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What is Perceptron

Chapter 20
Artificial Intelligence and IoT-Based Technologies for Sustainable Farming and Smart Agriculture
these are machine learning algorithms that undertake supervised learning of functions called binary classifiers which decide whether or not an input, usually identified with a vector of numbers, belongs to a particular class.
Published in Chapter:
Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture
Hari Kishan Kondaveeti (VIT-AP University, Andhra Pradesh, India), Gonugunta Priyatham Brahma (VIT-AP University, Andhra Pradesh, India), and Dandhibhotla Vijaya Sahithi (VIT-AP University, Andhra Pradesh, India)
Copyright: © 2021 |Pages: 21
DOI: 10.4018/978-1-7998-1722-2.ch020
Abstract
Deep learning (DL), a part of machine learning (ML), comprises a contemporary technique for processing the images and analyzing the big data with promising outcomes. Deep learning methods are successfully being used in various sectors to gain better results. Agriculture sector is one of the sectors that could be benefitted from the deep learning techniques since the current agriculture techniques cannot keep up with the rapid growth in population. In this chapter, the recent trends in the applications of deep learning techniques in the agricultural sector and the survey of the research efforts that employ deep learning techniques are going to be discussed. Also, the models that are implemented are going to be analyzed and compared with the other existing models.
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Chapter 626
Artificial Neural Networks Tutorial
A type of binary classifier that maps its inputs (a vector of real type) to an output value (a scalar real type). The perceptron may be considered as the simplest model of feed-forward neural network, as the inputs directly feeding the output units through weighted connections.
Published in Chapter: Artificial Neural Networks Tutorial; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 9
Deep Learning Techniques for Prediction, Detection, and Segmentation of Brain Tumors
A perceptron is a single-layer neural network. It includes input values, weights and bias, net sum, and an activation function.
Published in Chapter: Deep Learning Techniques for Prediction, Detection, and Segmentation of Brain Tumors; From: Deep Neural Networks for Multimodal Imaging and Biomedical Applications
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Chapter 2
Deep Learning on Edge: Challenges and Trends
The basic unit of a neural network that encodes inputs from neurons of the previous layer using a vector of weights or parameters associated with the connections between perceptrons.
Published in Chapter: Deep Learning on Edge: Challenges and Trends; From: Smart Systems Design, Applications, and Challenges
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Chapter 7
A Comparison of Machine Learning Algorithms of Big Data for Time Series Forecasting Using Python
A binary classification model where the prediction (target) is the indicator function of the weighted sum of the inputs greater than a specified threshold.
Published in Chapter: A Comparison of Machine Learning Algorithms of Big Data for Time Series Forecasting Using Python; From: Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities
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Chapter 216
Statistical Simulations on Perceptron-Based Adders
Type of artificial (feedforward) Neural Network.§
Published in Chapter: Statistical Simulations on Perceptron-Based Adders; From: Encyclopedia of Artificial Intelligence
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Chapter 6
Artificial Intelligence Applied: Six Actual Projects in Big Organizations
The simplest form of artificial neural network, a basic operational unit which employs supervised learning. It is used to classify data into two classes.
Published in Chapter: Artificial Intelligence Applied: Six Actual Projects in Big Organizations; From: Educational and Social Dimensions of Digital Transformation in Organizations
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Chapter 12
Artificial Intelligence in Business Processes: The Mechanism of Interaction in Process Neurons
A particular mathematical model of an artificial neuron which includes an algorithm to adjust the weighting of the input signals, in order to learn relationships between inputs and outputs.
Published in Chapter: Artificial Intelligence in Business Processes: The Mechanism of Interaction in Process Neurons; From: Handbook of Research on Digital Transformation, Industry Use Cases, and the Impact of Disruptive Technologies
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Chapter 228
Synthetic Neuron Implementations
Type of artificial (feedforward) Neural Network.
Published in Chapter: Synthetic Neuron Implementations; From: Encyclopedia of Artificial Intelligence
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Chapter 12
Support Vector Machines and Applications
Perceptron is a learning algorithm which is used to learn the decision boundary for linearly separable data.
Published in Chapter: Support Vector Machines and Applications; From: Handbook of Research on Applied Cybernetics and Systems Science
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Chapter 9
A Deep Learning-Based Solar Photovoltaic Emulator
It is one of the early artificial neural networks with just a single neuron which was used for classification purposes.
Published in Chapter: A Deep Learning-Based Solar Photovoltaic Emulator; From: AI Techniques for Renewable Source Integration and Battery Charging Methods in Electric Vehicle Applications
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