scholarly journals Fatigue modeling using neural networks: A comprehensive review

2022 ◽  
Author(s):  
Jie Chen ◽  
Yongming Liu
Keyword(s):  
Neural Networks ◽  
Comprehensive Review ◽  
Fatigue Modeling

Computational Mechanisms of Pulse-Coupled Neural Networks: A Comprehensive Review

2016 ◽  
Vol 24 (3) ◽  
pp. 573-588 ◽  
Author(s):  
Kun Zhan ◽  
Jinhui Shi ◽  
Haibo Wang ◽  
Yuange Xie ◽  
Qiaoqiao Li
Keyword(s):  
Neural Networks ◽  
Comprehensive Review ◽  
Coupled Neural Networks ◽  
Pulse Coupled Neural Networks

On robust randomized neural networks for regression: a comprehensive review and evaluation

2020 ◽  
Vol 32 (22) ◽  
pp. 16931-16950
Author(s):  
Ananda L. Freire ◽  
Ajalmar R. Rocha-Neto ◽  
Guilherme A. Barreto
Keyword(s):  
Neural Networks ◽  
Comprehensive Review

scholarly journals Graph Neural Networks: Taxonomy, Advances, and Trends

2022 ◽  
Vol 13 (1) ◽  
pp. 1-54
Author(s):  
Yu Zhou ◽  
Haixia Zheng ◽  
Xin Huang ◽  
Shufeng Hao ◽  
Dengao Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real World ◽  
Research Community ◽  
Future Research ◽  
Research Directions ◽  
Comprehensive Review ◽  
Future Research Directions ◽  
Graph Neural Networks ◽  
Low Dimensional

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers cannot see a panorama of the graph neural networks. This survey aims to overcome this limitation and provide a systematic and comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 327 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the challenges faced. It is expected that more and more scholars can understand and exploit the graph neural networks and use them in their research community.

A Comprehensive Review on Nature Inspired Neural Network based Adaptive Filter for Eliminating Noise in Medical Images

2020 ◽  
Vol 16 (4) ◽  
pp. 278-287 ◽  
Author(s):  
Manish Kumar ◽  
Sudhansu Kumar Mishra
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Adaptive Filters ◽  
Medical Images ◽  
Human Beings ◽  
Comprehensive Review ◽  
Biomedical Image ◽  
Artificial Neural ◽  
Nature Inspired Algorithms

Background: Various kind of medical imaging modalities are available for providing noninvasive view and for analyzing any pathological symptoms of human beings. Different noise may appear in those modalities at the time of acquisition, transmission, scanning, or at the time of storing. The removal of noises from the digital medical images without losing any inherent features is always considered a challenging task because a successful diagnosis relies on them. Numerous techniques have been proposed to fulfill this objective, and each having their own benefits and limitations. Discussion: In this comprehensive review article, more than 65 research articles are investigated to illustrate the applications of Artificial Neural Networks (ANN) in the field of biomedical image denoising. In particular, the zest of this article is to highlight the hybridized filtering model using nature-inspired algorithms and artificial neural networks for suppression of noise. Various other techniques, such as fixed filter, linear adaptive filters and gradient descent learning based neural network filter are also included. Conclusion: This article envisages how to train ANN using derivative free nature-inspired algorithms, and its performance in various medical images modalities and noise conditions.

scholarly journals Fatigue modeling using neural networks: a comprehensive review

2021 ◽  
Author(s):  
Jie Chen ◽  
Yongming Liu
Keyword(s):  
Life Prediction ◽  
Current Trend ◽  
Future Research ◽  
Fatigue Load ◽  
Research Directions ◽  
Comprehensive Review ◽  
The Past ◽  
Wide Range ◽  
Future Research Directions ◽  
Fatigue Modeling

Neural network (NN) models have made a significant impact on fatigue-related engineering communities and are expected to increase rapidly soon due to the recent advancements in machine learning and artificial intelligence. A comprehensive review of fatigue modeling methods using NNs is lacking and will help to recognize past achievements and suggest future research directions. Thus, this paper presents a survey of 251 publications between 1990 and July 2021. The NN modeling in fatigue is classified into five applications: fatigue life prediction, fatigue crack, fatigue damage diagnosis, fatigue strength, and fatigue load. A wide range of NN architectures are employed in the literature and are summarized in this review. An overview of important considerations and current limitations for the application of NNs in fatigue is provided. Statistical analysis for the past and the current trend is provided with representative examples. Existing gaps and future research directions are also presented based on the reviewed articles.

scholarly journals Data-Driven Single Image Deraining: A Comprehensive Review and New Perspectives

2021 ◽  
Author(s):  
Zhao Zhang ◽  
Yanyan Wei ◽  
Haijun Zhang ◽  
Yi Yang ◽  
Shuicheng Yan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Network Architecture ◽  
Training Data ◽  
Data Driven ◽  
Future Research ◽  
Single Image ◽  
Comprehensive Review ◽  
Depth Analysis ◽  
Real Scenario ◽  
High Level

<div>Abstract—Due to the powerful fitting ability of neural networks and massive training data, data-driven Single Image Deraining (SID) methods have obtained significant performance, and most of the existing studies focus on improving the deraining performance by proposing different kinds of deraining networks. However, the generalization ability of current SID methods may still be limited in the real scenario, and the deraining results also cannot effectively improve the performance of subsequent high-level tasks (such as object detection). The main reason may be because the current works mainly focus on designing new deraining neural networks, while neglecting the interpretation of the solving process. To investigate these issues, we in this paper re-examine the three important factors (i.e.,data, rain model and network architecture) for the SID task, and specifically analyze them by proposing new and more reasonable criteria (i.e., general vs. specific, synthetical vs. mathematical, black-box vs. white-box). We will also study the relationship of the three factors from new perspectives of data, and reveal two different solving paradigms (explicit vs. implicit) for the SID task. Besides, we also profoundly studied one of the three factors, i.e., data, and measured the performance of current methods on different datasets via extensive experiments to reveal the effectiveness of SID data. Finally, with the above comprehensive review and in-depth analysis, we also draw some valuable conclusions and suggestions for future research.</div>

scholarly journals Data-Driven Single Image Deraining: A Comprehensive Review and New Perspectives

2021 ◽  
Author(s):  
Zhao Zhang ◽  
Yanyan Wei ◽  
Haijun Zhang ◽  
Yi Yang ◽  
Shuicheng Yan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Network Architecture ◽  
Training Data ◽  
Data Driven ◽  
Future Research ◽  
Single Image ◽  
Comprehensive Review ◽  
Depth Analysis ◽  
Real Scenario ◽  
High Level

<div>Abstract—Due to the powerful fitting ability of neural networks and massive training data, data-driven Single Image Deraining (SID) methods have obtained significant performance, and most of the existing studies focus on improving the deraining performance by proposing different kinds of deraining networks. However, the generalization ability of current SID methods may still be limited in the real scenario, and the deraining results also cannot effectively improve the performance of subsequent high-level tasks (such as object detection). The main reason may be because the current works mainly focus on designing new deraining neural networks, while neglecting the interpretation of the solving process. To investigate these issues, we in this paper re-examine the three important factors (i.e.,data, rain model and network architecture) for the SID task, and specifically analyze them by proposing new and more reasonable criteria (i.e., general vs. specific, synthetical vs. mathematical, black-box vs. white-box). We will also study the relationship of the three factors from new perspectives of data, and reveal two different solving paradigms (explicit vs. implicit) for the SID task. Besides, we also profoundly studied one of the three factors, i.e., data, and measured the performance of current methods on different datasets via extensive experiments to reveal the effectiveness of SID data. Finally, with the above comprehensive review and in-depth analysis, we also draw some valuable conclusions and suggestions for future research.</div>

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