A survey on deep learning and its applications

Dong, Shi; Wang, Ping; Abbas, Khushnood

doi:10.1016/j.cosrev.2021.100379

Cited by 862 publications

(374 citation statements)

References 91 publications

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“…For this reason, new and innovative techniques must be applied in order to perform sufficient analysis of the accumulated data. Deep Learning is a part of machine learning (ML) methods based on the usage of artificial neural networks with representation learning (supervised, semi-supervised or unsupervised learning) [16].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Layers which are situated between the input and output layers constitute the hidden layer and accordingly the nodes which constitute this layer are known as hidden nodes. In contrary with traditional machine learning classifiers where the user must write complex hypothesis, in deep neural network applications the hypothesis is generated by the network itself, making it a powerful tool for learning nonlinear relationships effectively [16].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The most widely spread SL methods include logistic regression, support vector machine (SVM) and Gaussian mixture models [17][18][19][20][21][22][23][24][25][26]. SL main disadvantage is that it cannot handle complex real-world problems such as voice and image recognition [16]. On the contrary DL specializes in solving problems such as image classification, voice recognition etc.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks

Ιωάννου¹,

Myronidis

2021

Sustainability

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The number of solar photovoltaic (PV) arrays in Greece has increased rapidly during the recent years. As a result, there is an increasing need for high quality updated information regarding the status of PV farms. This information includes the number of PV farms, power capacity and the energy generated. However, access to this data is obsolete, mainly due to the fact that there is a difficulty tracking PV investment status (from licensing to investment completion and energy production). This article presents a novel approach, which uses free access high resolution satellite imagery and a deep learning algorithm (a convolutional neural network—CNN) for the automatic detection of PV farms. Furthermore, in an effort to create an algorithm capable of generalizing better, all the current locations with installed PV farms (data provided from the Greek Energy Regulator Authority) in the Greek Territory (131,957 km2) were used. According to our knowledge this is the first time such an algorithm is used in order to determine the existence of PV farms and the results showed satisfying accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks

Ιωάννου¹,

Myronidis

2021

Sustainability

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“…While VA attempts to visually represent a dataset with the aims of potentially obtaining some insights, DVA covers the more time-consuming tasks of formulating, refining, and validating theories about the phenomenon underlying the results. Usually, it consists of two major parts such as 1) data visualization which is an emerging field in the current situation [50,152], 2) DL which adds more insights, excels at knowledge communication, and discovering strategies by applying encoding techniques to transfer abstract data into meaningful representation [30]. Figure 1 shows an interactive clinical prediction visualization system, whereas DL brings an extra power to predict clinical risks.…”

Section: Deep Visual Analyticsmentioning

confidence: 99%

Deep Visual Analytics (DVA): Applications, Challenges and Future Directions

Islam¹,

Akter²,

Ratan³

et al. 2021

HCIS

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Visual interactive system (VIS) has been received significant attention for solving various complex problems. However, designing and implementing a novel VIS with the large scale of data is a challenging task. While existing studies have applied various visual analytics (VA) to analyze and visualize insightful information, deep visual analytics (DVA) have considered as a promising technique to provide input evidences and explain system results. In this study, we present several deep learning (DL) techniques for analyzing data with visualization, which summarizes the state-of-the-art review on (i) big data analysis, (ii) cognitive and perception science, (iii) customer behavior analysis, (iv) natural language processing, (v) recommended system, (vi) healthcare analysis, (vii) fintech ecosystem, and (viii) tourism management. We present open research challenges for emerging DVA in the visualization community. We also highlight some key themes from the existing literature that may help to explore for future study. Thus, our goal is to help readers and researchers in DL and VA to understand key aspects in designing VIS for analysing data.

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“…Deep learning is mainly realized by neural network, which is an extensive, parallel, and interconnected network composed of adaptive simple units. Its structure can simulate the interaction of biological neural system to real world objects [24][25][26][27][28]. e deep learning algorithm used in this paper is U-net [29].…”

Section: Introductionmentioning

confidence: 99%

A Micro-Doppler Frequency Ambiguity Resolution Method Based on Complex-Valued U-Net

Long¹,

Yang²,

Xia³

et al. 2021

Mathematical Problems in Engineering

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In order to resolute the micro-Doppler frequency ambiguity caused by radar pulse repetition frequency not high enough (i.e., pulse dimension does not satisfy the requirement of Nyquist sampling theorem), this paper presents a micro-Doppler frequency ambiguity resolution method based on complex-valued U-net. The echo sequence is interpolated by zeros in the pulse dimension to increase the equivalent pulse repetition frequency, so that the echo sequence after zero interpolation contains the real micro-Doppler frequency; at the same time, some new frequency components are generated. The variation law of the echo sequence frequency after zero interpolation is analyzed. Then, the echo sequence in time domain after zero interpolation is transformed to the time-frequency domain by short-time Fourier transform (STFT). Finally, the time-frequency results can be segmented by the model, which is trained by complex-valued U-net to eliminate the redundant frequencies generated by zero interpolation; thus, the reconstruction of real micro-Doppler frequency is realized. Theoretical analysis and simulation results show that the proposed method can solve the problem of micro-Doppler frequency ambiguity. Compared with fully convolution network (FCN) and fully convolution residual network (FCRN), the proposed method has better performance and robustness.

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A survey on deep learning and its applications

Cited by 862 publications

References 91 publications

Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks

Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks

Deep Visual Analytics (DVA): Applications, Challenges and Future Directions

A Micro-Doppler Frequency Ambiguity Resolution Method Based on Complex-Valued U-Net

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