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An Encoder-Decoder Based Convolution Neural Network (CNN) for Future Advanced Driver Assistance System (ADAS)

Applied Sciences (Switzerland), 2017
We propose a practical Convolution Neural Network (CNN) model termed the CNN for Semantic Segmentation for driver Assistance system (CSSA). It is a novel semantic segmentation model for probabilistic pixel-wise segmentation, which is able to predict ...
Robail Yasrab
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Convolutional Neural Networks (CNN)

2022
Deep 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 with Spatial-Variant Convolution Kernel

Remote Sensing, 2020
Radar images suffer from the impact of sidelobes. Several sidelobe-suppressing methods including the convolutional neural network (CNN)-based one has been proposed.
Tian Jin, Yongpeng Dai
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A Reversible-Logic based Architecture for Convolutional Neural Network (CNN)

2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), 2021
Convolutional-Neural-Network (CNN) is a deep learning model, which is used extensively to solve complex image classification or computer vision problems. CNN and more complex architecture variants of it such as vggX, GoogleNet, ImageNet, etc. are widely used in various application domains such as object detection, self-driving cars, instance ...
Kasem Khalil, Bappaditya Dey, Ashok Kumar 0001, Magdy A. Bayoumi   +3 more
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Convolutional Neural Network (CNN): The architecture and applications

Applied Journal of Physical Science, 2022
The 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 (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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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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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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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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