Results 51 to 60 of about 370,356 (314)
Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †
Energies, 2018 Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech ...
Gregory D. Merkel +2 more
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Deep Petri nets of unsupervised and supervised learning
Measurement + Control, 2020 Artificial intelligence is one of the hottest research topics in computer science. In general, when it comes to the needs to perform deep learning, the most intuitive and unique implementation method is to use neural network.
Yi-Nan Lin +5 more
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Design of an Intelligent Educational Evaluation System Using Deep Learning
IEEE Access, 2023 Nowadays, online education has been a more general demand in context of COVID-19 epidemic. The intelligent educational evaluation systems assisted by intelligent techniques are in urgent demand.
Yan Pei, Genshu Lu
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Creation of synthetic contrast-enhanced computed tomography images using deep neural networks to screen for renal cell carcinoma [PDF]
, 2023 In this study, we elucidate if synthetic contrast enhanced computed tomography images created from plain computed tomography images using deep neural networks could be used for screening, clinical diagnosis, and postoperative follow-up of small-diameter ...
Takahashi, Tomoichi +14 more
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Deep Neural Networks and PIDE Discretizations
SIAM Journal on Mathematics of Data Science, 2022 In this paper, we propose neural networks that tackle the problems of stability and field-of-view of a Convolutional Neural Network (CNN). As an alternative to increasing the network's depth or width to improve performance, we propose integral-based spatially nonlocal operators which are related to global weighted Laplacian, fractional Laplacian and ...
Bastian Bohn +2 more
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Implicit Saliency In Deep Neural Networks [PDF]
2020 IEEE International Conference on Image Processing (ICIP), 2020 In this paper, we show that existing recognition and localization deep architectures, that have not been exposed to eye tracking data or any saliency datasets, are capable of predicting the human visual saliency. We term this as implicit saliency in deep neural networks.
Yutong Sun +2 more
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Ambient backscatter communication-based smart 5G IoT network
EURASIP Journal on Wireless Communications and Networking, 2021 In this paper, we propose an ambient backscatter communication-based smart 5G IoT network. The network consists of two parts, namely a real-time data transmission system based on ambient backscatter communication and a real-time big data analysis system ...
Qiang Liu +3 more
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Development and Validation of a Nuclear Power Plant Fault Diagnosis System Based on Deep Learning
Energies, 2022 As artificial intelligence technology has progressed, numerous businesses have used intelligent diagnostic technology. This study developed a deep LSTM neural network for a nuclear power plant to defect diagnostics.
Bing Liu +3 more
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Impact of Disentanglement on Pruning Neural Networks [PDF]
, 2023 Efficient model compression techniques are required to deploy deep neural networks (DNNs) on edge devices for task specific objectives. A variational autoencoder (VAE) framework is combined with a pruning criterion to investigate the impact of having the
SINHA, Nilotpal +5 more
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Deep Morphological Neural Networks
CoRR, 2019 Mathematical morphology is a theory and technique to collect features like geometric and topological structures in digital images. Given a target image, determining suitable morphological operations and structuring elements is a cumbersome and time-consuming task.
Yucong Shen +2 more
openaire +2 more sources