A predictive artificial neural network model as a simulator of the extra virgin olive oil elaboration process

Funes, Estrella; Allouche, Yosra; Beltrán, Gabriel; Aguliera, M. Paz; Jiménez, Antonio

doi:10.1177/0967033517725639

Cited by 6 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Funes et al created nine neural models to predict the properties of extra virgin olive oil developed as a quality target and by-product. ey analyzed various properties of olive oil through multiple models [3]. Kang et al developed an artificial neural network (ANN) model to explore energysaving technologies for air conditioners.…”

Section: Related Workmentioning

confidence: 99%

E-Commerce Intelligent Logistics Data Based on Neural Network Model

Ding

2022

Mobile Information Systems

View full text Add to dashboard Cite

Not only e-commerce is developing rapidly, but also intelligent logistics technology is becoming more and more mature. In daily life, we can track the logistics information synchronously. On the smartphone app, the logistics information can be viewed at any time. However, the current processing algorithms are not enough for the exponentially increasing data volume and increasingly complex data types. This paper aims to analyze the massive data generated by e-commerce intelligent logistics. In this paper, a new classification algorithm is proposed, which is improved on the ordinary ladder classification algorithm, and artificial neural networks are added for automatic iterative update learning, which can automatically classify a large amount of data. The experimental results show that the classification error rate of the improved algorithm is less than 5%, and when the sample size is less than 30,000, the improved algorithm can significantly outperform the original algorithm.

show abstract

Section: Related Workmentioning

confidence: 99%

E-Commerce Intelligent Logistics Data Based on Neural Network Model

Ding

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Geographical information system is a flowchart for collecting, managing, analyzing, and displaying related geographic paths in the whole or part of the land space under the support of contemporary science and technology [11]. Around the 1960s, a country developed the world's first geographic information system in order to facilitate the collection of site geological composition [12]. In the late 1960s, the United States launched a census plan, which realized the sampling and analysis of data from various parts of the country, and it was also used for road and communication management.…”

Section: Geographic Informationmentioning

confidence: 99%

“…In formula (12), ε(p) is the speed at which 0.1 < ε < 1.1 can be changed, so that the average value can be modified.…”

Section: Rbf Neural Network Modelmentioning

confidence: 99%

Artificial Neural Network Model in Spatial Analysis of Geographic Information System

2021

Mobile Information Systems

View full text Add to dashboard Cite

In the past two decades, the computer technology industry has developed rapidly, and the geological prospecting industry is also undergoing a computerized and electronic revolution. The application technology of new geological information systems is gradually adding us to the spatial information system of geological prospecting projects. In order to deeply study the current situation of the artificial neural network model in the spatial analysis of our country’s geographic information system, this paper uses the traditional classification analysis method; database analysis and neural network analysis method of compensating samples were collected, an artificial model of the network is established, and the algorithm is simplified. And a neural network model is created. In the research of A and B counties’ geographic information system, using a new network model, 61 geological disasters were found in County A, of which 47 were landslides, 4 collapses, and 10 unstable slopes. There were 19 geographical disasters in County B, including 9 unstable slopes, 6 landslides and 4 collapses. In terms of geographic prediction combined with the network model, the comparison with the actual situation shows that the geographical distribution is 99.7% in the geographical and geological disaster-prone areas, and the geographical distribution is less in the nonprone areas, with a proportion of 0.3%. Geological disaster-prone areas of low points accounted for 76.9%, and the number of disaster-affected points in the low-prone areas accounted for 22.8%. The geographical and geological grades divided by the evaluation model are basically consistent with the actual grades, which can meet the needs of geographic evaluation. It is basically realized that starting from the model’s geographic information system, a more comprehensive and practical artificial neural network model is designed.

show abstract

“…Increasing the number of hidden layers may reduce the classification or regression errors. Still, it may also cause the vanishing/exploding gradients problem that prevents the convergence of the neural networks [ 45 , 52 – 55 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks

Yang,

Zhou,

et al. 2024

Plant Methods

View full text Add to dashboard Cite

Background Camellia oleifera, an essential woody oil tree in China, propagates through grafting. However, in production, it has been found that the interaction between rootstocks and scions may affect fruit characteristics. Therefore, it is necessary to predict fruit characteristics after grafting to identify suitable rootstock types. Methods This study used Deep Neural Network (DNN) methods to analyze the impact of 106 6-year-old grafting combinations on the characteristics of C.oleifera, including fruit and seed characteristics, and fatty acids. The prediction of characteristics changes after grafting was explored to provide technical support for the cultivation and screening of specialized rootstocks. After determining the unsaturated fat acids, palmitoleic acid C16:1, cis-11 eicosenoic acid C20:1, oleic acid C18:1, linoleic acid C18:2, linolenic acid C18:3, kernel oil content, fruit height, fruit diameter, fresh fruit weight, pericarp thickness, fresh seed weight, and the number of fresh seeds, the DNN method was used to calculate and analyze the model. The model was screened using the comprehensive evaluation index of Mean Absolute Error (MAPE), determinate correlation R2 and and time consumption. Results When using 36 neurons in 3 hidden layers, the deep neural network model had a MAPE of less than or equal to 16.39% on the verification set and less than or equal to 13.40% on the test set. Compared with traditional machine learning methods such as support vector machines and random forests, the DNN method demonstrated more accurate predictions for fruit phenotypic characteristics, with MAPE improvement rates of 7.27 and 3.28 for the 12 characteristics on the test set and maximum R2 improvement values of 0.19 and 0.33. In conclusion, the DNN method developed in this study can effectively predict the oil content and fruit phenotypic characteristics of C. oleifera, providing a valuable tool for predicting the impact of grafting combinations on the fruit of C. oleifera.

show abstract

A predictive artificial neural network model as a simulator of the extra virgin olive oil elaboration process

Cited by 6 publications

References 24 publications

E-Commerce Intelligent Logistics Data Based on Neural Network Model

E-Commerce Intelligent Logistics Data Based on Neural Network Model

Artificial Neural Network Model in Spatial Analysis of Geographic Information System

Prediction of fruit characteristics of grafted plants of Camellia oleifera by deep neural networks

Contact Info

Product

Resources

About