Survey of optimization algorithms in modern neural networks
The main goal of machine learning is the creation of self-learning algorithms in many areas
of human activity. It allows a replacement of a person with artificial intelligence in seeking to …
of human activity. It allows a replacement of a person with artificial intelligence in seeking to …
Applications MLP and other methods in artificial intelligence of fruit and vegetable in convective and spray drying
K Przybył, K Koszela - Applied Sciences, 2023 - mdpi.com
The seasonal nature of fruits and vegetables has an immense impact on the process of
seeking methods that allow extending the shelf life in this category of food. It is observed that …
seeking methods that allow extending the shelf life in this category of food. It is observed that …
A framework based on symbolic regression coupled with extended physics-informed neural networks for gray-box learning of equations of motion from data
We propose a framework and an algorithm to uncover the unknown parts of nonlinear
equations directly from data. The framework is based on eXtended Physics-Informed Neural …
equations directly from data. The framework is based on eXtended Physics-Informed Neural …
Deep learning model for detection of hotspots using infrared thermographic images of electrical installations
EK Ukiwe, SA Adeshina, T Jacob… - Journal of Electrical …, 2024 - Springer
Hotspots in electrical power equipment or installations are a major issue whenever it occurs
within the power system. Factors responsible for this phenomenon are many, sometimes …
within the power system. Factors responsible for this phenomenon are many, sometimes …
An improved customized CNN model for adaptive recognition of cerebral palsy people's handwritten digits in assessment
Cerebral palsy (CP) is used to describe a group of disorders, characterized by non-
progressive, but permanent damage to the develo** brain that results in motor deficits …
progressive, but permanent damage to the develo** brain that results in motor deficits …
Mixed-sized biomedical image segmentation based on U-Net architectures
Convolutional neural networks (CNNs) are becoming increasingly popular in medical Image
Segmentation. Among them, U-Net is a widely used model that can lead to cutting-edge …
Segmentation. Among them, U-Net is a widely used model that can lead to cutting-edge …
[HTML][HTML] An explainable AI-based blood cell classification using optimized convolutional neural network
White blood cells (WBCs) are a vital component of the immune system. The efficient and
precise classification of WBCs is crucial for medical professionals to diagnose diseases …
precise classification of WBCs is crucial for medical professionals to diagnose diseases …
Geometry-structure models for liquid crystal interfaces, drops and membranes: wrinkling, shape selection and dissipative shape evolution
We review our recent contributions to anisotropic soft matter models for liquid crystal
interfaces, drops and membranes, emphasizing validations with experimental and biological …
interfaces, drops and membranes, emphasizing validations with experimental and biological …
A feedforward neural network framework for approximating the solutions to nonlinear ordinary differential equations
In this paper, we propose a method to approximate the solutions to nonlinear ordinary
differential equations (ODE) using a deep learning feedforward artificial neural networks …
differential equations (ODE) using a deep learning feedforward artificial neural networks …
Early diagnosis of COVID-19 images using optimal CNN hyperparameters
Coronavirus disease (COVID-19) is a worldwide epidemic that poses substantial health
hazards. However, COVID-19 diagnostic test sensitivity is still restricted due to abnormalities …
hazards. However, COVID-19 diagnostic test sensitivity is still restricted due to abnormalities …