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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 ...
András Horváth +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 Network (CNN): The architecture and applications
Applied Journal of Physical Science, 2022The human brain is made up of several hundreds of billions of interconnected neurons that process information in parallel. Researchers in the field of artificial intelligence have successfully demonstrated a considerable level of intelligence on chips and this has been termed Neural Networks (NNs).
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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 Networks (CNN)
2019Convolutional neural networks (CNN) are a specific type of neural network systems that are particularly suited for computer vision problems such as image recognition. In such tasks, the dataset is represented as a 2-D grid of pixels. See Figure 35-1.
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Hybrid particle swarm training for convolution neural network (CNN)
2017 Tenth International Conference on Contemporary Computing (IC3), 2017Convolutional Neural Networks(CNN) are one of the most used neural networks in the present time. Its applications are extremely varied. Most recently they have been proving helpful with deep learning, as well. Since it is growing in more convoluted domains, its training complexity is also increasing.
Yoshika Chhabra +2 more
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Convolutional Neural Networks (CNNs) for Medical Imaging
The chapter delves into the transformative impact of Convolutional Neural Networks (CNNs) on medical imaging, highlighting their ability to enhance diagnostic accuracy, streamline workflows, and enable real-time image analysis. It provides a comprehensive overview of CNN architectures, their principles, and their integration into diverse medical ...S. Aishwarya +5 more
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DL-CNN: Double Layered Convolutional Neural Networks
Proceedings of the 24th International Conference on Enterprise Information Systems, 2022Lixin Fu 0001, Rohith Rangineni
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Morph-CNN: A Morphological Convolutional Neural Network for Image Classification
2017Deep neural networks, an emergent type of feed forward networks, have gained a lot of interest especially for computer vision problems such as analyzing and understanding digital images. In this paper, a new deep learning architecture is proposed for image analysis and recognition. Two key ingredients are involved in our architecture.
Dorra Mellouli +3 more
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