%0 Thesis %1 k2016novel %A K, Veena K %A Patil, Dr.Basavaraj S %C IRJCS,AM Publications %D 2016 %E IRJCS, %K %T A NOVEL APPROACH TO INVESTIGATE THE USE OF HYBRID DATA OVER REAL TIME DATA %U http://www.irjcs.com/volumes/Vol3/iss011/04.NVYCS10085.pdf %X The main aim of the proposed study is to develop a hybrid temporal model that provides learning pattern for classifying the temporal data. These results are unusual, which is in contrast with the Hidden Markov Models (HMM). The system is evaluated in terms of the capabilities of a hybrid learning algorithm, which is applied over the temporal data. Performance of the hybrid algorithm depends entirely on the dynamic data, which is fed into the system. The data fitting is an important concern, to find, analyse and predict the future instance. Hence, the difficulty in making a hybrid algorithm to fit the dynamic data is increasing, however, the data fits in better proportion over the expert system. An expensive research is required to build the required module for data pre-processing, analyzing and prediction. Also comparing such systems’ performance with the conventional schemes is required to prove its effectiveness. The study aims at developing a most generic artificial neural network hybrid algorithm, which predicts well the stock market data without the knowledge of past outputs. Hence, the end user does not trouble the recognition system and that is regarded as the virtues of soft computing tools %@ 2393-9842

@phdthesis{k2016novel, abstract = {The main aim of the proposed study is to develop a hybrid temporal model that provides learning pattern for classifying the temporal data. These results are unusual, which is in contrast with the Hidden Markov Models (HMM). The system is evaluated in terms of the capabilities of a hybrid learning algorithm, which is applied over the temporal data. Performance of the hybrid algorithm depends entirely on the dynamic data, which is fed into the system. The data fitting is an important concern, to find, analyse and predict the future instance. Hence, the difficulty in making a hybrid algorithm to fit the dynamic data is increasing, however, the data fits in better proportion over the expert system. An expensive research is required to build the required module for data pre-processing, analyzing and prediction. Also comparing such systems’ performance with the conventional schemes is required to prove its effectiveness. The study aims at developing a most generic artificial neural network hybrid algorithm, which predicts well the stock market data without the knowledge of past outputs. Hence, the end user does not trouble the recognition system and that is regarded as the virtues of soft computing tools}, added-at = {2017-07-24T15:50:21.000+0200}, address = {IRJCS,AM Publications}, author = {K, Veena K and Patil, Dr.Basavaraj S}, biburl = {https://www.bibsonomy.org/bibtex/226967177e4a86dfc164d11d4b63a2948/irjcs}, editor = {IRJCS}, interhash = {75056f1f1c9b320c787091267f108ccd}, intrahash = {26967177e4a86dfc164d11d4b63a2948}, isbn = {2393-9842}, keywords = {}, language = {English}, month = {November 2016}, note = {A NOVEL APPROACH TO INVESTIGATE THE USE OF HYBRID DATA OVER REAL TIME DATA Veena K K Research Scholar, VTU,RRC,Belgavi, Karnataka Veena.bichagal@gmail.com Dr.Basavaraj S Patil Professor, Department of Information Science & Engineering, AMC Engineering College, Bengaluru, Karnataka Chief Data Scientist, Predictive Research Pvt Ltd., Bengaluru Manuscript History Number: IRJCS/RS/Vol.03/Issue11/NVCS10085 Received: 05, September 2016 Final Correction: 20, October 2016 Final Accepted: 02, November 2016 Published: November 2016 Copyright: ©2016 This is an open access article distributed under the terms of the Creative Commons Attribution License, Which Permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Abstract: The main aim of the proposed study is to develop a hybrid temporal model that provides learning pattern for classifying the temporal data. These results are unusual, which is in contrast with the Hidden Markov Models (HMM). The system is evaluated in terms of the capabilities of a hybrid learning algorithm, which is applied over the temporal data. Performance of the hybrid algorithm depends entirely on the dynamic data, which is fed into the system. The data fitting is an important concern, to find, analyse and predict the future instance. Hence, the difficulty in making a hybrid algorithm to fit the dynamic data is increasing, however, the data fits in better proportion over the expert system. An expensive research is required to build the required module for data pre-processing, analyzing and prediction. Also comparing such systems’ performance with the conventional schemes is required to prove its effectiveness. The study aims at developing a most generic artificial neural network hybrid algorithm, which predicts well the stock market data without the knowledge of past outputs. Hence, the end user does not trouble the recognition system and that is regarded as the virtues of soft computing tools Keywords: Hybrid Approach, real time data, hybrid temporal model, learning algorithm, stock market data}, school = {VTU,RRC,Belgavi, Karnataka}, timestamp = {2017-07-24T15:50:21.000+0200}, title = {A NOVEL APPROACH TO INVESTIGATE THE USE OF HYBRID DATA OVER REAL TIME DATA}, type = {research report}, url = {http://www.irjcs.com/volumes/Vol3/iss011/04.NVYCS10085.pdf}, year = 2016 }