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Convolutional neural networks: applications, challenges and future prospects in brain tumor research. [PDF]
Front NeurolZhang P, Yang Z.
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C-CNN: Contourlet Convolutional Neural Networks
IEEE Transactions on Neural Networks and Learning Systems, 2021Extracting effective features is always a challenging problem for texture classification because of the uncertainty of scales and the clutter of textural patterns. For texture classification, spectral analysis is traditionally employed in the frequency domain.
Mengkun Liu +5 more
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Convolutional Neural Networks (CNN)
2021Convolutional neural networks (CNN or ConvNet) are a specific type of neural networks for processing grid-like data such as images and time series. In healthcare applications, the CNN models are widely used in automatic feature learning and disease classification from medical images, for example, automatic classification of skin lesions, detection of ...
Cao Xiao, Jimeng Sun
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Garbage classification using convolutional neural networks (CNNs)
Material Science & Engineering International Journal, 2023Proper garbage classification is essential for effective waste management and environmental sustainability. This research paper presents a comprehensive study of garbage classification using Convolutional Neural Networks (CNNs). The objective is to develop an accurate and automated garbage classification system leveraging the power of deep learning ...
Al-Mahmud Al-Mamun +4 more
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Cellular neural network friendly convolutional neural networks — CNNs with CNNs
Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017, 2017This paper discusses the development and evaluation of a Cellular Neural Network (CeNN) friendly deep learning network for solving the MNIST digit recognition problem. Prior work has shown that CeNNs leveraging emerging technologies such as tunnel transistors can improve energy or EDP of CeNNs, while simultaneously offering richer/more complex ...
Andras Horvath +4 more
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Convolutional Neural Networks (CNN)
2022Deep learning is one of the main technologies of machine learning. With deep learning, this chapter is talking about algorithms capable of mimicking the actions of the human brain through artificial neural networks. Compared to other algorithmic structures, neural networks have great advantages: first, their structure based on the stacking of non ...
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Convolutional Neural Networks (CNNs)
2017This model’s development can be traced back to the 1950s, where researchers Hubel and Wiesel modeled the animal visual cortex. At length in a 1968 paper, they discussed their findings, which identified both simple cells and complex cells within the brains of the monkeys and cats they studied. The simple cells, they observed, had a maximized output with
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Convolutional Neural Network (CNN) Accelerator Chip Design
2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID), 2019With the development of artificial intelligence, artificial neural network has been applied in many industry fields. The convolutional neural network (CNN) which is one of the most important algorithms in deep learning plays an important role in computer vision and natural language processing.
Xinran Ma, Ruiyong Zhao, Jianyang Zhou
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Convolutional Neural Network (CNN) Fundamental Operational Survey
2021A Convolutional Neural Network (CNN) is an algorithm of Deep Learning. It reads image as input. Allocate weights and biases to several objects in image. It identifies the differences from one image to another image. The pre-processing operation perform on CNN is less compare to other algorithms. CNN have the ability to gain filters.
B. P. Sowmya, M. C. Supriya
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