Using Encrypted Bloom Filters. Cryptography” ePrint Archive, Report 2004/022 (2004)
[5] Agrawal R., Kiernan J., Srikant R., Xu Y.: “Order Preserving Encryption for Numeric Data”. In: Proceedings of the 2004 ACM SIGMOD international conference on Management of data, pp. 563-574. ACM (2004)
[6] Chang Y.-C., Mitzenmacher M.: “Privacy Preserving Keyword Searches on Remote Encrypted Data”. In: Ioannidis J., Keromytis A. and Yung M. (eds.) ACNS 2005. LNCS, vol. 3531, pp. 442-455. Springer, Heidelberg (2005).

Adarana Thallapally, P. Poojitha, Sridhar Manda, "Design a Model in Alleviating Search Time in Large-Scale Database Retrieval," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.247-249, 2017.

Big data is huge amount of data which is beyond the processing capacity of conventional data base systems to manage and analyze the data in a specific time interval. The data is too big to store and processed by a single machine. New innovative methods are necessary to process and store large volumes of data. This paper endows with overview of big data, its size, nature, 12Vs of Big data and some technologies to handle it.

Big Data, Hadoop, Map Reduce, YARN

[1] R. Gupta,S. Gupta, A singhal," Big Data : Overview", International Journal of Computer Trends and Technology, Vol 9, Issue 5, pp. 266-268, 2014.
[2] S. Sagiroglu, D. Sinanc, "Big data: A review", In the Proceedings of the 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, pp. 42-47, 2013.
[3] S. Sathyamoorthy, "Data Mining and Information Security in Big Data", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.86-91, 2017.
[4] Cuzzocrea, I. Song, K.C. Davis "Analytics over Large-Scale Multidimensional Data: The Big Data Revolution", In the Proceedings of the ACM International Workshop on Data Warehousing and OLAP, pp. 101-104, 2011.
[5] Palaghat Yaswanth Sai, Pabolu Harika, "Illustration of IOT with Big Data Analytics", International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.221-223, 2017.
[6] Raju Din, Prabadevi B. , "Data Analyzing using Big Data (Hadoop) in Billing System", International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.84-88, 2017.
[7] V.K. Vavilapalli, A.C. Murthy, Ch. Douglas, S. Agarwal, M. Konar, R. Evans, T. Graves, J. Lowe, H. Shah, S. Seth, B. Saha, C. Curino, Owen O`Malley, S.Radia, B. Reed, and E. Baldeschwieler, "Apache Hadoop YARN: yet another resource negotiator", In Proceedings of the 4th annual Symposium on Cloud Computing (SOCC `13). ACM, New York, NY, USA, , Article 5 , pp.16, 2013.
[8] P. Sharma, V. Garg, R, Kaur, S, Sonare, " Big Data in Cloude Environment" International Journal of Computer Sciences and Engineering, Vol 1, Issue 3, pp.15-17, 2013.
[9] Prakash Singh , "Efficient Deep Learning for Big Data: A Review", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.6, pp.36-41, 2016.
[10] M. Bhanu Sridhar, A. Koushik, “A Study of Big Data Analytics in Clouds with a Security Perspective”, International Journal of Engineering Research and Technology, Vol 6, Issue 1, pp.5-9, January 2017.

M.A. Srinuvasu, A. Koushik, E.B. Santhosh, "Big Data: Challenges and Solutions," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.250-255, 2017.

Word Sense Disambiguation (WSD) is a challenging problem of Natural Language Processing (NLP). Though there are lots of algorithms for WSD available, still little work is carried out for choosing optimal algorithm for that. The job of word sense disambiguation is to decide the accurate meaning of an ambiguous term in a particular circumstance. When WSD is used in machine translation, an accurate translation in the resultant linguistic must be determined for an ambiguous term entry in the original language. Therefore Word Sense Disambiguation remains one of the most common real life problems that are associated to natural language processing which needs to be resolved efficiently.Unsupervised techniques use online dictionary for learning, and supervised techniques use manual learning sets. As there are some advantages and disadvantages of supervised learning and unsupervised learning, aim of this paper is to disambiguate the ambiguous word by using the hybrid approach for WSD. We have made use of parallel corpus and aligned the text by using GIZA.

WSD, Machine Translation, Corpus, Supervised, Unsupervised

[1] A.R. Pal and D. Saha, “Word Sense Disambiguation: A Survey”, International Journal of Control Theory and Computer Modeling (IJCTCM), Vol.5, No.3, pp. 1-16, 2015.
[2] A. Kundu, A. Singh, R. Shekhar, “A Hybrid Approach to Word Sense Disambiguation Combining Supervised and Unsupervised Learning”, International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 4, No. 4, pp. 89-101, 2013.
[3] A.R. Pal, A. Munshi, D. Saha, “An Approach To Speed-Up The Word Sense Disambiguation Procedure Through Sense Filtering”, International Journal of Instrumentation and Control Systems (IJICS) Vol.3, No.4, pp. 29-41, 2013.
[4] E. Agirre & G. Rigau, "Word sense disambiguation using conceptual density", In the Proceedings of the 16th International Conference on Computational Linguistics (COLING), Copenhagen, Denmark, pp. 16-22, 1996.
[5] E. Agirre, & D. Martínez, “Learning class-to-class selectional preferences”, In the Proceedings of the Conference on Natural Language Learning, Toulouse, France, pp. 15–22, 2001.
[6] S. Banerjee & T. Pedersen, “An adaptive Lesk Algorithm for Word Sense Disambiguation Using WordNet”, In the Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing, London, UK, pp. 136-145, 2002, ISBN: 3-540-43219-1..
[7] G. Escudero, L.M`arquez and G. Rigau, “Naïve Bayes and Exemplar-based approaches to Word Sense Disambiguation Revisited”, In the Proceedings of the 14th European Conference on Artificial Intelligence, pp. 421-425, 2000.
[8] R. Navigli, “word sense disambiguation: a survey”, ACM computing surveys, 41(2), ACM press, pp. 1-69, 2009.
[9] E. Agirre and A. Soroa, “Personalizing PageRank for Word Sense Disambiguation,” In the Proceedings of the 12th Conference European Chapter of the Association for Computational Linguistics, Greece, pp. 33–41, 2009.
[10] R. Mihalcea and D.I. Moldovan, “Pattern Learning and Automatic Feature Selection for Word Sense Disambiguation”, In the Proceedings of the Second international Workshop on Evaluating Word Sense Disambiguation Systems (SENSEVAL-2), Texas, pp. 127-130, 2001.
[11] R. Navigli and P. Velardi, “Structural Semantic Interconnections: A Knowledge-Based Approach to Word Sense Disambiguation”, Ieee Transactions On Pattern Analysis And Machine Intelligence, Washington, DC, USA, Vol. 27, No. 7, 2005.
[12] X. Zhou and H. Han, “Survey of Word Sense Disambiguation Approaches”, In the Proceedings of the 18th International FLAIR Conference, American Association for Artificial Intelligence, Philadelphia, pp. 307-313, 2005.
[13] D. Chiang, “A hierarchical phrase-based model for statistical machine translation”, In Proceedings of the ACL-05, USA, pp. 263–270, 2005.
[14] M. Galley, M. Hopkins, K. Knight, and D. Marcu. “What’s in a translation rule?”, In the Proceedings of the NAACL-04, pp. 273–280, 2004.
[15] K. Philipp, et.al, “Moses: Open source toolkit for statistical machine translation”, In the Proceedings of the ACL, Demonstration Session, pp. USA, 177–180. 2007.
[16] F. Och and H. Ney, “A systematic comparison of various statistical alignment models”, International Journal of Computational Linguistics, USA, Vol. 29, Issue 1, pp. 19–51, 2003.

S.G. Rawat, M.B. Chandak, N.A. Chavan, "An Approach for Improving Accuracy of Machine Translation using WSD and GIZA," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.256-259, 2017.