Rock Classification from Field Image Patches Analyzed Using a Deep Convolutional Neural Network

Ran, Xiangjin; Liu, Xue; Zhang, Yanyan; Liu, Zeyu; Sang, Xuejia; He, Jun

doi:10.3390/math7080755

Cited by 66 publications

(24 citation statements)

References 30 publications

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“…where W indicates the convolution kernels. Recurrent convolutional neural network (RCNN) can be heaped to establish a deep architecture, called the deep recurrent convolutional neural network (DRCNN) [18,19]. To use the DRCNN method in the predictive task, Equation (6) determines how the last phase of the model serves as a supervised learning layer.r…”

Section: Deep Recurrent Convolution Neural Network (Drcnn)mentioning

confidence: 99%

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Deep Learning Methods for Modeling Bitcoin Price

et al. 2020

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A precise prediction of Bitcoin price is an important aspect of digital financial markets because it improves the valuation of an asset belonging to a decentralized control market. Numerous studies have studied the accuracy of models from a set of factors. Hence, previous literature shows how models for the prediction of Bitcoin suffer from poor performance capacity and, therefore, more progress is needed on predictive models, and they do not select the most significant variables. This paper presents a comparison of deep learning methodologies for forecasting Bitcoin price and, therefore, a new prediction model with the ability to estimate accurately. A sample of 29 initial factors was used, which has made possible the application of explanatory factors of different aspects related to the formation of the price of Bitcoin. To the sample under study, different methods have been applied to achieve a robust model, namely, deep recurrent convolutional neural networks, which have shown the importance of transaction costs and difficulty in Bitcoin price, among others. Our results have a great potential impact on the adequacy of asset pricing against the uncertainties derived from digital currencies, providing tools that help to achieve stability in cryptocurrency markets. Our models offer high and stable success results for a future prediction horizon, something useful for asset valuation of cryptocurrencies like Bitcoin.

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Section: Deep Recurrent Convolution Neural Network (Drcnn)mentioning

confidence: 99%

“…The variance limit follows, where one variable is significant if its variance increases concerning the rest of the variables as a whole. The Sobol method [16] is applied to decompose the variance of the total output V (Y) offered by the set of equations expressed in Equation (19).…”

Section: Sensitivity Analysismentioning

confidence: 99%

Deep Learning Methods for Modeling Bitcoin Price

et al. 2020

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“…With more and more research investigating CNN, the number of improved CNNs has increased conspicuously. They are playing an increasingly important role in various fields [50][51][52]. Deep learning is also very suitable for automatic identification of land types in the field since feature selection is not required.…”

Section: Original Intention Of Slic-cnnmentioning

confidence: 99%

Intelligent High-Resolution Geological Mapping Based on SLIC-CNN

Sang

Liu

Ran

et al. 2020

IJGI

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High-resolution geological mapping is an important supporting condition for mineral and energy exploration. However, high-resolution geological mapping work still faces many problems. At present, high-resolution geological mapping is still generated by expert interpretation of survey lines, compasses, and field data. The work in the field is constrained by the weather, terrain, and personnel, and the working methods need to be improved. This paper proposes a new method for high-resolution mapping using Unmanned Aerial Vehicle (UAV) and deep learning algorithms. This method uses the UAV to collect high-resolution remote sensing images, cooperates with some groundwork to anchor the lithology, and then completes most of the mapping work on high-resolution remote sensing images. This method transfers a large amount of field work into the room and provides an automatic mapping process based on the Simple Linear Iterative Clustering-Convolutional Neural Network (SLIC-CNN) algorithm. It uses the convolutional neural network (CNN) to identify the image content and confirms the lithologic distribution, the simple linear iterative cluster (SLIC) algorithm can be used to outline the boundary of the rock mass and determine the contact interface of the rock mass, and the mode and expert decision method is used to clarify the results of the fusion and mapping. The mapping method was applied to the Taili waterfront in Xingcheng City, Liaoning Province, China. In this study, the Area Under the Curve (AUC) of the mapping method was 0.937. The Kappa test result was k = 0.8523, and a high-resolution geological map was obtained.

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“…Recently, deep learning has been a focused area of research [25]. Wang et al [26] employed deep learning for action recognition from videos.…”

Section: Deep Learning Based Action Recognition From 2d Videosmentioning

confidence: 99%

Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification

et al. 2020

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Gestures are one of the basic modes of human communication and are usually used to represent different actions. Automatic recognition of these actions forms the basis for solving more complex problems like human behavior analysis, video surveillance, event detection, and sign language recognition, etc. Action recognition from images is a challenging task as the key information like temporal data, object trajectory, and optical flow are not available in still images. While measuring the size of different regions of the human body i.e., step size, arms span, length of the arm, forearm, and hand, etc., provides valuable clues for identification of the human actions. In this article, a framework for classification of the human actions is presented where humans are detected and localized through faster region-convolutional neural networks followed by morphological image processing techniques. Furthermore, geometric features from human blob are extracted and incorporated into the classification rules for the six human actions i.e., standing, walking, single-hand side wave, single-hand top wave, both hands side wave, and both hands top wave. The performance of the proposed technique has been evaluated using precision, recall, omission error, and commission error. The proposed technique has been comparatively analyzed in terms of overall accuracy with existing approaches showing that it performs well in contrast to its counterparts.

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Rock Classification from Field Image Patches Analyzed Using a Deep Convolutional Neural Network

Cited by 66 publications

References 30 publications

Deep Learning Methods for Modeling Bitcoin Price

Deep Learning Methods for Modeling Bitcoin Price

Intelligent High-Resolution Geological Mapping Based on SLIC-CNN

Image Analysis Using Human Body Geometry and Size Proportion Science for Action Classification

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