Results 1 to 10 of about 191,916 (149)
A noise robust convolutional neural network for image classification
Results in Engineering, 2021 Convolutional Neural Networks (CNNs) are extensively used for image classification. Noisy images reduce the classification performance of convolutional neural networks and increase the training time of the networks.
Mohammad Momeny +4 more
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Optical Diffractive Convolutional Neural Networks Implemented in an All-Optical Way
Sensors, 2023 Optical neural networks can effectively address hardware constraints and parallel computing efficiency issues inherent in electronic neural networks. However, the inability to implement convolutional neural networks at the all-optical level remains a ...
Yaze Yu +4 more
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Binary and Multiclass Text Classification by Means of Separable Convolutional Neural Network
Inventions, 2021 In this paper, the structure of a separable convolutional neural network that consists of an embedding layer, separable convolutional layers, convolutional layer and global average pooling is represented for binary and multiclass text classifications ...
Elena Solovyeva, Ali Abdullah
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Simplified Routing Mechanism for Capsule Networks
Algorithms, 2023 Classifying digital images using neural networks is one of the most fundamental tasks within the field of artificial intelligence. For a long time, convolutional neural networks have proven to be the most efficient solution for processing visual data ...
János Hollósi +2 more
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INVESTIGATIONS ON THE POTENTIAL OF CONVOLUTIONAL NEURAL NETWORKS FOR VEHICLE CLASSIFICATION BASED ON RGB AND LIDAR DATA [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017 In recent years, there has been a significant improvement in the detection, identification and classification of objects and images using Convolutional Neural Networks.
R. Niessner, H. Schilling, B. Jutzi
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Convolution-Bidirectional Temporal Convolutional Network for Protein Secondary Structure Prediction
IEEE Access, 2022 As a basic feature extraction method, convolutional neural networks have some information loss problems when dealing with sequence problems, and a temporal convolutional network can compensate for this problem.
Yunqing Zhang, Yuming Ma, Yihui Liu
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STUDY OF HARDWARE-IMPLEMENTED CONVOLUTIONAL NEURAL NETWORKS OF THE U-NET CLASS
Известия Томского политехнического университета: Промышленная кибернетика, 2023 The authors have developed and implemented two convolutional neural networks of the U-Net class: a modification of the classical U-Net and a UNet with dilated convolutions. For training and testing convolutional neural networks, data sets were used based
Ivan V. Zoev +3 more
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Measurement: Sensors, 2021 The convolutional neural network is a subfield of artificial neural networks and has made great achievements in various domains over the past decade.
Hengyi Li +5 more
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Journal of Imaging, 2023 Artificial neural networks can solve various tasks in computer vision, such as image classification, object detection, and general recognition.
Ladislav Karrach, Elena Pivarčiová
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Кібернетика та комп'ютерні технології, 2021 Introduction. The implementation of information technologies in various spheres of public life dictates the creation of efficient and productive systems for entering information into computer systems. In such systems it is important to build an effective
Elshan Mustafayev, Rustam Azimov
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