Results 271 to 280 of about 73,468 (309)

Convolutional Neural Networks (CNNs)

2017
This 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)

2019
Convolutional 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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KRR-CNN: kernels redundancy reduction in convolutional neural networks

Neural Computing and Applications, 2021
Convolutional neural networks (CNNs) are a promising tool for solving real-world problems. However, successful CNNs often require a large number of parameters, which leads to a significant amount of memory and a higher computational cost. This may produce some undesirable phenomena, notably the overfitting.
El houssaine Hssayni, Nour-Eddine Joudar, Mohamed Ettaouil   +2 more
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Hybrid particle swarm training for convolution neural network (CNN)

2017 Tenth International Conference on Contemporary Computing (IC3), 2017
Convolutional 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, Sanchit Varshney, Ankita   +2 more
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Classifiers Comparison for Convolutional Neural Networks (CNNs) in Image Classification

2019 IEEE/ACM 23rd International Symposium on Distributed Simulation and Real Time Applications (DS-RT), 2019
This paper presents a comparison between five different classifiers (Multi-class Logistic Regression (MLR), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Random Forest (RF) and Gaussian Naive Bayes (GNB)) to be used in a Convolutional Neural Network (CNN) in order to perform images classification.
Mauro Tropea, Giuseppe Fedele
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V-CNN: Data Visualizing based Convolutional Neural Network

2018 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2018
Recently, artificial intelligence technology has aroused wide attention and application worldwide, and is considered to be the next technology to create a new paradigm in the industry. The convolutional neural network (CNN), which is beneficial in fields such as imaging and voice analysis, is a type of representative algorithm of artificial ...
Guanxiong Feng, Bo Li 0089, Mao Yang 0001, Zhongjiang Yan   +3 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, C. Selvamurugan, K. G. Parthiban, J. Manoj Prabhakar, K. K. Lakshmikandhan, J. Tharik Raja   +5 more
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UP-CNN: Un-pooling augmented convolutional neural network

Pattern Recognition Letters, 2019
Abstract Convolutional neural network (CNN) has shown remarkable performance in various visual recognition tasks. Most of existing CNN is a purely bottom-up and feed-forward architecture, we argue that it fails to consider the interaction between low-level fine details and high-level semantic information.
Chunyan Xu, Jian Yang 0003, Hanjiang Lai, Junbin Gao, Linlin Shen, Shuicheng Yan   +5 more
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DL-CNN: Double Layered Convolutional Neural Networks

Proceedings of the 24th International Conference on Enterprise Information Systems, 2022
Lixin Fu 0001, Rohith Rangineni
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Morph-CNN: A Morphological Convolutional Neural Network for Image Classification

2017
Deep 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, Tarek M. Hamdani, Mounir Ben Ayed, Adel M. Alimi   +3 more
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