New Trends in the Global Digital Transformation Process of the Agri-Food Sector: An Exploratory Study Based on Twitter

Ancín, María; Pindado, Emilio; Sánchez, Mercedes

doi:10.2139/ssrn.4093526

Cited by 1 publication

(2 citation statements)

References 135 publications

(161 reference statements)

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“…Sigmoid: this activation function uses real numbers as inputs and limits the output between 0 and 1. The curve of the sigmoid function is S-shaped and can be mathematically represented as in (2).…”

Section: Nonlinearity Layer (Activation Function)mentioning

confidence: 99%

“…Deep learning has developed human-like abilities, such as knowledge learning, problem-solving, and decision-making [1]. Big companies have tried to adopt the latest digital technologies, including the Internet of Things (IoT), Big Data, Artificial Intelligence (AI), and Blockchain [2]. Deep learning technology is a development of machine learning and Artificial Intelligence (AI) [3].…”

Section: Introductionmentioning

confidence: 99%

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Understanding of Convolutional Neural Network (CNN): A Review

Purwono

Ma’arif

Rahmaniar

et al. 2023

IJRCS

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The application of deep learning technology has increased rapidly in recent years. Technologies in deep learning increasingly emulate natural human abilities, such as knowledge learning, problem-solving, and decision-making. In general, deep learning can carry out self-training without repetitive programming by humans. Convolutional neural networks (CNNs) are deep learning algorithms commonly used in wide applications. CNN is often used for image classification, segmentation, object detection, video processing, natural language processing, and speech recognition. CNN has four layers: convolution layer, pooling layer, fully connected layer, and non-linear layer. The convolutional layer uses kernel filters to calculate the convolution of the input image by extracting the fundamental features. The pooling layer combines two successive convolutional layers. The third layer is the fully connected layer, commonly called the convolutional output layer. The activation function defines the output of a neural network, such as 'yes' or 'no'. The most common and popular CNN activation functions are Sigmoid, Tanh, ReLU, Leaky ReLU, Noisy ReLU, and Parametric Linear Units. The organization and function of the visual cortex greatly influence CNN architecture because it is designed to resemble the neuronal connections in the human brain. Some of the popular CNN architectures are LeNet, AlexNet and VGGNet.

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Section: Nonlinearity Layer (Activation Function)mentioning

confidence: 99%