Refine your search
Collections
Co-Authors
Journals
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
Saradha, A.
- An Intelligent Conversation Agent for Health Care Domain
Abstract Views :154 |
PDF Views:0
Authors
K. Karpagam
1,
A. Saradha
2
Affiliations
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
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.
- A Hybrid Optimization Technique for Effective Document Clustering in Question Answering System
Abstract Views :405 |
PDF Views:4
Authors
K. Karpagam
1,
A. Saradha
2
Affiliations
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
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.
- Deep Learning Approaches for Answer Selection in Question Answering System for Conversation Agents
Abstract Views :152 |
PDF Views:0
Authors
Affiliations
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
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.
- Diet Recommendation for Glycemic Patients using Improved Kmeans and Krill-Herd Optimization
Abstract Views :173 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, IN
1 Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 10, No 3 (2020), Pagination: 2096-2101Abstract
Maintaining nutrition for glycemic (diabetic) patients in order to retain the blood glucose level is one of the important activity to be followed. Stimulating the amount of carbohydrates, protein, vitamins, and minerals will result in a healthy diet. So, there is a necessity for recommendation of nutrition to those diabetic patients nowadays. Recommender Systems (RS) play a vital role in urging relevant suggestions to the users. To promote improvised and optimized results, Optimization technique plays a significant role in refining the parameters of chosen algorithm. To optimize and to upgrade the accuracy of recommendations, the system has been developed by implementing improved Krill-Herd algorithm. The system which clusters the profiles of diabetic patients using improved k-means clustering algorithm and results has been optimized using Improved Krill-Herd optimization algorithm. The performance will be analysed using different measures like Precision, Recall, F-measure, Accuracy, Matthews correlation, Fallout rate and Miss rate. The evaluation results show that the proposed system performs better and produces optimized results to the diabetic patients with minimum error rate.Keywords
Data Mining, Diabetes Patients, Recommender Systems, Clustering Algorithm, Improved K-Means, Krill Herd Optimization.References
- Y. Koren and R.M. Bell, “Advances in Collaborative Filtering”, Springer, 2011.
- Kunal Shah, “Unsupervised Recommender Systems: An Overview of Different Approaches to Recommendations”, Proceedings of IEEE International Conference on Innovations in Information, Embedded and Communication Systems, pp. 1-4, 2017.
- Xiaofeng Li, “Improved Hybrid Collaborative Filtering Algorithm Based on K-Means”, Expert Systems with Applications, Vol. 842, pp. 928-934, 2019.
- Semeh Ben Salema and Sami Naouali, “A Fast and Effective Partitional Clustering Algorithm for Large Categorical Datasets using a K-means Based Approach”, Computers and Electrical Engineering, Vol. 37, No. 9, pp. 463-483, 2018.
- J. Hu, J. Liang, Y. Kuang and V. Honavar, “A User Similarity-Based Top-N Recommendation Approach for Mobile In-Application Advertising”, Expert Systems with Applications, Vol. 111, pp. 51-60, 2018.
- Anand Khandare and A.S. Alvi, “Clustering Algorithms: Experiment and Improvements”, Computing and Network Sustainability, Vol. 25, No. 12, pp. 263-271, 2017.
- Laith Mohammad Abualigah, Ahamad Tajudin Khader, Essam Said Hanandeh, and Amir H. Gandomi, “A Novel Hybridization Strategy for Krill-Herd Algorithm Applied to Clustering Techniques”, Applied Soft Computing, Vol. 60, pp. 423-435, 2017.
- Laith Mohammad Abualigah, Ahamad Tajudin Khader and Essam Said Hanandeh, “Hybrid Clustering Analysis using Improved Krill-Herd Algorithm”, Applied Intelligence, Vol. 48, No. 11, pp. 4047-4071, 2018.
- D. Dua and E. Karra Taniskidou, “UCI Machine Learning Repository”, Available at: http://archive.ics.uci.edu/ml, Accessed on 2017.
- Methods and Application of Food Composition Laboratory, Available at: http://www.ars.usda.gov/Services/docs.htm?docid=8964.
- R.V. Rao, D.P. Rai and J. Balic, “A Multi-Objective Algorithm for Optimization of Modern Machining Processes”, Engineering Applications of Artificial Intelligence, Vol. 61, pp. 103-125, 2017.
- Divya Pandove, “A Comprehensive Study on Clustering Approaches for Big Data Mining”, Proceedings of IEEE International Conference on Electronics and Communication System, pp. 1333-1338, 2015.
- Jonathan Jeffrey, “Optimizing Web Structures using Web Mining Techniques”, Springer, 2007.
- Daniel Kluver, M.D. Ekstrand and J.A. Konstan, “Rating-Based Collaborative Filtering: Algorithms and Evaluation”, Social Information Access, Vol. 42, No. 8, pp. 344-390, 2018.
- Gunnar Schroder, Maik Thiele and Wolfgang Lehner, “Setting Goals and Choosing Metrics for Recommender System Evaluations”, Proceedings of Workshop at 5th ACM Conference on Recommender Systems, pp. 53-61, 2011.
- M.V.B.T. Santhi, “Enhancing K-Means Clustering Algorithm”, International Journal on Computer Science and Technology, Vol. 2, No. 4, pp. 235-246, 2012.
- Huanjing Wang and Jaason Van Hulse, “A Comparative Study of Threshold-based Feature Selection Techniques”, Proceedings of IEEE International Conference on Granular Computing, pp. 499-504, 2010.
- X. Chen, X. Xu, J.Z. Huang and Y. Ye, “K Means: Automated Two-Level Variable Weighting Clustering Algorithm for Multiview Data”, IEEE Transactions on Knowledge and Data Engineering, Vol. 25, No. 4, pp. 932-944, 2013.
- G.Y. Zhang, C.D. Wang and D. Huang, “K-Means: Two Level Weighted Collaborative K-Means for Multiview Clustering”, Knowledge Based Systems, Vol. 150, pp. 127-138, 2018.
- X.T. Zhang, “Multi-Task Multi-View Clustering”, IEEE Transactions on Knowledge and Data Engineering, Vol. 28, No. 4, pp. 3324-3338, 2016.
- L.M. Abualigah, A.T. Khader, E.S. Hanandeh and A.H. Gandomi, “A Novel Hybridization Strategy for Krill-Herd Algorithm Applied to Clustering Techniques”, Applications of Soft Computing, Vol. 60, pp. 423-435, 2017.
- J. Bhavithra and A. Saradha, “Personalized Web Page Recommendation using Case-Based Clustering and Weighted Association Rule Mining”, Cluster Computing, Vol. 22, No. 3, pp. 6991-7002, 2018.
- K. Renuka Devi, “Evaluation of Partitional and Hierarchical Clustering Techniques”, International Journal of Computer Science and Mobile Computing, Vol. 8, No. 11, pp. 48-54, 2019.
- K. Renuka Devi, J. Bhavithra and A. Saradha, “Personalized Nutrition Recommendation for Diabetic Patients using Improved K-Means and Krill-Herd Optimization”, International Journal of Scientific and Technology Research, Vol. 9, No. 3, pp. 1076-1083, 2020.