A Comprehensive Study of Deep Learning Architectures, Applications and Tools

Ganatra, Nilay; Patel, Atul

doi:10.26438/ijcse/v6i12.701705

Cited by 13 publications

(10 citation statements)

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“…Deep learning is an algorithm based on the principle of machine learning [14], and it has been widely used in various forecasting and sequence modeling tasks [15][16][17][18][19][20]. According to various evaluation criteria, recurrent neural networks (RNNs), a type of deep learning models, are fairly suitable for analyzing sessionbased customer behavior data.…”

Section: Methodsmentioning

confidence: 99%

Inter-Purchase Time Prediction Based on Deep Learning

Kao¹,

Chiu²,

Lin³

et al. 2022

Computer Systems Science and Engineering

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Inter-purchase time is a critical factor for predicting customer churn. Improving the prediction accuracy can exploit consumer's preference and allow businesses to learn about product or pricing plan weak points, operation issues, as well as customer expectations to proactively reduce reasons for churn. Although remarkable progress has been made, classic statistical models are difficult to capture behavioral characteristics in transaction data because transaction data are dependent and short-, medium-, and long-term data are likely to interfere with each other sequentially. Different from literature, this study proposed a hybrid inter-purchase time prediction model for customers of on-line retailers. Moreover, the analysis of differences in the purchase behavior of customers has been particularly highlighted. The integrated self-organizing map and Recurrent Neural Network technique is proposed to not only address the problem of purchase behavior but also improve the prediction accuracy of inter-purchase time. The permutation importance method was used to identify crucial variables in the prediction model and to interpret customer purchase behavior. The performance of the proposed method is evaluated by comparing the prediction with the results of three competing approaches on the transaction data provided by a leading e-retailer in Taiwan. This study provides a valuable reference for marketing professionals to better understand and develop strategies to attract customers to shorten their inter-purchase times.

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Section: Methodsmentioning

confidence: 99%

Inter-Purchase Time Prediction Based on Deep Learning

Kao¹,

Chiu²,

Lin³

et al. 2022

Computer Systems Science and Engineering

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“…Deep learning is the key instrument in the artificial intelligence for the application like image classification [18]. The goal of image classification is to classify a specific image from set of possible categories [19].…”

Section: Various Pre-trained Benchmark Cnn Models Used For Tumor Clasmentioning

confidence: 99%

Development of a Convolutional Neural Network Model for Brain Abnormality Classification

Barad¹,

Patel²

2020

INDJCSE

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“…Deep learning is a method falls in the wider family of machine learning algorithms that works on the principle of learning (Ganatra & Patel, 2018). Recently, it has been successfully applied to a variety of prediction and sequence modeling tasks (Mikolov et al, 2010;Sutskever et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Prediction of remaining time on site for e‐commerce users: A SOM and long short‐term memory study

Kao

Chiu

Wang

et al. 2021

Journal of Forecasting

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With the development of information technology, online transactions and e-commerce are gradually replacing conventional consumption patterns. To obtain a competitive advantage, industries proactively engage in digital transformations and the management of e-commerce platforms. Faced with changes in market patterns, e-commerce channels and online advertising firms hope to extend users' website browsing duration/time on site to enhance the effects of product promotion and the likelihood of advertisement clicks. The greatest challenge in predicting time on site is that clickstream data are not mutually independent, and short-, mid-, and long-term data may intervene in a time series. Such timing dependence increases difficulty of capturing or learning the characteristics of website users for ordinary prediction models and leads to confusion and deviation during model construction. Accordingly, this study proposed a prediction method integrating self-organizing map (SOM) and long short-term memory (LSTM). The SOM method was initially applied to categorize website members into groups based on similarities in browsing behavior, and the LSTM prediction model was subsequently developed using the webpage browsing data of each group. The performance of the proposed method is evaluated by comparing the prediction with the results of three competing approaches (SOM with support vector regression, SOM with multilayer perceptron, and single LSTM) on the clickstream data provided by a leading online retailer specializing in selling skin care and cosmetics products in Taiwan. The Wilcoxon signed-rank test validated the proposed SOM-LSTM model outperforms competing approaches in remaining time-on-site prediction. This study serves as a first attempt to systematically predict remaining time on site for e-commerce users in terms of empirically verifying a hybrid approach which integrates SOM and LSTM techniques.

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A Comprehensive Study of Deep Learning Architectures, Applications and Tools

Cited by 13 publications

References 0 publications

Inter-Purchase Time Prediction Based on Deep Learning

Inter-Purchase Time Prediction Based on Deep Learning

Development of a Convolutional Neural Network Model for Brain Abnormality Classification

Prediction of remaining time on site for e‐commerce users: A SOM and long short‐term memory study

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