Results 51 to 60 of about 849,090 (345)
Long Short-Term Memory Spatial Transformer Network
, 2019 Spatial transformer network has been used in a layered form in conjunction with a convolutional network to enable the model to transform data spatially.
Chen, Tianyue, Feng, Shiyang, Sun, Hao
core +1 more source
A review of artificial intelligence in brachytherapy
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in various aspects of brachytherapy.
Jingchu Chen +4 more
wiley +1 more source
Research onconvolutional neural network for reservoir parameter prediction
Tongxin xuebao, 2016 As the branch of artificial intelligence,artificial neural network solved many difficult practical problems in pattern recognition and classification prediction field successfully.However,they cannot learn the feature from networks.In recent years,deep ...
You-xiang DUAN, Gen-tian LI, Qi-feng SUN
doaj +2 more sources
SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions
Applied Sciences, 2020 The automated optical inspection of a surface mount technology line inspects a printed circuit board for quality assurance, and subsequently classifies the chip assembly defects.
Young-Gyu Kim, Tae-Hyoung Park
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Fault Diagnosis of Rotating Machinery Based on Evolutionary Convolutional Neural Network
Shock and Vibration, 2022 This paper proposes a fault diagnosis method for rotating machinery based on evolutionary convolutional neural network (ECNN). With the time-frequency images as the network input, with the help of the global optimization ability of the genetic algorithm,
Yihao Bai +3 more
doaj +1 more source
Mesh-based graph convolutional neural networks for modeling materials with microstructure [PDF]
arXiv, 2021 Predicting the evolution of a representative sample of a material with microstructure is a fundamental problem in homogenization. In this work we propose a graph convolutional neural network that utilizes the discretized representation of the initial microstructure directly, without segmentation or clustering.
arxiv
, 2017 Traditional recommendation systems rely on past usage data in order to generate new recommendations. Those approaches fail to generate sensible recommendations for new users and items into the system due to missing information about their past ...
A Rajaraman +4 more
core +1 more source
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Current radiotherapy practices rely on manual contouring of CT scans, which is time‐consuming, prone to variability, and requires highly trained experts. There is a need for more efficient and consistent contouring methods. This study evaluated the performance of the Varian Ethos AI auto‐contouring tool to assess its potential integration into
Robert N. Finnegan +6 more
wiley +1 more source
Interpretable Convolutional Neural Networks [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 This paper proposes a method to modify traditional convolutional neural networks (CNNs) into interpretable CNNs, in order to clarify knowledge representations in high conv-layers of CNNs. In an interpretable CNN, each filter in a high conv-layer represents a certain object part.
Ying Nian Wu +2 more
openaire +3 more sources
Forest fire image recognition based on convolutional neural network
Journal of Algorithms & Computational Technology, 2019 In order to detect fire automatically, a forest fire image recognition method based on convolutional neural networks is proposed in this paper. There are two main types of fire recognition algorithms.
Yuanbin Wang, Langfei Dang, Jieying Ren
doaj +1 more source