A comparative review on deep learning models for text classification

Zulqarnain, Muhammad; Ghazali, Rozaida; Hassim, Yana Mazwin Mohmad; Rehan, Muhammad

doi:10.11591/ijeecs.v19.i1.pp325-335

Cited by 55 publications

(37 citation statements)

References 19 publications

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“…On the other hand, the network model's complexity and weight numbers are diminished. Figure 2 shows the main structure of CNN that contains mainly five layers; the input layer, convolution layer with activation function, pooling layer, fully connected layer, and finally the softmax layer [12][13][14][15].…”

Section: Cnn Artechitctermentioning

confidence: 99%

Detection of citrus leaf diseases using a deep learning technique

Luaibi

Salman

Miry

2021

IJECE

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The food security major threats are the diseases affected in plants such as citrus so that the identification in an earlier time is very important. Convenient malady recognition can assist the client with responding immediately and sketch for some guarded activities. This recognition can be completed without a human by utilizing plant leaf pictures. There are many methods employed for the classification and detection in machine learning (ML) models, but the combination of increasing advances in computer vision appears the deep learning (DL) area research to achieve a great potential in terms of increasing accuracy. In this paper, two ways of conventional neural networks are used named Alex Net and Res Net models with and without data augmentation involves the process of creating new data points by manipulating the original data. This process increases the number of training images in DL without the need to add new photos, it will appropriate in the case of small datasets. A self-dataset of 200 images of diseases and healthy citrus leaves are collected. The trained models with data augmentation give the best results with 95.83% and 97.92% for Res Net and Alex Net respectively.

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Section: Cnn Artechitctermentioning

confidence: 99%

Detection of citrus leaf diseases using a deep learning technique

Luaibi

Salman

Miry

2021

IJECE

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“…The max-pooling layer, in CNN, performs on data to compress and make it smooth. While for selecting the maximum value of the responsive area the Max-layer is used which produces data-invariant small translational changes ( Zulqarnain, Ghazali & Hassim, 2019 ). A fully connected layer is used as a final layer of CNN which produces the output by connecting all neurons in the forward and backward manner.…”

Section: Deep Learning Approachesmentioning

confidence: 99%

A comparative analysis on question classification task based on deep learning approaches

Zulqarnain

Alsaedi

Ghazali

et al. 2021

PeerJ Computer Science

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Question classification is one of the essential tasks for automatic question answering implementation in natural language processing (NLP). Recently, there have been several text-mining issues such as text classification, document categorization, web mining, sentiment analysis, and spam filtering that have been successfully achieved by deep learning approaches. In this study, we illustrated and investigated our work on certain deep learning approaches for question classification tasks in an extremely inflected Turkish language. In this study, we trained and tested the deep learning architectures on the questions dataset in Turkish. In addition to this, we used three main deep learning approaches (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN)) and we also applied two different deep learning combinations of CNN-GRU and CNN-LSTM architectures. Furthermore, we applied the Word2vec technique with both skip-gram and CBOW methods for word embedding with various vector sizes on a large corpus composed of user questions. By comparing analysis, we conducted an experiment on deep learning architectures based on test and 10-cross fold validation accuracy. Experiment results were obtained to illustrate the effectiveness of various Word2vec techniques that have a considerable impact on the accuracy rate using different deep learning approaches. We attained an accuracy of 93.7% by using these techniques on the question dataset.

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“…It works on data to compresses and makes smooth data. Max-layer selects the maximum value of the receptive field and produces data invariant to small translational changes [22]. Consequently," generate three CNNs layers to manage various data prediction due to their variances in sizes.…”

Section: Fig1 a Schematic Presentation Of Cnnmentioning

confidence: 99%

Predicting Financial Prices of Stock Market using Recurrent Convolutional Neural Networks

Zulqarnain¹,

Ghazali²,

Ghouse³

et al. 2020

IJISA

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Financial time-series prediction has been long and the most challenging issues in financial market analysis. The deep neural networks is one of the excellent data mining approach has received great attention by researchers in several areas of time-series prediction since last 10 years. “Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for financial predictions. In this paper, we proposed to combine architectures, which exploit the advantages of CNN and RNN simultaneously, for the prediction of trading signals. Our model is essentially presented to financial time series predicting signals through a CNN layer, and directly fed into a gated recurrent unit (GRU) layer to capture long-term signals dependencies. GRU model perform better in sequential learning tasks and solve the vanishing gradients and exploding issue in standard RNNs. We evaluate our model on three datasets for stock indexes of the Hang Seng Indexes (HSI), the Deutscher Aktienindex (DAX) and the S&P 500 Index range 2008 to 2016, and associate the GRU-CNN based approaches with the existing deep learning models. Experimental results present that the proposed GRU-CNN model obtained the best prediction accuracy 56.2% on HIS dataset, 56.1% on DAX dataset and 56.3% on S&P500 dataset respectively.

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A comparative review on deep learning models for text classification

Cited by 55 publications

References 19 publications

Detection of citrus leaf diseases using a deep learning technique

Detection of citrus leaf diseases using a deep learning technique

A comparative analysis on question classification task based on deep learning approaches

Predicting Financial Prices of Stock Market using Recurrent Convolutional Neural Networks

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