...) increasingly turned to data-driven deep learning for effective FDC. Kim et al. (2019) proposed a self-attentive convolutional network (CNN) with attention on temporal order to concentrate on fault-relevant intervals while suppressing noise. Complementing this framework, Fan et al. (2023...

...Yukun Yang; Wanxin Qiao; Yuting Lin; Rui Liu Purpose Leveraging deep learning models to detect the depressive tendencies of social media users and visualizing the trajectories of their emotional changes. Addressing the issues of subjectivity bias and high implementation cost existing...

...Yang Liu; Jiahao Huang; Fei Huang; Zhen Zhu; Lili Ma Purpose This research employs innovative deep learning techniques to perform sentiment analysis on airline customer feedback, systematically investigating service quality dimensions and customer satisfaction determinants through the theoretical...

.../methodology/approach Drawing on the Elaboration Likelihood Model (ELM), we develop a theoretical framework to examine how the complexity of textual and visual content in online reviews, the consistency between them and the visual quality of images affect their usefulness. We employ several deep learning...

... 2024 09 12 2024 © Emerald Publishing Limited 2025 Emerald Publishing Limited Licensed re-use rights only Deep learning Dual adversarial architecture Generative adversarial network Natural language generation In the last decade, the field of Natural Language...

... to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS). Design/methodology/approach Leveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward...

... 2023 30 08 2023 22 09 2023 25 09 2023 © Emerald Publishing Limited 2023 Emerald Publishing Limited Licensed re-use rights only Deep learning Recommendation system Matrix factorization Long short-term memory In this information explosion (i.e. info-plosion) era...

... using deep learning models. Subsequently, inferential analysis was established with ordinary least squares regression. Further machine learning algorithms, like support vector regression, k-nearest neighbour, decision tree and random forest, are used to analyze the significance and robustness...

... 12 2022 18 01 2023 25 01 2023 © Emerald Publishing Limited 2023 Emerald Publishing Limited Licensed re-use rights only Deep learning Credit risk prediction Interpolation Missing data in irregular time-series Supply chain Credit risk prediction (CRP) refers...

... predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method. ML offers a relevant and effective tool for forecasting and supporting decision-making in health care...

...Yi-Cheng Chen Purpose Recently, more and more attention has been put forth on the application and deep learning, due to the widespread practicability of neural network computation. The purpose of this paper is developing an effective algorithm to automatically discover the optimal neural network...

... deep learning algorithm and manual coding to operationalize image and text aesthetics. Findings The empirical results indicate that text aesthetics has a U-shaped relationship with bounce rate, whereas the relationship between image aesthetics and bounce rate is insignificant. Moreover, the U...

... Deep learning Mobility pattern POI recommendation Recurrent neural network Long short-term memory With the expansion of mobile communication technology, social networks have been absorbed into the daily lives of the public. One technique directly related to our daily lives is location-based...

.... The purpose of this paper is to forecast container throughput using deep learning methods and benchmark its performance over other traditional time-series methods. Design/methodology/approach In this study, long short-term memory (LSTM) networks are implemented to forecast container throughput...

... model, such as high efficiency, strong interpretability, which is suitable for industrial production environment. Supply chain Deep learning Gradient boosting machine Sale forecast Highlights: The structure of our model is shown in Figure 1 . It is mainly divided into two parts: data...