Results 41 to 50 of about 203,239 (310)
Data-Driven Bearing Fault Diagnosis for Induction Motor
Journal of Electrical and Computer Engineering, 2023 Bearings are critical components in modern manufacturing, yet they are prone to failures in induction machines. Detecting these faults early can reduce repair costs.
Aqib Raqeeb +5 more
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Journal of Education, Health and Sport, 2021 Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis.
Szymon Płotka +4 more
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Compressing Convolutional Neural Networks
CoRR, 2015 Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes increase, so do the storage and memory requirements of the classifiers.
Wenlin Chen +4 more
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Factorial Convolution Neural Networks
CoRR, 2021 In recent years, GoogleNet has garnered substantial attention as one of the base convolutional neural networks (CNNs) to extract visual features for object detection. However, it experiences challenges of contaminated deep features when concatenating elements with different properties.
Jaemo Sung, Eun-Sung Jung
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Noise-Enhanced Associative Memories [PDF]
, 2013 Recent advances in associative memory design through structured pattern sets and graph-based inference algorithms allow reliable learning and recall of exponential numbers of patterns.
Amir Hesam Salavati +7 more
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, 2022 Rectal or colorectal cancer is one of the leading causes of cancer-related death. With the advancement in surgical techniques, the survival rate has been improved.
Sun, Xiao-Feng, +4 more
core +1 more source
CONVOLUTIONAL DEEP LEARNING NEURAL NETWORK FOR STROKE IMAGE RECOGNITION: REVIEW
Вестник КазНУ. Серия математика, механика, информатика, 2021 Deep learning is one of the developing area of articial intelligence research. It includes machine learning methods based on articial neural networks. One method that has been widely used and researched in recent years is convolution neural networks (CNN)
Azhar Toilybaikyzy Tursynova +3 more
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Convolutional Graph Neural Networks
2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019 Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural networks to be a convolution with a bank of learned filters. This makes them suitable for learning tasks based on data that exhibit the regular structure of time signals and images.
Fernando Gama +3 more
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Scientific Reports, 2021 Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is much ...
Dimitrios Bellos +3 more
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Pointwise Convolutional Neural Networks [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 10 pages, 6 figures, 10 tables.
Binh-Son Hua +2 more
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