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Muhammad Zulqarnain
Universiti Tun Hussein Onn Malaysia, Johor, Malaysia
Computer Science & IT

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Journal : JOIV : International Journal on Informatics Visualization

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Efficient processing of GRU based on word embedding for text classification Muhammad Zulqarnain; Rozaida Ghazali; Muhammad Ghulam Ghouse; Muhammad Faheem Mushtaq
JOIV : International Journal on Informatics Visualization Vol 3, No 4 (2019)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1066.464 KB) | DOI: 10.30630/joiv.3.4.289

Abstract

Text classification has become very serious problem for big organization to manage the large amount of online data and has been extensively applied in the tasks of Natural Language Processing (NLP). Text classification can support users to excellently manage and exploit meaningful information require to be classified into various categories for further use. In order to best classify texts, our research efforts to develop a deep learning approach which obtains superior performance in text classification than other RNNs approaches. However, the main problem in text classification is how to enhance the classification accuracy and the sparsity of the data semantics sensitivity to context often hinders the classification performance of texts. In order to overcome the weakness, in this paper we proposed unified structure to investigate the effects of word embedding and Gated Recurrent Unit (GRU) for text classification on two benchmark datasets included (Google snippets and TREC). GRU is a well-known type of recurrent neural network (RNN), which is ability of computing sequential data over its recurrent architecture. Experimentally, the semantically connected words are commonly near to each other in embedding spaces. First, words in posts are changed into vectors via word embedding technique. Then, the words sequential in sentences are fed to GRU to extract the contextual semantics between words. The experimental results showed that proposed GRU model can effectively learn the word usage in context of texts provided training data. The quantity and quality of training data significantly affected the performance. We evaluated the performance of proposed approach with traditional recurrent approaches, RNN, MV-RNN and LSTM, the proposed approach is obtained better results on two benchmark datasets in the term of accuracy and error rate.
Neural Network Techniques for Time Series Prediction: A Review Muhammad Faheem Mushtaq; Urooj Akram; Muhammad Aamir; Haseeb Ali; Muhammad Zulqarnain
JOIV : International Journal on Informatics Visualization Vol 3, No 3 (2019)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (902.684 KB) | DOI: 10.30630/joiv.3.3.281

Abstract

It is important to predict a time series because many problems that are related to prediction such as health prediction problem, climate change prediction problem and weather prediction problem include a time component. To solve the time series prediction problem various techniques have been developed over many years to enhance the accuracy of forecasting. This paper presents a review of the prediction of physical time series applications using the neural network models. Neural Networks (NN) have appeared as an effective tool for forecasting of time series.  Moreover, to resolve the problems related to time series data, there is a need of network with single layer trainable weights that is Higher Order Neural Network (HONN) which can perform nonlinearity mapping of input-output. So, the developers are focusing on HONN that has been recently considered to develop the input representation spaces broadly. The HONN model has the ability of functional mapping which determined through some time series problems and it shows the more benefits as compared to conventional Artificial Neural Networks (ANN). The goal of this research is to present the reader awareness about HONN for physical time series prediction, to highlight some benefits and challenges using HONN.
Co-Authors Haseeb Ali Muhammad Aamir Muhammad Faheem Mushtaq Muhammad Ghulam Ghouse Rozaida Ghazali Urooj Akram