Application of Mixture Design and Kohonen Neural Network for Determination of Macro- and Microelement in Mullet (Mugil cephalus) by MIP OES

2021 ◽  
Author(s):  
Clissiane Soares Viana Pacheco ◽  
Floriatan Santos Costa ◽  
Wesley Nascimento Guedes ◽  
Marina Santos de Jesus ◽  
Thiago Pereira das Chagas ◽  
...  
Keyword(s):  
Neural Network ◽  
Mixture Design ◽  
Mugil Cephalus ◽  
Kohonen Neural Network ◽  
Mip Oes

Artificial neural network employment for element determination in Mugil cephalus by ICP OES in Pontal Bay, Brazil

2020 ◽  
Vol 12 (29) ◽  
pp. 3713-3721
Author(s):  
Milana Aboboreira Simões Batista ◽  
Luana Novaes Santos ◽  
Bruna Cirineu Chagas ◽  
Ivon Pinheiro Lôbo ◽  
Cleber Galvão Novaes ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sample Preparation ◽  
Chemical Elements ◽  
Mixture Design ◽  
Mugil Cephalus ◽  
Self Organizing Map ◽  
Icp Oes ◽  
Element Determination ◽  
Artificial Neural

Mixture design applied to sample preparation of Mugil cephalus and exploratory evaluation of the concentrations of chemical elements using Kohonen Self-Organizing Map (KSOM) combined with Artificial Neural Network (ANNs).

Development of procedure for sample preparation of cashew nuts using mixture design and evaluation of nutrient profiles by Kohonen neural network

2019 ◽  
Vol 273 ◽  
pp. 136-143 ◽  
Author(s):  
Luana Santos Moreira ◽  
Bruna Cirineu Chagas ◽  
Clissiane Soares Viana Pacheco ◽  
Herick Macedo Santos ◽  
Luiz Henrique Sales de Menezes ◽  
...  
Keyword(s):  
Neural Network ◽  
Sample Preparation ◽  
Mixture Design ◽  
Kohonen Neural Network ◽  
Nutrient Profiles

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

Determination of Age, Gender, Dress Color and Type of a Person by Convolutional Neural Network (CNN)

2020 ◽  
Author(s):  
CSN Koushik ◽  
Shruti Bhargava Choubey ◽  
Abhishek Choubey ◽  
D. Naresh ◽  
N. Bhanu Prakash Reddy
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

scholarly journals Determination of rheological properties of bio-asphalt binders through experimental and multilayer feed-forward neural network methods

2021 ◽  
Author(s):  
Abdulnaser M Al-Sabaeei ◽  
Madzlan B Napiah ◽  
Muslich H Sutanto ◽  
Suzielah Rahmad ◽  
Nur Izzi Md Yusoff ◽  
...  
Keyword(s):  
Neural Network ◽  
Rheological Properties ◽  
Asphalt Binders ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Network Methods

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

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