...Yang Liu; Lining Han Purpose The COVID-19 pandemic has imposed unprecedented strain on healthcare systems worldwide, largely due to significant and unpredictable fluctuations in patients' length of stay (LOS) during hospitalization. Interpretable deep learning techniques have emerged as promising...

...Meimanat Dadras; Ali Adibiyan; Mahdi Kherad Purpose The purpose of this study is to develop a hybrid approach for document image quality assessment ( DIQA ) that effectively integrates traditional handcrafted features with deep learning-based features to improve accuracy, robustness...

... satellite images and on-site water level measurements collected at the An Khe and Ka Nak Reservoir, Gia Lai, Vietnam, spanning January 2019 to December 2022. Experimental results demonstrate that the HOG-GRU variant significantly outperforms conventional deep learning models. The specific evaluation metrics...

...Rona Nisa Sofia Amriza; Khairun Nisa Meiah Ngafidin Purpose This research aims to develop a robust deep-learning approach for classifying emotion in social media. Design/methodology/approach This study integrates three deep learning techniques: Bidirectional Gated Recurrent Units (BiGRU...

... and the service layer. The data layer focuses on the perception and collection of major emergency information. The analysis layer includes key components such as causality recognition, causality extraction, event fusion and generalization. In this layer, we developed several deep learning (DL)-based models using...

... assessments conducted by pathologists by integrating AI technologies, particularly deep learning. By accurately computing the percentage of Ki-67-labeled cells, the research aims to streamline the diagnostic process, reduce subjectivity and contribute to the advancement of diagnostic precision in pathological...

... performance of learners without integrating learning patterns identification techniques. Design/methodology/approach This study proposes a new framework for identifying learning patterns and predicting learning performance. Two modules, the learning patterns identification module and the deep learning...

... structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified...

...-consuming enterprises and provide crucial information for global carbon emission reduction. Design/methodology/approach The proposed coal price forecasting system combines data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. It addresses the challenge of merging...

... Emerald Publishing Limited Licensed re-use rights only Data mining Logistic tender named entity recognition Bidding e-platforms Deep learning Lattice-LSTM BERT National Natural Science Foundation of China 10.13039/501100001809 No. 72174086 With the success of supply...

... neural network models mentioned in this paper. Design/methodology/approach This study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore...

... clustering and statistical inference methods, deep learning techniques for community detection have grown in popularity due to their ease of processing high-dimensional network data. Graph convolutional neural networks (GCNNs) have received much attention recently and have developed into a potential...

...Akhil Kumar Purpose This work aims to present a deep learning model for face mask detection in surveillance environments such as automatic teller machines (ATMs), banks, etc. to identify persons wearing face masks. In surveillance environments, complete visibility of the face area is a guideline...

... models (Bhandari et al., 2019) include artificial neural networks (ANN), support vector machines (SVM), random forests (RF), and gradient boosting decision trees (GBDT) (Friedman, 2001). In recent years, a newer form of machine learning called deep learning has become a trend (Pandey and Janghel...

... challenge in email categorization. The purpose of this paper is to examine the effectiveness of the pre-trained embedding model for the classification of emails using deep learning classifiers such as the long short-term memory (LSTM) model and convolutional neural network (CNN) model. Design/methodology...

..., indicating that the reconstruction errors can be utilized to select suspected taxpayers for an audit in a cost-effective manner. Originality/value The proposed deep learning-based approach is expected to be applied in a real-world tax administration, promoting voluntary compliance of taxpayers...

... Publishing Limited Licensed re-use rights only Neural network Deep learning Sales forecast Precision marketing strategy XGBOOST Integration model External factors are external stimuli that consumers receive, and they are not determined by the consumer or the online stores (Steinker...

... workmanship, such as exterior walls and floor tiling correspond to the visual attributes of a house, and it is difficult to capture and evaluate such attributes efficiently through classical models like regression analysis. Deep learning approach is taken in the valuation process to utilize this visual...

... methods. Originality/value The e-mail phishing detection is performed in this paper using the optimization-based deep learning networks. The e-mails include a number of unwanted messages that are to be detected in order to avoid the storage issues. The importance of the method is that the inclusion...

..., the authors propose a deep learning approach based on gated recurrent units for automatic patent classification built on the trained word embeddings. Findings Experiments on a standardized evaluation data set show that the approach increases average precision for patent classification by 17 percent...