A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Karpagam, K.
- Enhanced Association Rule Mining Algorithm to Extract High Utility Itemsets from a Large Dataset
Authors
1 H.H. The Rajah's College (Autonomous), Pudukkottai, Tamil Nadu, IN
2 Dept. of Computer Applications, Karpaga Vinayaga College of Engineering and Technology, Kancheepuram, Tamil Nadu, IN
Source
Data Mining and Knowledge Engineering, Vol 7, No 7 (2015), Pagination: 238-241Abstract
Data mining aims at bringing out the hidden information from a large data set using data mining techniques according to the requirements. Association rule mining identifies itemsets that occur frequently in data set and frames association rules by taking all items equally. But many differences exist among the items that play a vital role in decision making. By taking one or more values of items as utilities, the utility mining technique works on finding the itemsets with greater utilities. In the proposed paper we present a utility mining algorithm named IUM (Improved Utility Mining) algorithm that finds high utility itemsets and also low utility itemsets from a large data set and the experiments states that the proposed algorithm performs better than existing algorithms in case of running time.Keywords
Association Rules, Frequent Itemsets, Low Utility Itemset, High Utility Itemset.- An Intelligent Conversation Agent for Health Care Domain
Authors
1 Department of Computer Applications, Dr. Mahalingam College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, Institute of Road and Transport Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 4, No 3 (2014), Pagination: 772-776Abstract
Human Computer Interaction is one of the pervasive application areas of computer science to develop with multimodal interaction for information sharings. The conversation agent acts as the major core area for developing interfaces between a system and user with applied AI for proper responses. In this paper, the interactive system plays a vital role in improving knowledge in the domain of health through the intelligent interface between machine and human with text and speech. The primary aim is to enrich the knowledge and help the user in the domain of health using conversation agent to offer immediate response with human companion feel.Keywords
Artificial Intelligence, Question Answering, Conversational Agent, HCI, Pattern Matching, Speech Synthesis.- Antimicrobial Studies on Selected Medicinal Plants ,i>(Coleus amboinicus, Phyla nodiflora and Vitex negundo)
Authors
1 Department of Biotechnology, Bharathidasan University College (w), Orathanadu, Thanjavur, Tamilnadu, IN
2 Department of Microbiology, Meenakshi Chandrasekaran College of Arts and Science, Pattukkottai – 614 626, Thanjavur, Tamilnadu, IN
3 Department of Biochemistry, Meenakshi Chandrasekaran College of Arts and Science, Pattukkottai – 614 626, Thanjavur, Tamilnadu, IN
Source
Asian Journal of Pharmacy and Technology, Vol 1, No 2 (2011), Pagination: 53-55Abstract
Medicinal plants contribute in human health care system. Most of the plants utilizes by village people as a folk medicine. Now we are turned in to medicinal plant analysis of active compounds and conservation aspect. In the present study we had select the three important medicinal plants. We have collected three medicinally important medicinal plants such as Coleus amboinicus, Phyla nodiflora and Vitex negundo for antimicrobial studies. The experiment carried out in the selected medicinal plants leaves. The results are discussed with the available literature.Keywords
Antimicrobial Activity, Pathogens, Coleus amboinicus, Phyla Nodiflora and Vitex Negundo.- A Hybrid Optimization Technique for Effective Document Clustering in Question Answering System
Authors
1 Department of Master of Computer Applications, Dr. Mahalingam College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, Institute of Road and Transport Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 7, No 3 (2017), Pagination: 1447-1451Abstract
Today, the information is growing enormously and it is difficult and tedious task to retrieve the necessary information from that pool. The main area for retrieving relevant answers is called intelligent information retrieval. To achieve this, question and answering system is used. This question and answering plays a major role in user query processing, information retrieval and extracting related information from the information pool. Recently, number of optimization algorithms is introduced to obtain the accurate and better results. Genetic Algorithm and Cuckoo Search are nature inspired meta-heuristic optimization algorithms. In this paper, combination of Genetic Algorithm with Cuckoo Search is applied to the question and answering system. The proposed algorithm is tested with the Amazon review, Trip Advisor and 20 news group data sets. The results are compared with Genetic Algorithm and Cuckoo Search algorithms.Keywords
Document Clustering, Cuckoo Search, Genetic Algorithm, Information Retrieval, Question and Answering.References
- John H. Holland, “Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence”, MIT Press, 1975.
- Xin-She Yang and Suash Deb, “Cuckoo Search via Levy Flights”, Proceedings of World Congress on Nature and Biologically Inspired Computing, pp. 210-214, 2009.
- Abdessamad Echihabi, Ulf Hermjakob, Eduard Hovy, Daniel Marcu, Eric Melz and Deepak Ravichandran, “How to Select Answer String”, Available at: http://www.isi.edu/naturallanguage/people/hovy/papers/05QAbook-answer-stringselect.pdf.
- J. Jeon, W. Croft and J. Lee, “Finding Semantically Similar Questions based on Their Answers”, Proceedings of 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 617-618, 2005.
- P. Pathak, M. Gordon and W. Fan, “Effective Information Retrieval using Genetic Algorithms based Matching Functions Adaption”, Proceedings of 33rd Hawaii International Conference on System Sciences, pp. 1-8, 2000.
- Xin-She Yang and Suash Deb, “Engineering Optimization by Cuckoo Search”, International Journal of Mathematical Modeling and Numerical Optimization, Vol. 1, No. 4, pp. 117, 2010.
- Pinar Civicioglu and Erkan Besdok, “A Conceptual Comparison of the Cuckoo-Search, Particle Swarm Optimization, Differential Evolution and Artificial Bee Colony Algorithms”, Artificial Intelligent Reviews, Vol. 39, No. 4, pp. 315-346, 2011.
- Iztok Fister Jr., Xin-She Yang, Iztok Fister, Janez Brest and Dusan Fister, “A Brief Review of Nature-Inspired Algorithms for Optimization”, Elektrotehni Ski Vestnik, Vol.80, No. 3, pp. 1-7, 2013.
- M. Bhuvaneswari, S. Hariraman, B. Anantharaj and N. Balaji, “Nature Inspired Algorithms: A Review”, International Journal of Emerging Technology in Computer Science and Electronics, Vol. 12, No. 1, pp. 21-28, 2014.
- Nitisha Gupta and Sharad Sharma, “Nature-Inspired Techniques for Optimization: A Brief Review”, International Journal of Advance Research in Science and Engineering, Vol. 5, No. 5, pp. 36-44, 2016.
- Mansaf Alam and Kishwar Sadaf, “Web Search Result Clustering based on Cuckoo Search and Consensus Clustering”, Indian Journal of Science and Technology, Vol. 9, No. 15, pp. 1-18, 2016.
- J. Sethilnath, V. Das, S.N. Omkar and V.Maniv , “Clustering using Levy flight cuckoo search”, Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications, pp. 65-75, 2012.
- R.G. Babu Kartik and P. Dhavachelvan, “Hybrid Algorithm by the advantage of ACO and Cuckoo Search for Job Scheduling”, International Journal of Information Technology Convergence and Services, Vol. 2, No. 4, pp. 25-34, 2012.
- Satyendra Singh, Jitendra Kurmi and Sudanshu Prakash Tiwari, “A Hybrid Genetic and Cuckoo Search Algorithm for Job Scheduling”, International Journal of Scientific and Research Publications, Vol. 5, No. 6, pp. 1-4, 2015.
- Oleksandr Kolomiyets and Marie-Francine Moens, “A Survey on Question Answering Technology from an information Retrieval Perspective”, Information Sciences, Vol. 181, No. 24, pp. 5412-5434, 2011.
- Gunnar Schroder, Maik Thiele and Wolfgang Lehne, “Setting Goals and Choosing Metrics for Recommender System Evaluations”, Proceedings of 5th ACM Conference on Dresden University of Technology Recommender Systems, pp. 78-85, 2011
- Iman Khodadi and Mohammad Saniee Abadeh, “Genetic Programming-based feature Learning for Question Answering”, Information Processing and Management, Vol. 52, No. 2, pp. 340-357, 2016.
- Comparative Analysis of Optimization Algorithms for Document Clustering
Authors
1 Department of Master of Computer Application, Dr. Mahalingam College of Engineering & Technology, Pollachi, IN
2 Department of Computer science and Engineering, Institute of Road and Transport Technology, Erode., IN
Source
Data Mining and Knowledge Engineering, Vol 9, No 6 (2017), Pagination: 120-125Abstract
Document clustering or text clustering is an unsupervised technique and it is used to grouping the documents of same context. Document clustering algorithms are widely used in web searching engines to produce results relevant to a query. Today, the information in websites is growing in huge size and it leads to the process of managing, retrieve the required and updated information is a tedious task. Also necessary to obtain the exact information required by the user from the documents. Recently optimization algorithms are introduced and are applied to the clustering algorithms. The Genetic Algorithm and Cuckoo Search algorithms are meta-heuristic optimization algorithms and are used to obtain the optimum solutions. In this paper, Genetic Algorithm and Cuckoo Search algorithm based Domain-specific Keyword Similarity based Knowledgebase Creation algorithm are proposed to optimize the document clustering to answers the question answering system. The experimental were conducted on benchmark datasets and the performance was analyzed in terms of Precision, Recall, F1, Missrate, Fallout and Purity.
Keywords
Cuckoo Search, Document Clustering, Genetic Algorithm, Information Processing Knowledge Base, Text Mining.References
- J.H. Holland, Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, 1975.
- Y.Xin-She, S.Deb, “Cuckoo search via lévy flights”, World Congress on Nature & Biologically Inspired Computing, NaBIC, , 2009,pp. 210–214.
- X.S.Yang and S.Deb, "Engineering Optimization by Cuckoo Search", J. Mathematical Modeling and Numerical Optimization, vol. 1, no. 4, 2010.
- K.Karpagam and A.Saradha, ”An Improved Question Answering System Using Domain Context Specific Document Clustering with Wordnet”, International Journal of Printing, Packaging & Allied Sciences, 2016, Volume 4, No. 5, Pages 3257 -3265
- H.Yang, T.Chua, S.Wang, C.Koh, ”Structured use of external knowledge for event- based open domain question answering“, In Proceedings of the annual international ACM SIGIR conference on research and development in information retrieval ACM, 2003, pp. 33–40.
- J.Jeon, W.Croft, and J.Lee, “Finding semantically similar questions based on their answers”, In Proceedings of the annual international ACM SIGIR conference on research and development in information retrieval, 2005.
- K.Iman, S.A.Mohammad, “Genetic programming-based feature learning for question answering”, Elsevier- Information Processing and Management, 2016.
- T.Ming, D.S.Cicero, X.Bing and Z. Bowen, Improved Representation Learning for Question Answer Matching”,, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, August, 2016,pp. 7-12.
- P.Pathak, M.Gordon, and W.Fan, “Effective information retrieval using genetic algorithms based matching functions adaption,” in Proc.33rd Hawaii International Conference on Science (HICS), Hawaii, USA, 2000.
- E.Abdessamad, H.Ulf, H.Eduard, M.Daniel, M.Eric, and R.Deepak, “How to Select Answer String”, Springer Netherlands, 2006.
- A.Mansaf, S.Kishwar, Web Search Result Clustering based on Cuckoo Search and Consensus Clustering”, Indian Journal of science and Technology,Volume 9, Issue 15, April, 2016.
- C.Cobos, H.M.Collazos, R.U.Munoz, M. Medoza, E.Leon and E.H.Veidema, “Clustering of web search results based on cuckoo search algorithm and balanced Bayesian information criterion”, Information Sciences,.2014,pp. :248- 264.
- J. Sethilnath, V. Das, S.N. Omkar, and V. Maniv, “Clustering using Levy flight cuckoo search”, Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories andApplications,BIC-TA, 2012.
- S.Liu, F.Liu, C.Yu, and W. Meng, “An effective approach to document retrieval via utilizing WordNet and recognizing phrases", In Proceedings of the annual international ACM SIGIR conference on research and development in information retrieval (pp.266–272), ACM, 2004.
- Voorhess, Ellen, Graff, and David,” AQUAINT-2 information retrieval text research collection LDC2002T25, Web Download. Philadelphia, Linguistic data consortium 2008.
- M.Saeedeh, K.Dietrich, “Bridging the vocabulary gap between questions and answer sentences”, Elsevier- Information Processing and Management, 2015.
- S.Gunnar et al, “Setting Goals and Choosing Metrics for Recommender System Evaluations”, 5th ACM Conference on Dresden University of Technology Recommender Systems,Chicago, 2011.
- Heie, H. Matthias, Whittaker, W.D.Edward and S.Furui, “Question answering using statistical language modeling”,Computer Speech and Language, 26, , 2012 pp. 193–209.
- http://qwone.com/~jason/20Newsgroups/
- Graff, David, “The AQUAINT corpus of English News Text”, LDC2002T31, Web Download. Philadelphia, Linguistic data consortium, 2002.
- Deep Learning Approaches for Answer Selection in Question Answering System for Conversation Agents
Authors
1 Department of Computer Applications, Dr. Mahalingam College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, Institute of Road and Transport Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 10, No 2 (2020), Pagination: 2040-2044Abstract
The conversation agent acts as core interfaces between a system and user in answering users queries with proper responses. Question answering system acquires an important role in the information retrieval field. The deep learning approach enhances the accuracy in answering complex questions. As outcome, the user is receiving the precise answer instead of large document collections. The aim of this paper is to develop a model with deep learning approach for improving answer selection process which supports more relevant answer displaying by conversation agents. To achieve this, word2vector used for word representation and biLSTM attentive model is used for training, testing and disclosure play precise answer. Question type is identified using POS-tagger based Question Pattern analysis (T-QPA) model. The knowledgebase is created from the bench mark datasets bAbI Facebook (simple QA tasks), TREC QA, Yahoo! Answer, Insurance QA dataset. The proposed framework is built by embedding of questions and answers based on bidirectional long short-term memory (biLSTM) attentive models. The similarity between questions and answers has been measured by semantic and cosine similarity. The proposed model reduces the search gap in extracting among user queries and answer sentences in the education domain. The system results are evaluated with the standard metrics MAP, Top 1 accuracy, F1- Score for the answer selection.Keywords
Deep Learning, Question Answering System, Attentive Model, Conversation Agents, Cosine Similarity.References
- Yashvardhan Sharma and Sahil Guptaa, “Deep Learning Approaches for Question Answering System”, Proceedings of International Conference on Computational Intelligence and Data Science, Vol. 132, pp. 785-794, 2018.
- James O. Shea and Zuhair Bandar Keeley, “Systems Engineering and Conversation Agents”, Master Thesis, School of Computing, Manchester Metropolitan University, pp. 1-232, 2011.
- G.O. Sing, K.W. Wong, C.C. Fung and A. Depickere, “Towards a more Natural and Intelligent Interface with Embodied Conversation Agent”, Proceedings of International Conference on Game Research and Development, pp. 177-183, 2006.
- Chatbots, “Alice (Artificial Linguistic Internet Computer Entity)”, Available at: chatbots.org/chatbot/a.l.i.c.e/
- M. Tan, C.Dos Santos, B. Xiang and B. Zhou, “Improved Representation Learning for Question Answer Matching”, Proceedings of 54th Annual Meeting of the Association for Computational Linguistics, pp. 464-473, 2016.
- X. Li and D. Roth, “Learning Question Classifiers”, Proceedings of 19th International Conference on Computational Linguistics, pp. 1-7, 2002.
- The Babi Project, Available at: https://research.fb.com/downloads/babi/
- Antoine Bordes, Nicolas Usunier, Sumit Chopra and Jason Weston, “Large-Scale Simple Question Answering with Memory Networks”, Proceedings of International Conference on Computation and Language, pp. 1-10, 2015.
- Analytics Vidhya, “Essentials of Deep Learning: Introduction to Long Short Term Memory”, Available at: https://www.analyticsvidhya.com/blog/2017/12/fundamentals-of-deep-learning-introduction-to-lstm/
- M. Feng, B. Xiang, M.R. Glass, L. Wang and B. Zhou, “Applying Deep Learning to Answer Selection: A Study and An Open Task”, Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 1-10, 2015.
- T.L. Lai, T. Bui and S. Li, “A Review on Deep Learning Techniques Applied to Answer Selection”, Proceedings of 27th International Conference on Computational Linguistics, pp. 2132-2144, 2018.
- Ming Tan, Bing Xiang and Bowen Zhou, “LSTM-based Deep Learning Models for Non-Factoid Answer Selection”, Proceedings of International Conference on Computational Language and Machine Learning, pp. 1-7, 2016.
- Tom Young, Devamanyu Hazarika, Soujanya Poria and Erik Cambria, “Recent Trends in Deep Learning based Natural Language Processing”, IEEE Computational Intelligence Magazine, Vol. 13, No. 3, pp. 55-75, 2017.
- Lei Yu, Karl Moritz Hermann, Phil Blunsom and Stephen G. Pulman, “Deep Learning for Answer Sentence Selection”, Proceedings of International Workshop on Deep Learning, pp. 1-9, 2014.
- K. Karpagam and A. Saradha, “A Framework For Intelligent Question Answering System using Semantic Context Specific Document Clustering and Wordnet”, Sadhana-Academy Proceedings in Engineering Sciences, Vol. 44, No. 3, pp. 1-10, 2019.
- K. O. Shea, “An Approach to Conversational Agent Design using Semantic Sentence Similarity”, Applied Intelligence, Vol. 37, No. 4, pp. 558-568, 2012.
- Ming-Wei Chang, Lev Ratinov, Dan Roth and Vivek Srikumar, “Importance of Semantic Representation: Data less Classification”, Proceedings of 23rd AAAI Conference on Artificial Intelligence, pp. 830-835, 2008.
- Ellen M Voorhees, “The TREC Question Answering Track”, Journal of Natural Language Engineering, Vol. 7, No. 4, pp. 361-378, 2001.