scholarly journals Classification of Ground Targets Based on Radar Micro-Doppler Signatures Using Deep Learning and Conventional Supervised Learning Methods

2018 ◽  
Vol 27 (3) ◽  
pp. 835-845
Author(s):  
Peibei Cao ◽  
Weijie Xia ◽  
Yi Li
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Learning Methods

Classification of audio codecs with variable bit-rates using deep-learning methods

2020 ◽  
pp. 102952
Author(s):  
Atieh Khodadadi ◽  
Soheila Molaei ◽  
Mehdi Teimouri ◽  
Hadi Zare
Keyword(s):  
Deep Learning ◽  
Learning Methods

scholarly journals Generative Adversarial Networks-Based Semi-Supervised Automatic Modulation Recognition for Cognitive Radio Networks

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3913 ◽  
Author(s):  
Mingxuan Li ◽  
Ou Li ◽  
Guangyi Liu ◽  
Ce Zhang
Keyword(s):  
Deep Learning ◽  
Cognitive Radio ◽  
Supervised Learning ◽  
Rapid Development ◽  
Generative Adversarial Networks ◽  
Modulation Recognition ◽  
Learning Methods ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Automatic Modulation Recognition

With the recently explosive growth of deep learning, automatic modulation recognition has undergone rapid development. Most of the newly proposed methods are dependent on large numbers of labeled samples. We are committed to using fewer labeled samples to perform automatic modulation recognition in the cognitive radio domain. Here, a semi-supervised learning method based on adversarial training is proposed which is called signal classifier generative adversarial network. Most of the prior methods based on this technology involve computer vision applications. However, we improve the existing network structure of a generative adversarial network by adding the encoder network and a signal spatial transform module, allowing our framework to address radio signal processing tasks more efficiently. These two technical improvements effectively avoid nonconvergence and mode collapse problems caused by the complexity of the radio signals. The results of simulations show that compared with well-known deep learning methods, our method improves the classification accuracy on a synthetic radio frequency dataset by 0.1% to 12%. In addition, we verify the advantages of our method in a semi-supervised scenario and obtain a significant increase in accuracy compared with traditional semi-supervised learning methods.

scholarly journals Utility of Deep Learning Methods for Referability Classification of Age-Related Macular Degeneration

2018 ◽  
Vol 136 (11) ◽  
pp. 1305 ◽  
Author(s):  
Phillippe Burlina ◽  
Neil Joshi ◽  
Katia D. Pacheco ◽  
David E. Freund ◽  
Jun Kong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Learning Methods ◽  
Age Related

scholarly journals A Systematic Review of Deep Learning Methods Applied to Ocular Images

2019 ◽  
Vol 30 (1) ◽  
pp. 9-26 ◽  
Author(s):  
Oscar Julian Perdomo Charry ◽  
Fabio Augusto González Osorio
Keyword(s):  
Deep Learning ◽  
Macular Degeneration ◽  
Clinical Signs ◽  
Age Related Macular Degeneration ◽  
Ocular Diseases ◽  
Learning Methods ◽  
Age Related ◽  
Box Models ◽  
The One

Artificial intelligence is having an important effect on different areas of medicine, and ophthalmology has not been the exception. In particular, deep learning methods have been applied successfully to the detection of clinical signs and the classification of ocular diseases. This represents a great potential to increase the number of people correctly diagnosed. In ophthalmology, deep learning methods have primarily been applied to eye fundus images and optical coherence tomography. On the one hand, these methods have achieved an outstanding performance in the detection of ocular diseases such as: diabetic retinopathy, glaucoma, diabetic macular degeneration and age-related macular degeneration.  On the other hand, several worldwide challenges have shared big eye imaging datasets with segmentation of part of the eyes, clinical signs and the ocular diagnostic performed by experts. In addition, these methods are breaking the stigma of black-box models, with the delivering of interpretable clinically information. This review provides an overview of the state-of-the-art deep learning methods used in ophthalmic images, databases and potential challenges for ocular diagnosis

scholarly journals Performance analysis of pre-trained transfer learning models for the classification of the rolling bearing faults

2021 ◽  
Vol 2070 (1) ◽  
pp. 012141
Author(s):  
Pavan Sharma ◽  
Hemant Amhia ◽  
Sunil Datt Sharma
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Manufacturing Industry ◽  
Rolling Bearing ◽  
Performance Comparison ◽  
Learning Methods ◽  
Training Time ◽  
Bearing Faults ◽  
Artificial Intelligence Techniques

Abstract Nowadays, artificial intelligence techniques are getting popular in modern industry to diagnose the rolling bearing faults (RBFs). The RBFs occur in rotating machinery and these are common in every manufacturing industry. The diagnosis of the RBFs is highly needed to reduce the financial and production losses. Therefore, various artificial intelligence techniques such as machine and deep learning have been developed to diagnose the RBFs in the rotating machines. But, the performance of these techniques has suffered due the size of the dataset. Because, Machine learning and deep learning methods based methods are suitable for the small and large datasets respectively. Deep learning methods have also been limited to large training time. In this paper, performance of the different pre-trained models for the RBFs classification has been analysed. CWRU Dataset has been used for the performance comparison.

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