Recommendation system based on deep learning methods: a systematic review and new directions

2019 ◽  
Vol 53 (4) ◽  
pp. 2709-2748 ◽  
Author(s):  
Aminu Da’u ◽  
Naomie Salim
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Recommendation System ◽  
Learning Methods ◽  
New Directions

scholarly journals Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 667
Author(s):  
Wei Chen ◽  
Qiang Sun ◽  
Xiaomin Chen ◽  
Gangcai Xie ◽  
Huiqun Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Deep Learning ◽  
Heart Sounds ◽  
Learning Approaches ◽  
Automated Classification ◽  
Learning Methods ◽  
Artificial Intelligence Technology ◽  
Medical Big Data ◽  
Effective Models

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

scholarly journals Learning Feature Interactions with Lorentzian Factorization Machine

2020 ◽  
Vol 34 (04) ◽  
pp. 6470-6477
Author(s):  
Canran Xu ◽  
Ming Wu
Keyword(s):  
Deep Learning ◽  
Hyperbolic Space ◽  
Recommendation System ◽  
Triangle Inequality ◽  
State Of The Art ◽  
Learning Methods ◽  
New Model ◽  
User Behaviors ◽  
Feature Interactions ◽  
Factorization Machine

Learning representations for feature interactions to model user behaviors is critical for recommendation system and click-trough rate (CTR) predictions. Recent advances in this area are empowered by deep learning methods which could learn sophisticated feature interactions and achieve the state-of-the-art result in an end-to-end manner. These approaches require large number of training parameters integrated with the low-level representations, and thus are memory and computational inefficient. In this paper, we propose a new model named “LorentzFM” that can learn feature interactions embedded in a hyperbolic space in which the violation of triangle inequality for Lorentz distances is available. To this end, the learned representation is benefited by the peculiar geometric properties of hyperbolic triangles, and result in a significant reduction in the number of parameters (20% to 80%) because all the top deep learning layers are not required. With such a lightweight architecture, LorentzFM achieves comparable and even materially better results than the deep learning methods such as DeepFM, xDeepFM and Deep & Cross in both recommendation and CTR prediction tasks.

scholarly journals Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review

2020 ◽  
Vol 122 ◽  
pp. 103801 ◽  
Author(s):  
Shenda Hong ◽  
Yuxi Zhou ◽  
Junyuan Shang ◽  
Cao Xiao ◽  
Jimeng Sun
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Learning Methods

scholarly journals Deep Learning-Based Models for Pain Recognition: A Systematic Review

2020 ◽  
Author(s):  
Rasha M. Al-Eidan ◽  
Hend Al-Khalifa ◽  
AbdulMalik Alsalman
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Data Sets ◽  
Observer Variability ◽  
Learning Models ◽  
Learning Methods ◽  
Inter Observer Variability ◽  
Recognition Systems ◽  
Pain Recognition ◽  
Open Issues

The traditional standards employed for pain assessment have many limitations. One such limitation is reliability because of inter-observer variability. Therefore, there have been many approaches to automate the task of pain recognition. Recently, deep-learning methods have appeared to solve many challenges, such as feature selection and cases with a small number of data sets. This study provides a systematic review of pain-recognition systems that are based on deep-learning models for the last two years only. Furthermore, it presents the major deep-learning methods that were used in review papers. Finally, it provides a discussion of the challenges and open issues.

scholarly journals Advancement in Precision Medicine and Recommendation System for Clinical Trials Using Deep Learning Methods

2021 ◽  
Vol 1055 (1) ◽  
pp. 012110
Author(s):  
A.P. Ponselvakumar ◽  
S Anandamurugan ◽  
K. Logeswaran ◽  
S. Nivashini ◽  
S.K. Showentharya ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Deep Learning ◽  
Precision Medicine ◽  
Recommendation System ◽  
Learning Methods

scholarly journals Deep-Learning-Based Models for Pain Recognition: A Systematic Review

2020 ◽  
Vol 10 (17) ◽  
pp. 5984
Author(s):  
Rasha M. Al-Eidan ◽  
Hend Al-Khalifa ◽  
AbdulMalik Al-Salman
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Data Sets ◽  
Observer Variability ◽  
Learning Models ◽  
Learning Methods ◽  
Inter Observer Variability ◽  
Recognition Systems ◽  
Pain Recognition ◽  
Open Issues

Traditional standards employed for pain assessment have many limitations. One such limitation is reliability linked to inter-observer variability. Therefore, there have been many approaches to automate the task of pain recognition. Recently, deep-learning methods have appeared to solve many challenges such as feature selection and cases with a small number of data sets. This study provides a systematic review of pain-recognition systems that are based on deep-learning models for the last two years. Furthermore, it presents the major deep-learning methods used in the review papers. Finally, it provides a discussion of the challenges and open issues.

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