Deep neural networks (DNN) presents various applications in many industries and addresses numerous real-world problems. [1] The goal of the DNNs is to mimic the biological neuron of the human mind and ...
ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
Understand what activation functions are and why they’re essential in deep learning! This beginner-friendly explanation covers popular functions like ReLU, Sigmoid, and Tanh—showing how they help ...
Hunan Engineering and Technology Research Center for Agricultural Big Data Analysis & Decision-making, Hunan Agricultural University, Changsha, China In the context of intelligent agriculture in China ...
Abstract: The electrocardiogram (ECG) is an important tool in diagnosing heart diseases. In this study, we introduce ECGNet a customized deep learning model that utilizes advanced activation functions ...
Introduction: Carotid B-mode ultrasound (U/S) imaging provides more than the degree of stenosis in stroke risk assessment. Plaque morphology and texture have been extensively investigated in U/S ...
Neural networks are one typical structure on which artificial intelligence can be based. The term neural describes their learning ability, which to some extent mimics the functioning of neurons in our ...
Activation functions play a critical role in AI inference, helping to ferret out nonlinear behaviors in AI models. This makes them an integral part of any neural network, but nonlinear functions can ...
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