Results 91 to 100 of about 2,004,297 (387)

Research on Fault Diagnosis of HMCVT Shift Hydraulic System Based on Optimized BPNN and CNN

Agriculture, 2023
There are some problems in the shifting process of hydraulic CVT, such as irregularity, low stability and high failure rate. In this paper, the BP neural network and convolutional neural network are used for fault diagnosis of the HMCVT hydraulic system.
Jiabo Wang, Zhixiong Lu, Guangming Wang, Ghulam Hussain, Shanhu Zhao, Haijun Zhang, Maohua Xiao   +6 more
doaj   +1 more source

LCNN: Lookup-based Convolutional Neural Network

, 2017
Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose a fast, compact, and accurate model for convolutional neural networks that enables efficient ...
Bagherinezhad, Hessam, Farhadi, Ali, Rastegari, Mohammad   +2 more
core   +1 more source

Does dose calculation algorithm affect the dosimetric accuracy of synthetic CT for MR‐only radiotherapy planning in brain tumors?

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose This study compares the dosimetric accuracy of deep‐learning‐based MR synthetic CT (sCT) in brain radiotherapy between the Analytical Anisotropic Algorithm (AAA) and AcurosXB (AXB). Additionally, it proposes a novel metric to predict the dosimetric accuracy of sCT for individual post‐surgical brain cases.
Jeffrey C. F. Lui, Francis K. H. Lee, C. C. Law, James C. H. Chow   +3 more
wiley   +1 more source

Deep neural networks for direct, featureless learning through observation: the case of 2d spin models

, 2018
We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4x4 Ising model. Using its success at this task, we motivate the study of the larger 8x8 Ising model, showing that the deep neural ...
Mills, Kyle, Tamblyn, Isaac
core   +1 more source

DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation [PDF]

Conference on Empirical Methods in Natural Language Processing, 2019
Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources.
Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, Alexander Gelbukh   +4 more
semanticscholar   +1 more source

T2‐weighted imaging of rectal cancer using a 3D fast spin echo sequence with and without deep learning reconstruction: A reader study

Journal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose To compare image quality and clinical utility of a T2‐weighted (T2W) 3‐dimensional (3D) fast spin echo (FSE) sequence using deep learning reconstruction (DLR) versus conventional reconstruction for rectal magnetic resonance imaging (MRI).
Dan Nguyen, Sarah Palmquist, Ken‐Pin Hwang, Jingfei Ma, Usama Salem, Jia Sun, Xinzeng Wang, Jong Bum Son, Randy Ernst, Peng Wei, Harmeet Kaur, Nir Stanietzky   +11 more
wiley   +1 more source

Dependency-based Convolutional Neural Networks for Sentence Embedding

, 2015
In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequentially and neglect long-distance dependencies. To exploit both deep learning
Huang, Liang, Ma, Mingbo, Xiang, Bing, Zhou, Bowen   +3 more
core   +1 more source

Convolutional Neural Network Language Models [PDF]

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016
Convolutional Neural Networks (CNNs) have shown to yield very strong results in several Computer Vision tasks. Their application to language has received much less attention, and it has mainly focused on static classification tasks, such as sentence classification for Sentiment Analysis or relation extraction.
Pham, Ngoc-Quan, Kruszewski, Germán, Boleda, Gemma   +2 more
openaire   +3 more sources

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, Richard L. J. Qiu, Tonghe Wang, Shadab Momin, Xiaofeng Yang   +4 more
wiley   +1 more source

FocusedDropout for Convolutional Neural Network

Applied Sciences, 2022
In a convolutional neural network (CNN), dropout cannot work well because dropped information is not entirely obscured in convolutional layers where features are correlated spatially. Except for randomly discarding regions or channels, many approaches try to overcome this defect by dropping influential units.
Minghui Liu, Tianshu Xie, Xuan Cheng, Jiali Deng, Meiyi Yang, Xiaomin Wang, Ming Liu   +6 more
openaire   +2 more sources