Detection of Chronic Kidney Disease by Using Artificial Neural Networks and Gravitational Search Algorithm

2018 ◽  
pp. 441-448 ◽  
Author(s):  
S. M. K. Chaitanya ◽  
P. Rajesh Kumar
Keyword(s):  
Neural Networks ◽  
Chronic Kidney Disease ◽  
Artificial Neural Networks ◽  
Kidney Disease ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Gravitational Search ◽  
Artificial Neural

An end stage kidney disease predictor based on an artificial neural networks ensemble

2013 ◽  
Vol 40 (11) ◽  
pp. 4438-4445 ◽  
Author(s):  
Tommaso Di Noia ◽  
Vito Claudio Ostuni ◽  
Francesco Pesce ◽  
Giulio Binetti ◽  
David Naso ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Kidney Disease ◽  
End Stage Kidney Disease ◽  
Artificial Neural ◽  
Neural Networks Ensemble ◽  
End Stage

scholarly journals Optimization of Running Blade Prosthetics Utilizing Crow Search Algorithm Assisted by Artificial Neural Networks

2021 ◽  
Vol 67 (3) ◽  
pp. 88-100
Author(s):  
Rosel Solís Manuel Javier ◽  
José Omar Dávalos Ramírez ◽  
Javier Molina Salazar ◽  
Juan Antonio Ruiz Ochoa ◽  
Antonio Gómez Roa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Carbon Fibre ◽  
Failure Criterion ◽  
Search Algorithm ◽  
Acrylonitrile Butadiene Styrene ◽  
Sensitivity Study ◽  
Design Parameters ◽  
Artificial Neural ◽  
Important Design

A crow search algorithm (CSA) was applied to perform the optimization of a running blade prosthetics (RBP) made of composite materials like carbon fibre layers and cores of acrylonitrile butadiene styrene (ABS). Optimization aims to increase the RBP displacement limited by the Tsai-Wu failure criterion. Both displacement and the Tsai-Wu criterion are predicted using artificial neural networks (ANN) trained with a database constructed from finite element method (FEM) simulations. Three different cases are optimized varying the carbon fibre layers orientations: –45°/45°, 0°/90°, and a case with the two-fibre layer orientations intercalated. Five geometric parameters and a number of carbon fibre layers are selected as design parameters. A sensitivity analysis is performed using the Garzon equation. The best balance between displacement and failure criterion was found with fibre layers oriented at 0°/90°. The optimal candidate with –45°/45° orientation presents higher displacement; however, the Tsai-Wu criterion was less than 0.5 and not suitable for RBP design. The case with intercalated fibres presented a minimal displacement being the stiffer RBP design. The damage concentrates mostly in the zone that contacts the ground. The sensitivity study found that the number of layers and width were the most important design parameters.

scholarly journals Artificial Neural Networks Based Optimization Techniques: A Review

Electronics ◽  
2021 ◽  
Vol 10 (21) ◽  
pp. 2689
Author(s):  
Maher G. M. Abdolrasol ◽  
S. M. Suhail Hussain ◽  
Taha Selim Ustun ◽  
Mahidur R. Sarker ◽  
Mahammad A. Hannan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Optimization Techniques ◽  
Initial Population ◽  
Neural Net ◽  
Bee Colony ◽  
Whale Optimization ◽  
Artificial Neural

In the last few years, intensive research has been done to enhance artificial intelligence (AI) using optimization techniques. In this paper, we present an extensive review of artificial neural networks (ANNs) based optimization algorithm techniques with some of the famous optimization techniques, e.g., genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), and backtracking search algorithm (BSA) and some modern developed techniques, e.g., the lightning search algorithm (LSA) and whale optimization algorithm (WOA), and many more. The entire set of such techniques is classified as algorithms based on a population where the initial population is randomly created. Input parameters are initialized within the specified range, and they can provide optimal solutions. This paper emphasizes enhancing the neural network via optimization algorithms by manipulating its tuned parameters or training parameters to obtain the best structure network pattern to dissolve the problems in the best way. This paper includes some results for improving the ANN performance by PSO, GA, ABC, and BSA optimization techniques, respectively, to search for optimal parameters, e.g., the number of neurons in the hidden layers and learning rate. The obtained neural net is used for solving energy management problems in the virtual power plant system.

ANALYSIS OF DOCUMENTS RANKING ALGORITHMS BASED ON THE ARTIFICIAL NEURAL NETWORKS

2019 ◽  
Author(s):  
Andrew Lishchytovych ◽  
Volodymyr Pavlenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Architecture ◽  
Search Algorithm ◽  
Network Algorithms ◽  
Text Document ◽  
Ranking Algorithms ◽  
Selection For ◽  
Artificial Neural

The object of this study is to analyse the effectiveness of document ran­ king algorithms in search engines that use artificial neural networks to match the texts. The purpose of the study was to inspect a neural network model of text document ran­ king that uses clustering, factor analysis, and multi-layered network architecture. The work of neural network algorithms was compared with the standard statistical search algorithm OkapiBM25. The result of the study is to evaluate the effectiveness of the use of particular models and to recommend model selection for specific datasets.

Sign in / Sign up

Export Citation Format

Share Document