Results 61 to 70 of about 874,187 (329)

Applications of artificial neural networks for enhanced livestock productivity: A review

Indian Journal of Animal Sciences, 2016
Artificial neural network models are machine-learning systems, a type of artificial intelligence. They have been inspired by and developed along the working principles of the human brain and its nerve cells.
V B DONGRE, R S GANDHI
doaj   +1 more source

Eigen Artificial Neural Networks

, 2019
{"references": ["Francisco Yepes Barrera. B\u00fasqueda de la estructura \u00f3ptima de redes neurales con algoritmos gen\u00e9ticos y simulated annealing. verificaci\u00f3n con el benchmark proben1. In- teligencia Artificial, Revista Iberoamericana de IA, 11(34):41\u201361, 2007.", "Christopher M. Bishop.
openaire   +2 more sources

Diffusion Tractography Biomarker for Epilepsy Severity in Children With Drug‐Resistant Epilepsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To develop a novel deep‐learning model of clinical DWI tractography that can accurately predict the general assessment of epilepsy severity (GASE) in pediatric drug‐resistant epilepsy (DRE) and test if it can screen diverse neurocognitive impairments identified through neuropsychological assessments.
Jeong‐Won Jeong, Min‐Hee Lee, Hiroshi Uda, Yoon Ho Hwang, Michael Behen, Aimee Luat, Csaba Juhász, Eishi Asano   +7 more
wiley   +1 more source

Optimized Time–Frequency Analysis for Induction Motor Fault Detection Using Hybrid Differential Evolution and Deep Learning Techniques

International Journal of Adaptive Control and Signal Processing, EarlyView.
Workflow of the parameter optimization process for ITSC fault detection, applying Differential Evolution optimization and the Smooth Pseudo Wigner‐Ville Distribution for signal processing. The optimized parameters are then used in the failure identification pipeline, which combines the signal processing with a YOLO‐based architecture for fault severity
Rafael Martini Silva, Avyner Lorran de Oliveira Vitor, Marcelo Favoretto Castoldi, Alessandro Goedtel, Wesley Angelino Souza   +4 more
wiley   +1 more source

An approach based on gamma-ray transmission technique and artificial neural network for accurately measuring the thickness of various materials [PDF]

Nuclear Technology and Radiation Protection
This paper presents an approach based on the gamma-ray transmission technique and artificial neural network for accurately measuring the thickness of various materials in flat sheet form.
Trang Le Thi Ngoc, Linh Nguyen Thi Truc, Thanh Tran Thien, Tam Hoang Duc, Chuong Huynh Dinh   +4 more
doaj   +1 more source

Demand forecasting in a Supply Chain using Machine Learning Algorithms [PDF]

مجله مدل سازی در مهندسی, 2015
—the purpose of this paper is to compare two artificial intelligence algorithms for forecasting supply chain demand. In first step data are prepared for entering into forecasting models.
Mohsen Shafiei Nikabadi, Mohsen Shafiei Nikabadi, Ali Azimi   +2 more
doaj   +1 more source

CoCalc as a Learning Tool for Neural Network Simulation in the Special Course "Foundations of Mathematic Informatics"

, 2018
The role of neural network modeling in the learning content of the special course "Foundations of Mathematical Informatics" was discussed. The course was developed for the students of technical universities - future IT-specialists and directed to ...
Markova, Oksana, Popel, Maiia, Semerikov, Serhiy   +2 more
core   +1 more source

Detecting Dengue in Flight: Leveraging Machine Learning to Analyze Mosquito Flight Patterns for Infection Detection

Advanced Biology, EarlyView.
Dengue infection alters mosquito flight behavior, enabling detection using machine learning classifiers. This study analyzes 3D flight trajectories and evaluates multiple models, showing that longer sequence lengths improve classification performance.
Nouman Javed, Adam J. López‐Denman, Prasad N. Paradkar, Asim Bhatti   +3 more
wiley   +1 more source

Artificial Neural Network Methods in Quantum Mechanics

, 1997
In a previous article we have shown how one can employ Artificial Neural Networks (ANNs) in order to solve non-homogeneous ordinary and partial differential equations.
A. Likas, Barron, Christodoulou, D.I. Fotiadis, Evangelakis, Flügge, Ford, Ford, Frost, Funahashi, Hornik, I.E. Lagaris, Kaluza, Kaneko, Kincaid, Lagaris, Papageorgiou, Saad, Saad, Saad, Williamson   +20 more
core   +4 more sources

Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics

Advanced Engineering Materials, EarlyView.
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani, Aditi Singh, Behnam Ashrafi   +2 more
wiley   +1 more source