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What is Convolutional Neural Network

Chapter 2
Handbook of Research on Deep Learning Innovations and Trends
A type of artificial neural networks, which uses a set of filters with tunable (learnable) parameters to extract local features from the input data.
Published in Chapter:
Enhanced Footsteps Generation Method for Walking Robots Based on Convolutional Neural Networks
Sergei Savin (Southwest State University, Russia) and Aleksei Ivakhnenko (Applied Parallel Computing LLC, Russia)
Copyright: © 2019 |Pages: 24
DOI: 10.4018/978-1-5225-7862-8.ch002
Abstract
In this chapter, the problem of finding a suitable foothold for a bipedal walking robot is studied. There are a number of gait generation algorithms that rely on having a set of obstacle-free regions where the robot can step to and there are a number of algorithms for generating these regions. This study breaches the gap between these algorithms, providing a way to quickly check if a given obstacle free region is accessible for foot placement. The proposed approach is based on the use of a classifier, constructed as a convolutional neural network. The study discusses the training dataset generation, including datasets with uncertainty related to the shapes of the obstacle-free regions. Training results for a number of different datasets and different hyperparameter choices are presented and showed robustness of the proposed network design both to different hyperparameter choices as well as to the changes in the training dataset.
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Chapter 22
Convolutional Neural Networks for Real-Time Eye Tracking in Interactive Applications
A type of artificial neural network used in image recognition and processing tasks, designed for processing pixel data.
Published in Chapter: Convolutional Neural Networks for Real-Time Eye Tracking in Interactive Applications; From: Handbook of Research on Applied AI for International Business and Marketing Applications
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Chapter 10
Empowering Health With an Advanced Multi-Disease Prediction System and Medical Encyclopaedia: Apna Clinic
A convolutional neural network (CNN) is a type of deep learning algorithm that is specifically designed for processing and analysing structured grid-like data, such as images or time series data. CNNs have been particularly successful in tasks related to computer vision, including image classification, object detection, and image segmentation. The key feature of CNNs is their ability to automatically learn and extract hierarchical patterns and features from input data. They achieve this by using specialised layers called convolutional layers, which apply filters or kernels to input data to detect local patterns. These filters are learned during the training process and help capture different visual features, such as edges, textures, and shapes, at various levels of abstraction.
Published in Chapter: Empowering Health With an Advanced Multi-Disease Prediction System and Medical Encyclopaedia: Apna Clinic; From: Intelligent Engineering Applications and Applied Sciences for Sustainability
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Chapter 5
Implementation of Deep Learning Neural Network for Retinal Images
In deep learning, a convolutional neural network is a class of deep neural networks, most commonly applied to analyzing visual imagery. CNNs use a variation of multilayer perceptrons designed to require minimal preprocessing.
Published in Chapter: Implementation of Deep Learning Neural Network for Retinal Images; From: Handbook of Research on Applications and Implementations of Machine Learning Techniques
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Chapter 58
Understanding Machine Learning Concepts
A type of deep neural network designed for processing structured arrays of data and most commonly applied to analyze visual imagery because its ability to identify patterns in the input image, such as lines, gradients, circles, or even eyes and faces.
Published in Chapter: Understanding Machine Learning Concepts; From: Encyclopedia of Data Science and Machine Learning
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Chapter 23
A Meta-Analytical Review of Deep Learning Prediction Models for Big Data
Convolutional Neural Network (CNN) is a form of feedforward neural network which make use of convolution, ReLU function, and pooling layers which help in dealing with information with high measurements.
Published in Chapter: A Meta-Analytical Review of Deep Learning Prediction Models for Big Data; From: Encyclopedia of Data Science and Machine Learning
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Chapter 16
Efficient End-to-End Asynchronous Time-Series Modeling With Deep Learning to Predict Customer Attrition
A type of neural network with an architecture that consists of kernels that learn to perform the matrix convolution operation on inputs to find patterns that have spatial proximity such as images or time-series.
Published in Chapter: Efficient End-to-End Asynchronous Time-Series Modeling With Deep Learning to Predict Customer Attrition; From: Handbook of Research on Methodologies and Applications of Supercomputing
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Chapter 18
What Is Deep Learning and How Has It Helped the COVID-19 Pandemic?
One of the most popular deep learning models, which includes the convolution operation and has benefits in terms of sparse interactions, parameter sharing, and equivariant representations. These types of networks are particularly effective for image recognition.
Published in Chapter: What Is Deep Learning and How Has It Helped the COVID-19 Pandemic?; From: Handbook of Research on Interdisciplinary Perspectives on the Threats and Impacts of Pandemics
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Chapter 9
Big Data Helps for Non-Pharmacological Disease Control Measures of COVID-19
A typical deep learning model that is commonly used to image classification, object detection, natural language procession, and predictive analysis. Such a network structure is a regularized version of fully connected networks, which belong to the class of artificial neural network.
Published in Chapter: Big Data Helps for Non-Pharmacological Disease Control Measures of COVID-19; From: Encyclopedia of Data Science and Machine Learning
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Chapter 8
Multi-Input CNN-LSTM for End-to-End Indian Sign Language Recognition: A Use Case With Wearable Sensors
Convolutional neural network, also known as ConvNet or CNN is a deep feed forward neural network that makes use of operation of convolution with sliding kernel to generate representative feature map of the input.
Published in Chapter: Multi-Input CNN-LSTM for End-to-End Indian Sign Language Recognition: A Use Case With Wearable Sensors; From: Challenges and Applications for Hand Gesture Recognition
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Chapter 9
Deep Learning Approach for Extracting Catch Phrases from Legal Documents
A neural network with a convolutional layer which does the mathematical operation of convolution in addition to the other layers of deep neural network.
Published in Chapter: Deep Learning Approach for Extracting Catch Phrases from Legal Documents; From: Neural Networks for Natural Language Processing
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Chapter 153
An Image-Based Ship Detector With Deep Learning Algorithms
A typical deep learning model that is commonly used to image classification, object detection, natural language procession, and predictive analysis. Such a network structure is a regularized version of fully connected networks, which belong to the class of artificial neural network.
Published in Chapter: An Image-Based Ship Detector With Deep Learning Algorithms; From: Encyclopedia of Data Science and Machine Learning
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Chapter 6
Is AI in Your Future?: AI Considerations for Scholarly Publishers
In deep learning, a convolutional neural network is a class of deep neural network, most commonly applied to analyze visual imagery.
Published in Chapter: Is AI in Your Future?: AI Considerations for Scholarly Publishers; From: Transforming Scholarly Publishing With Blockchain Technologies and AI
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Chapter 7
Advancing Artificial Intelligence-Enabled Cybersecurity for the Internet of Things
Convolutional neural network is a class of deep neural networks that mostly applied to analyzing visual imagery. They are generally used in image recognition, classification, face recognition, etc.
Published in Chapter: Advancing Artificial Intelligence-Enabled Cybersecurity for the Internet of Things; From: Handbook of Research on Advancing Cybersecurity for Digital Transformation
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Chapter 3
Understanding Convolutional Neural Network With TensorFlow: CNN
A Convolutional Neural Network is a Deep Learning method that can receive an image as an input, ascribe significance to different attributes in the picture, and distinguish between them. CNN requires far less pre-processing than other classification techniques. CNN can learn these characteristics with sufficient training, whereas filters in basic approaches are handcrafted.
Published in Chapter: Understanding Convolutional Neural Network With TensorFlow: CNN; From: Machine Learning Algorithms Using Scikit and TensorFlow Environments
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Chapter 3
Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays
A neural network where the hidden layers include layers that perform convolutional operations. The convolutional layers are usually followed by pooling layers and dense layers. At the convolutional layers, a filter (a vector or a multi-dimension tensor) will slide through the neurons to take the inner product with these neurons to provide values for the next layer.
Published in Chapter: Comparing Deep Neural Networks and Gradient Boosting for Pneumonia Detection Using Chest X-Rays; From: Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning
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Chapter 2
Detection of Diabetic Retinopathy With Mobile Application Using Deep Learning
It is a class of deep neural networks, most commonly applied to analyzing visual imagery.
Published in Chapter: Detection of Diabetic Retinopathy With Mobile Application Using Deep Learning; From: Diagnostic Applications of Health Intelligence and Surveillance Systems
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Chapter 6
Artificial Intelligence Methods for Face Covering Detections
A typical deep learning model that is commonly used to image classification, object detection, natural language procession, and predictive analysis. Such a network structure is a regularized version of fully connected networks, which belong to the class of artificial neural network.
Published in Chapter: Artificial Intelligence Methods for Face Covering Detections; From: Applied Big Data Analytics and Its Role in COVID-19 Research
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