Deep learning-based anomaly detection using one-dimensional convolutional neural networks (1D CNN) in machine centers (MCT) and computer numerical control (CNC) machines

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Deep learning-based anomaly detection using one-dimensional convolutional neural networks (1D CNN) in machine centers (MCT) and computer numerical control (CNC) machines

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Deep learning-based anomaly detection using one-dimensional convolutional neural networks (1D CNN) in machine centers (MCT) and computer numerical control (CNC) machines is a scholarly article[1].

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  • Deep learning-based anomaly detection using one-dimensional convolutional neural networks (1D CNN) in machine centers (MCT) and computer numerical control (CNC) machines's instance of is recorded as scholarly article[2].

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APA 4ort.xyz Knowledge Graph. (2026). Deep learning-based anomaly detection using one-dimensional convolutional neural networks (1D CNN) in machine centers (MCT) and computer numerical control (CNC) machines. Retrieved May 24, 2026, from https://4ort.xyz/entity/deep-learning-based-anomaly-detection-using-one-dimensional-convolutional-neural-networks-1d-cnn-in-machine-centers-mct-
MLA “Deep learning-based anomaly detection using one-dimensional convolutional neural networks (1D CNN) in machine centers (MCT) and computer numerical control (CNC) machines.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/deep-learning-based-anomaly-detection-using-one-dimensional-convolutional-neural-networks-1d-cnn-in-machine-centers-mct-.
BibTeX @misc{4ortxyz_deep-learning-based-anomaly-detection-using-one-dimensional-convolutional-neural-networks-1d-cnn-in-machine-centers-mct-_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Deep learning-based anomaly detection using one-dimensional convolutional neural networks (1D CNN) in machine centers (MCT) and computer numerical control (CNC) machines}}, year = {2026}, url = {https://4ort.xyz/entity/deep-learning-based-anomaly-detection-using-one-dimensional-convolutional-neural-networks-1d-cnn-in-machine-centers-mct-}, note = {Accessed: 2026-05-24}}
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