Results 21 to 30 of about 191,916 (149)
Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification
IEEE Access, 2021 A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature data without requiring the design ...
Kaisheng Liao +4 more
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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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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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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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Classification methods for handwritten digit recognition: A survey
Vojnotehnički Glasnik, 2023 Introduction/purpose: This paper provides a survey of handwritten digit recognition methods tested on the MNIST dataset. Methods: The paper analyzes, synthesizes and compares the development of different classifiers applied to the handwritten digit ...
Ira M. Tuba +2 more
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Dual-channel deep graph convolutional neural networks
Frontiers in Artificial IntelligenceThe dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks.
Zhonglin Ye +15 more
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An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks
IEEE AccessRecent advancements in quantum machine learning have spurred the development of hybrid quantum-classical convolutional neural networks (HQCCNNs), which have demonstrated promising potential for image classification tasks.
Li Hai +4 more
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A new approach to seasonal energy consumption forecasting using temporal convolutional networks
Results in Engineering, 2023 There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature.
Abdul Khalique Shaikh +4 more
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Geometric Deep Learning for Protein–Protein Interaction Predictions
IEEE Access, 2022 This work introduces novel approaches, based on geometrical deep learning, for predicting protein–protein interactions. A dataset containing both interacting and non-interacting proteins is selected from the Negatome Database.
Gabriel St-Pierre Lemieux +3 more
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