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3rd International Conference on Electromechanical Control Technology and Transportation, 2018 Convolutional neural networks are designed to work with grid-structured inputs, which have strong spatial dependencies in local regions of the grid. The most obvious example of grid-structured data is a 2-dimensional image. This type of data also exhibits spatial dependencies, because adjacent spatial locations in an image often have similar color ...
Mathew Salvaris +2 more
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Convolution-Bidirectional Temporal Convolutional Network for Protein Secondary Structure Prediction
IEEE Access, 2022 As a basic feature extraction method, convolutional neural networks have some information loss problems when dealing with sequence problems, and a temporal convolutional network can compensate for this problem.
Yunqing Zhang, Yuming Ma, Yihui Liu
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Pansharpening by Convolutional Neural Networks [PDF]
Remote Sensing, 2016 A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem. Moreover, to improve performance without increasing complexity, we augment the input by including several maps of nonlinear radiometric indices ...
MASI, GIUSEPPE +3 more
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Improved Convolutional Neural Image Recognition Algorithm based on LeNet-5
Journal of Computer Networks and Communications, 2022 Convolutional neural network (CNN) is a very important method in deep learning, which solves many complex pattern recognition problems. Fruitful results have been achieved in image recognition, speech recognition, and natural language processing ...
Lijie Zhou, Weihai Yu
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Texture synthesis of ecological plant protection image based on convolution neural network
Frontiers in Plant Science, 2022 Texture synthesis technology is an important realistic rendering technology. Texture synthesis technology also has a good application prospect in image rendering and other fields. Convolutional neural network is a very popular technology in recent years.
Libing Hu, Fei Zhou, Xianjun Fu
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Method for predicting cutter remaining life based on multi-scale cyclic convolutional network
International Journal of Distributed Sensor Networks, 2022 In the process of predicting the remaining cutter life, the deep-learning method such as convolutional neural network does not consider the time correlation of different degradation states, which directly affects the accuracy of the remaining cutter life
Tao Li +5 more
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Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm
IEEE Access, 2020 As an algorithm with excellent performance, convolutional neural network has been widely used in the field of image processing and achieved good results by relying on its own local receptive fields, weight sharing, pooling, and sparse connections.
Youhui Tian
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Forecast Model of TV Show Rating Based on Convolutional Neural Network
Complexity, 2021 The TV show rating analysis and prediction system can collect and transmit information more quickly and quickly upload the information to the database. The convolutional neural network is a multilayer neural network structure that simulates the operating
Lingfeng Wang
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Speech Command Recognition using Artificial Neural Networks
JOIV: International Journal on Informatics Visualization, 2020 Speech is one of the most effective way for human and machine to interact. This project aims to build Speech Command Recognition System that is capable of predicting the predefined speech commands. Dataset provided by Google’s TensorFlow and AIY teams is
Sushan Poudel, Dr. R Anuradha
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Systemic risk prediction based on Savitzky-Golay smoothing and temporal convolutional networks
Electronic Research Archive, 2023 Based on the data from January 2007 to December 2021, this paper selects 14 representatives from four levels of the extreme risk of financial institutions, the contagion effect between financial systems, volatility and instability of financial markets ...
Xite Yang +4 more
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