scholarly journals Artificial Neural Networks and Gompertz Functions for Modelling and Prediction of Solvents Produced by the S. cerevisiae Safale S04 Yeast

Fermentation ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 217
Author(s):  
Vinicio Moya Almeida ◽  
Belén Diezma Iglesias ◽  
Eva Cristina Correa Hernando
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Networks ◽  
Ethyl Acetate ◽  
Temperature Control ◽  
Input Data ◽  
Amyl Alcohol ◽  
Coefficient Of Determination ◽  
P Value ◽  
Fermentation Temperature

The present work aims to develop a mathematical model, based on Gompertz equations and ANNs to predict the concentration of four solvent compounds (isobutanol, ethyl acetate, amyl alcohol and n-propanol) produced by the yeasts S. cerevisiae, Safale S04, using only the fermentation temperature as input data. A beer wort was made, daily samples were taken and analysed by GC-FID. The database was grouped into five datasets of fermentation at different setpoint temperatures (15.0, 16.5, 18.0, 19.0 and 21.0 °C). With these data, the Gompertz models were parameterized, and new virtual datasets were used to train the ANNs. The coefficient of determination (R2) and p-value were used to compare the results. The ANNs, trained with the virtual data generated with the Gompertz functions, were the models with the highest R2 values (0.939 to 0.996), showing that the proposed methodology constitutes a useful tool to improve the quality (flavour and aroma) of beers through temperature control.

scholarly journals Predicting Tooth Surface Loss Using Genetic Algorithms-Optimized Artificial Neural Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ali Al Haidan ◽  
Osama Abu-Hammad ◽  
Najla Dar-Odeh
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Tooth Surface ◽  
Output Data ◽  
Surface Loss ◽  
Artificial Neural ◽  
High Degree

Our aim was to predict tooth surface loss in individuals without the need to conduct clinical examinations. Artificial neural networks (ANNs) were used to construct a mathematical model. Input data consisted of age, smoker status, type of tooth brush, brushing, and consumption of pickled food, fizzy drinks, orange, apple, lemon, and dried seeds. Output data were the sum of tooth surface loss scores for selected teeth. The optimized constructed ANN consisted of 2-layer network with 15 neurons in the first layer and one neuron in the second layer. The data of 46 subjects were used to build the model, while the data of 15 subjects were used to test the model. Accepting an error of ±5 scores for all chosen teeth, the accuracy of the network becomes more than 80%. In conclusion, this study shows that modeling tooth surface loss using ANNs is possible and can be achieved with a high degree of accuracy.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

scholarly journals Saturated Hydraulic Conductivity Estimation Using Artificial Neural Networks

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 705
Author(s):  
Josué Trejo-Alonso ◽  
Carlos Fuentes ◽  
Carlos Chávez ◽  
Antonio Quevedo ◽  
Alfonso Gutierrez-Lopez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hydraulic Conductivity ◽  
Input Data ◽  
Logistic Function ◽  
Saturated Hydraulic Conductivity ◽  
Irrigation District ◽  
Pedotransfer Functions ◽  
Back Propagation Algorithm ◽  
Artificial Neural

In the present work, we construct several artificial neural networks (varying the input data) to calculate the saturated hydraulic conductivity (KS) using a database with 900 measured samples obtained from the Irrigation District 023, in San Juan del Rio, Queretaro, Mexico. All of them were constructed using two hidden layers, a back-propagation algorithm for the learning process, and a logistic function as a nonlinear transfer function. In order to explore different arrays for neurons into hidden layers, we performed the bootstrap technique for each neural network and selected the one with the least Root Mean Square Error (RMSE) value. We also compared these results with pedotransfer functions and another neural networks from the literature. The results show that our artificial neural networks obtained from 0.0459 to 0.0413 in the RMSE measurement, and 0.9725 to 0.9780 for R2, which are in good agreement with other works. We also found that reducing the amount of the input data offered us better results.

Rainfall Prediction by Artificial Neural Networks Trained using Different Climate Variables

2021 ◽  
Author(s):  
Mateus Alexandre da Silva ◽  
Marina Neves Merlo ◽  
Michael Silveira Thebaldi ◽  
Danton Diego Ferreira ◽  
Felipe Schwerz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Back Propagation ◽  
Metropolitan Region ◽  
Climate Variables ◽  
Back Propagation Algorithm ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Belo Horizonte

Abstract Predicting rainfall can prevent and mitigate damages caused by its deficit or excess, besides providing necessary tools for adequate planning for the use of water. This research aimed to predict the monthly rainfall, one month in advance, in four municipalities in the metropolitan region of Belo Horizonte, using artificial neural networks (ANN) trained with different climate variables, and to indicate the suitability of such variables as inputs to these models. The models were developed through the MATLAB® software version R2011a, using the NNTOOL toolbox. The ANN’s were trained by the multilayer perceptron architecture and the Feedforward and Back propagation algorithm, using two combinations of input data were used, with 2 and 6 variables, and one combination of input data with 3 of the 6 variables most correlated to observed rainfall from 1970 to 1999, to predict the rainfall from 2000 to 2009. The most correlated variables to the rainfall of the following month are the sequential number corresponding to the month, total rainfall and average compensated temperature, and the best performance was obtained with these variables. Furthermore, it was concluded that the performance of the models was satisfactory; however, they presented limitations for predicting months with high rainfall.

scholarly journals Development of turning process digital twin based on machine learning

2021 ◽  
pp. 32-41
Author(s):  
D. A. Rastorguev ◽  
◽  
A. A. Sevastyanov ◽  
Keyword(s):  
Neural Networks ◽  
Mathematical Model ◽  
Artificial Neural Networks ◽  
Information Technologies ◽  
Cutting Process ◽  
Turning Process ◽  
Force Load ◽  
Cnc Machine ◽  
Digital Twin ◽  
Artificial Neural

Today, manufacturing technologies are developing within the Industry 4.0 concept, which is the information technologies introduction in manufacturing. One of the most promising digital technologies finding more and more application in manufacturing is a digital twin. A digital twin is an ensemble of mathematical models of technological process, which exchanges information with its physical prototype in real-time. The paper considers an example of the formation of several interconnected predictive modules, which are a part of the structure of the turning process digital twin and designed to predict the quality of processing, the chip formation nature, and the cutting force. The authors carried out a three-factor experiment on the hard turning of 105WCr6 steel hardened to 55 HRC. Used an example of the conducted experiment, the authors described the process of development of the digital twin diagnostic module based on artificial neural networks. When developing a mathematical model for predicting and diagnosing the cutting process, the authors revealed higher accuracy, adaptability, and versatility of artificial neural networks. The developed mathematical model of online diagnostics of the cutting process for determining the surface quality and chip type during processing uses the actual value of the cutting depth determined indirectly by the force load on the drive. In this case, the model uses only the signals of the sensors included in the diagnostic subsystem on the CNC machine. As an informative feature reflecting the force load on the machine’s main motion drive, the authors selected the value of the energy of the current signal of the spindle drive motor. The study identified that the development of a digital twin is possible due to the development of additional modules predicting the accuracy of dimensions, geometric profile, tool wear.

A Neural Networks Pruning and Data Fusion Based Intrusion Detection Model

2014 ◽  
Vol 651-653 ◽  
pp. 1772-1775
Author(s):  
Wei Gong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Intrusion Detection ◽  
Data Fusion ◽  
Input Data ◽  
The Other ◽  
Pruning Algorithm ◽  
Detection Model ◽  
Process Ability

The abilities of summarization, learning and self-fitting and inner-parallel computing make artificial neural networks suitable for intrusion detection. On the other hand, data fusion based IDS has been used to solve the problem of distorting rate and failing-to-report rate and improve its performance. However, multi-sensor input-data makes the IDS lose its efficiency. The research of neural network based data fusion IDS tries to combine the strong process ability of neural network with the advantages of data fusion IDS. A neural network is designed to realize the data fusion and intrusion analysis and Pruning algorithm of neural networks is used for filtering information from multi-sensors. In the process of intrusion analysis pruning algorithm of neural networks is used for filtering information from multi-sensors so as to increase its performance and save the bandwidth of networks.

Deep Learning and Biomedical Engineering

Biotechnology ◽  
2019 ◽  
pp. 562-575
Author(s):  
Suraj Sawant
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Real World ◽  
Biomedical Engineering ◽  
Input Data ◽  
Reference Source ◽  
Data Input ◽  
Essential Information

Deep learning (DL) is a method of machine learning, as running over artificial neural networks, which has a structure above the standards to deal with large amounts of data. That is generally because of the increasing amount of data, input data sizes, and of course, greater complexity of objective real-world problems. Performed research studies in the associated literature show that the DL currently has a good performance among considered problems and it seems to be a strong solution for more advanced problems of the future. In this context, this chapter aims to provide some essential information about DL and its applications within the field of biomedical engineering. The chapter is organized as a reference source for enabling readers to have an idea about the relation between DL and biomedical engineering.

Real-Time Recoloring Ishihara Plates Using Artificial Neural Networks for Helping Colorblind People

2020 ◽  
pp. 138-156
Author(s):  
Martín Montes Rivera ◽  
Alejandro Padilla ◽  
Juana Canul-Reich ◽  
Julio Ponce
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Input Data ◽  
Traffic Signals ◽  
Educational Materials ◽  
Ishihara Test ◽  
Artificial Neural ◽  
Intelligence Technique

Vision sense is achieved using cells called rods (luminosity) and cones (color). Color perception is required when interacting with educational materials, industrial environments, traffic signals, among others, but colorblind people have difficulties perceiving colors. There are different tests for colorblindness like Ishihara plates test, which have numbers with colors that are confused with colorblindness. Advances in computer sciences produced digital assistants for colorblindness, but there are possibilities to improve them using artificial intelligence because its techniques have exhibited great results when classifying parameters. This chapter proposes the use of artificial neural networks, an artificial intelligence technique, for learning the colors that colorblind people cannot distinguish well by using as input data the Ishihara plates and recoloring the image by increasing its brightness. Results are tested with a real colorblind people who successfully pass the Ishihara test.

scholarly journals Artificial Neural Networks and Linear Regression Reduce Sample Intensity to Predict the Commercial Volume of Eucalyptus Clones

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 268 ◽  
Author(s):  
Ivaldo Tavares Júnior ◽  
Jonas Rocha ◽  
Ângelo Ebling ◽  
Antônio Chaves ◽  
José Zanuncio ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Model Fit ◽  
P Value ◽  
Diameter Class ◽  
The Real ◽  
Total Data ◽  
Artificial Neural ◽  
Clone Group

Equations to predict Eucalyptus timber volume are continuously updated, but most of them cannot be used for certain locations. Thus, equations of similar strata are applied to clonal plantations where trees cannot be felled to fit volumetric models. The objective of this study was to use linear regression and artificial neural networks (ANN) to reduce the number of trees sampled while maintaining the accuracy of commercial volume predictions with bark up to 4 cm in diameter at the top (v) of Eucalyptus clones. Two methods were evaluated in two scenarios: (a) regression model fit and ANN training with 80% of the data (533 trees) and per clone group with 80% of the trees in each group; and (b) model fit and ANN training with trees of only one clone group at ages two and three, with sample intensities of six, five, four, three, two, and one tree per diameter class. The real and predicted v averages did not differ in sample intensities from six to two trees per diameter class with different methods. The frequency distribution of individuals by volume class by the two methods (regression and ANN) compared to the real values were similar in scenarios (a) and (b) by the Kolmogorov–Smirnov test (p-value > 0.01). The application of ANN was more effective for total data analysis with non-linear behavior, without sampled environment stratification. The Prodan model also generates estimates with accuracy, and, among the regression models, is the best fit to the data. The volume with bark up to 4 cm in diameter at the top of Eucalyptus clones can be predicted with at least three trees per diameter class with regression (root mean square error in percentage, RMSE = 12.32%), and at least four trees per class with ANN (RMSE = 11.73%).

Apply Artificial Neural Networks to Estimate Stress Distribution around Holes Edge in Composites

2012 ◽  
Vol 490-495 ◽  
pp. 3105-3108
Author(s):  
Kamran Pazand ◽  
Younes Alizadeh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Composite Laminates ◽  
Composite Plate ◽  
Input Data ◽  
Data Sets ◽  
Fast Determination ◽  
Artificial Neural

The purpose of this paper is to estimate the fast determination of stress distribution around a circular hole in symmetric composite laminates under in-plane loading. For this purpose calculation of stress values in the composite plate around edge holes in different plies position for a finite number of input data sets using the Lekhnitskii expressions and code program. The resulting data would then be used to train artificial neural networks (ANN) which would be able to predict –accurately enough- those quantities throughout the composite plate body for any given input value in any position ply and fore and stress that impose.

Sign in / Sign up

Export Citation Format

Share Document