Volume-7 Issue-11


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Volume-7 Issue-11, August 2018, ISSN: 2278-3075 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd.

Page No.

1.

Authors:

Akshada Bhanushali, Pravin Rahate

Paper Title:

An Efficient Approach to Reduce Text Dimension for Precise Text Classification for Big Data

Abstract: In today’s society, few famous news websites such as Google and sina server gives information for users. But recently with the continuous development of information technology, the quantity of disorder data is increasing in volume. Text classification and organization has become a task. The traditional manual classification of news text not only consumes a lot of human and financial resources, but classification is also not achieved quickly. This paper makes a research about the news text classification. A news text classification model is proposed based on Latent Dirichlet Allocation (LDA) and Domain Word Filtering. The model reduces the features dimension of the news text effectively and gets good classification results. This model uses topic model to reduce text dimension and get good features as the dimension of the news texts is too high.

Keywords: Topic Model, LDA, Domain Word Filtering, News Website, Text Classification

References:

  1. Zhenzhong Li “News text classification model based on topic model” in Communication University of china Beijing, china, 2016.
  2. -H. Liao, P.-H. Chu, and P.-Y. Hsiao, “Data mining techniques and applications–a decade review from 2000 to 2011,” Expert Systems with Applications, 2012
  3. Kumaran and J. Allan, “Text classification and named entities for new event detection,” in SIGIR, 2004.
  4. Liu, B. Huet “Heterogeneous features and model selection for event- based media classification,” in ICMR, 2013.
  5. R. Kender and M. R. Naphade, “Visual concepts for news story tracking: Analyzing and exploiting the nist trecvid video annotation experiment.” in CVPR, 2005.
  6. M. Blei, A.Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” JMLR, vol.3, pp. 993-1022, Mar. 2003
  7. Wenqian, D. Hongbin, Z. Haibin, and W. Yongbin, “A novel feature weight algorithm for text categorization,” in Proceedings of the International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE ’08), pp. 1–7, Beijing, China, October 2008.
  8. King, P. Lam, and M. Roberts, “Computer-assisted keyword and document set discovery from unstructured text” (2014)
  9. Padhy, D. Mishra, R. Panigrahi et al., “The survey of data mining applications and feature scope,” (2012)

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2.

Authors:

Phan Thi Ha, Phuong Nguyen

Paper Title:

Classification of Telephone Subscriber Errors Based on Text Messages in Vietnamese Language

Abstract: This article describes a method for automatically classifying telecommunications subscriber errors based on text messages, using a machine learning method Support Vector Machine (SVM). The SVM method trains and tests on a set of data obtained from the text messages in Vietnamese of the actual line workers sending to the service operation centers. The results show that the proposed classification method using the SVM gives high accuracy and can be applied in practice.

Keywords: Text Classification, Natural Language Processing, Learning Support Vector Machine.

References:

  1. Nguyễn Thị Thùy Linh, The classification of Web pages regardless of the languages, undergraduate thesis of Hanoi National University of Technology, 2006
  2. Phan Thị Hà, Hà Hải Nam: Automatic main text extraction from web pages – Journal of Science and Technology, Vietnam Academy of Science, vol. 51, no. 1, 2013.
  3. Tran Vu Pham, “Dynamic Profile Representation and Matching in Distributed Scientific Networks”, Journal of Science and Technology Development, Vol. 14, No. K2, 2011.
  4. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?: sentiment classification using machine learning techniques,” Proceedings of the Conference on Empirical methods in natural language processing, 2002.
  5. Martineau and T. Finin, “Delta tfidf: An improved feature space for sentiment analysis,” in Proceedings of the AAAI International Conference on Weblogs and Social Media, 2009.
  6. Aman and S. Szpakowicz, “Using roget's thesaurus for fine-grained emotion recognition,” in Proceedings of the Third International Joint Conference on Natural Language Processing, 2008, pp. 296-302.
  7. M. Kim, A. Valitutti, and R. A. Calvo, “Evaluation of unsupervised emotion models to textual affect recognition,” Proceedings of the NAACL HLT Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, 2010, pp. 62-70.
  8. Kozareva, B. Navarro, S. V¡zquez, and A. Montoyo, “Uazbsa: a headline emotion classification through web information,” in Proceedings of the 4th International Workshop on Semantic Evaluations, 2007, pp. 334-337.
  9. https://www.openhub.net/p/vntokenizer ,2018.
  10. http://www.cs.cornell.edu/people/tj/svm_light/svm_multiclass.html, 2018.
  11. https://github.com/stopwords/vietnamese-stopwords/blob/master/ vietnamese-stopwords.txt,2017

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3.

Authors:

Puspanjali Mohapatra, Reeva Mishra, Tapas Kumar Patra

Paper Title:

A Jaya Algorithm Trained Neural Network for Stock Market Prediction

Abstract: This paper demonstrates how the two types of FLANN models (Functional link artificial neural network models) i.e. Chebyshev-FLANN (CFLANN) and Trigonometric-FLANN (TFLANN) are trained using Jaya algorithm to predict the Stock Market Indices. The intention of the current paper is putting forward a contrast between popular training algorithms such as Back Propagation (BP) and Jaya algorithm. The BP and Jaya algorithm trained FLANN models are examined for predicting stock indices for a day and a week ahead. The stock indices BSE500, DJIA and NASDAQ with few technical indicators are taken as inputs in this experimental time series data. The study confirms the superiority of Jaya algorithm trained FLANN models to the traditional BP trained FLANN models. The Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) are used for performance evaluation. The simulation study is done using python3 in Anaconda environment.

Keywords: Stock Market Prediction, BP, Jaya algorithm, MAPE, MSE.

References:

  1. Tang, Him, K. C. Chun, and Lei Xu. "Finite mixture of ARMA-GARCH model for stock price prediction." Proceedings of the Third International Workshop on Computational Intelligence in Economics and Finance (CIEF'2003), North Carolina, USA. 2003.
  2. Qin, A. Kai, Vicky Ling Huang, and Ponnuthurai N. Suganthan. "Differential evolution algorithm with strategy adaptation for global numerical optimization." IEEE transactions on Evolutionary Computation2 (2009): 398-417.
  3. Dorigo, Marco, and Mauro Birattari. "Ant colony optimization." Encyclopedia of machine learning. Springer, Boston, MA, 2011. 36-39.
  4. Kennedy, James. "Particle swarm optimization." Encyclopedia of machine learning. Springer US, 2011. 760-766.
  5. Jang, J-SR, and C-T. Sun. "Functional equivalence between radial basis function networks and fuzzy inference systems." IEEE transactions on Neural Networks1 (1993): 156-159.
  6. Guresen, Erkam, Gulgun Kayakutlu, and Tugrul U. Daim. "Using artificial neural network models in stock market index prediction." Expert Systems with Applications8 (2011): 10389-10397.
  7. Chen, Yuehui, Xiaohui Dong, and Yaou Zhao. "Stock index modeling using EDA based local linear wavelet neural network." Neural Networks and Brain, 2005. ICNN&B'05. International Conference on. Vol. 3. IEEE, 2005.
  8. Kim, Hyun-jung, and Kyung-shik Shin. "A hybrid approach based on neural networks and genetic algorithms for detecting temporal patterns in stock markets." Applied Soft Computing2 (2007): 569-576.
  9. Mohapatra C. and Ron S., “Flann based model to predict stock price movements of stock indices," Ph.D. dissertation, 2007.
  10. Bebarta D. K., Biswal B., and Dash P. K., “Polynomial based functional link artificial recurrent neural network adaptive system for predicting indian stocks," International Journal of Computational Intelligence Systems, vol. 8, no. 6, pp. 1004{1016, 2015.
  11. Angeline P. J., Evolutionary optimization versus particle swarm optimization: Phi-losophy and performance di erences," in International Conference on Evolutionary Programming. Springer, 1998, pp. 601{610.
  12. Jothi G., Inbarani H. H., Azar A. T., and Devi K. R. ,”Rough set theory with jaya optimization for acute lymphoblastic leukemia classification," Neural Computing and Applications, pp. 1{20, 2018.
  13. Mohapatra, Puspanjali, Alok Raj, and Tapas Kumar Patra. "Indian stock market prediction using differential evolutionary neural network model." International Journal of Electronics Communication and Computer Technology (IJECCT) Volume2 (2012).
  14. Rao R., “Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems," International Journal of Industrial En-gineering Computations, vol. 7, no. 1, pp. 19{34, 2016.
  15. Rao R., More K., Taler J., and Oclon P.,”Dimensional optimization of a micro-channel heat sink using jaya algorithm," Applied Thermal Engineering, vol. 103, pp. 572{582, 2016.
  16. Benala T. R., Chinnababu K., Mall R., and Dehuri S., “A particle swarm optimized functional link artificial neural network (pso- ann) in software cost estimation," in Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). Springer, 2013, pp. 59-66.

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