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Saradha, A.
- A Framework for Forecasting Wind Speed and Power Using Adaboost with Back Propagation
Abstract Views :252 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Science & Engineering, Institute of Road and Transport Technology, Erode, IN
1 Department of Computer Science & Engineering, Institute of Road and Transport Technology, Erode, IN
Source
Data Mining and Knowledge Engineering, Vol 8, No 1 (2016), Pagination: 19-23Abstract
Electricity can be generated by a variety of ways. Wind power has many characteristics which other fossil energy does not have, such as clean, intermittent and randomness. This is because the wind is a natural phenomenon. Wind energy converts into mechanical energy in the way that wind blow through fans to drive rotor rotation. The reason why the demand for wind power around the world grows involves many aspects, including the shortage of energy, change in climate, the progress of economy and technology, etc. Due to wind is intermittent and less dispatchable, wind power fluctuates as the wind fluctuating and is uncontrollable. The way to solve the problem is forecasting the wind power. The wind speed and wind power are considered as the main input to forecasting models. The new forecasting model is adaboost with Back propagation NN. The new model mainly focusing the speed and accuracy. The new algorithm adaboost with Back propagation NN will improve the accuracy of the forecasting power with the forecasting wind. This is because using the forecasting wind speed instead of the original one can reduce the error which is caused by the training data and the noise which is produced in sampling process or data transmission.Keywords
Wind Energy, Wind Power, Adaboost, Back-Propagation Nn.
- Enhancement of Intermittent Demands in Forecasting for Spare Parts Industry
Abstract Views :147 |
PDF Views:0
Authors
B. Vasumathi
1,
A. Saradha
2
Affiliations
1 Department of Computer Science and Applications, PGP College of Arts and Science, Namakkal – 637207, Tamil Nadu, IN
2 Department of Computer Science and Technology, Institute of Road and Transport Technology, Erode – 638316, Tamil Nadu, IN
1 Department of Computer Science and Applications, PGP College of Arts and Science, Namakkal – 637207, Tamil Nadu, IN
2 Department of Computer Science and Technology, Institute of Road and Transport Technology, Erode – 638316, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 8, No 25 (2015), Pagination:Abstract
The standard method to forecast intermittent demand is Croston’s Method. This method is available in ERP type solution such as SAP and specialized forecasting software packages (e.g., Forecast Pro), and often applied in practice6. In this paper, two forecasting methods, Croston’s Method and a New Method (Bisection of Croston’s Method), are compared. Two kinds of spare parts were chosen for the analysis. By using a modified Croston’s Method, we forecast the average of last two demands over a fixed lead time. The mean square errors are shown to meet the theoretical and practical requirements of intermittent demand. Based on these measures, the best statistical summary can be obtained. The out-of-sample comparison results indicate superior performance of the New Method. In addition, the results show that the mean square error is a well-behaved accuracy measures for intermittent demand. Methods/Statistical Analysis: Croston’s Method was implemented for forecasting irregular demands. Bisection Numerical method was compared with existing Croston’s Method. The results are based on the Mean Square Error Values (MSE) values. Results/Findings: Bisection Method showed MSE Values of Proposed Method is lesser than the MSE values of Existing Method. Conclusion/Application: 'Bisection Method ' can be a better method to predict the Production of Spare Parts Industries.Keywords
Bisection Method, Crostons Method, Mean Square Error, Spare PartsFull Text
- 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
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- 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.
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- 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
Abstract Views :236 |
PDF Views:3
Authors
K. Karpagam
1,
A. Saradha
2
Affiliations
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
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.
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- 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.
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- 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.
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- 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
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- 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
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