scholarly journals The Use of Artificial Neural Networks to Prioritize Impact Factors Affecting Thai Rural Village Development

2011 ◽  
Vol 2 (2) ◽  
pp. 89-93
Author(s):  
Wittaya Pornpatcharapong ◽  
Chuvej Chansa-ngavej . ◽  
Supachok Wiriyacosol . ◽  
Chanchai Bunchapattanasakda .
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Community Development ◽  
Impact Factors ◽  
Factors Affecting ◽  
Rural Village ◽  
Artificial Neural ◽  
Information Database ◽  
Development Department

This paper aims to prioritize impact factors which affect Thai rural village development. The basic village-leveled information database (NRD-2C) of the Community Development Department (CDD), Ministry of Interior, Thailand, was applied with Artificial Neural Networks (ANN) to measure the amount of impact for each factor affecting Thai rural village development. According to results, the top 5 impact factors are “Land Possession”, “Electricity”, “Communication”, “Educational Level”, and “Household Industry” with 17.88, 15.35, 14.02, 12.06, and 10.57 score of impact respectively with 95.60 percent of estimated accuracy.

scholarly journals The use of classical methods and neural networks in deformation studies of hydrotechnical objects

2020 ◽  
Vol 12 (1) ◽  
pp. 718-725
Author(s):  
Maria Mrówczyńska ◽  
Jacek Sztubecki ◽  
Małgorzata Sztubecka ◽  
Izabela Skrzypczak
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cylinder Surface ◽  
Building Structures ◽  
Steel Cylinder ◽  
Factors Affecting ◽  
Initial Stage ◽  
Artificial Neural ◽  
Set Of Points

Abstract Objects’ measurements often boil down to the determination of changes due to external factors affecting on their structure. The estimation of changes in a tested object, in addition to proper measuring equipment, requires the use of appropriate measuring methods and experimental data result processing methods. This study presents a statement of results of geometrical measurements of a steel cylinder that constitutes the main structural component of the historical weir Czersko Polskie in Bydgoszcz. In the initial stage, the estimation of reliable changes taking place in the cylinder structure involved the selection of measuring points essential for mapping its geometry. Due to the continuous operation of the weir, the points covered only about one-third of the cylinder area. The set of points allowed us to determine the position of the cylinder axis as well as skews and deformations of the cylinder surface. In the next stage, the use of methods based on artificial neural networks allowed us to predict the changes in the tested object. Artificial neural networks have proved to be useful in determining displacements of building structures, particularly hydro-technical objects. The above-mentioned methods supplement classical measurements that create the opportunity for carrying out additional analyses of changes in a spatial position of such structures. The purpose of the tests is to confirm the suitability of artificial neural networks for predicting displacements of building structures, particularly hydro-technical objects.

Factors Affecting the Stability of Nanoemulsions—Use of Artificial Neural Networks

2009 ◽  
Vol 27 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Amir Amani ◽  
Peter York ◽  
Henry Chrystyn ◽  
Brian J. Clark
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Factors Affecting ◽  
Artificial Neural ◽  
The Stability

Application of Artificial Neural Networks to Predict Cotton Production: A Case Study in Diyarbakır Province, Turkey

2020 ◽  
Author(s):  
Nazire Mikail ◽  
Mehmet Fırat BARAN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Plant Production ◽  
Good Prediction ◽  
Ann Model ◽  
Cotton Production ◽  
Factors Affecting ◽  
Artificial Neural Network Method ◽  
Artificial Neural

Abstract Cultivators are always curious about the factors affecting yield in plant production. Determining these factors can provide information about the yield in the future. The reliability of information is dependent on a good prediction model. According to the operating process, artificial neural networks imitate the neural network in humans. The ability to make predictions for the current situation by combining the information people have gained from different experiences is designed in artificial neural networks. Therefore, in complex problems, it gives better results than artificial neural networks.In this study, we used an artificial neural network method to model the production of cotton. From a comprehensive datum collection spanning 73 farms in Diyarbakır, Turkey, the mean cotton production was 559.19 kg da-1. There are four factors that are selected as pivotal inputs into this model. As a result, the ultimate ANN model is able to forshow cotton production, which is built on elements such as farm states (cotton area and irrigation periodicity), machinery usage and fertilizer consumption.At the end of the study, cotton yield was estimated with 84% accuracy.

scholarly journals Building artificial neural networks to predict direction and magnitude of wind, current and wave for sailing vessels

2018 ◽  
Vol 8 (2) ◽  
pp. 55-61
Author(s):  
Timur Inan ◽  
Ahmet Fevzi Baba
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Direction ◽  
Wind Wave ◽  
Current Direction ◽  
Wave Direction ◽  
Factors Affecting ◽  
Historical Information ◽  
Sea Current ◽  
Artificial Neural

Current, wind, wave direction and magnitude are important factors affecting the course of ships. These factors may act positively or negatively depending on the course of a vessel. In both cases, optimization of the route according to these conditions, will improve the factors such as labor, fuel and time. In order to estimate the wind, wave, current direction and magnitude for the region to be navigated, it is necessary to develop a system that can make predictions by using historical information. Our study uses historical information from the E1M3A float, which is a part of the POSEIDON system. With this information being used, artificial neural networks were trained and three separate artificial neural networks were created. Artificial neural networks can predict wind direction and speed, direction and speed of sea current, wave direction and heigth. The esmitations made by this system are only valid for the region where the float is located. For different regions, it is necessary to use artificial neural networks trained using the historical information of those regions. This study is an example for prospective studies.

scholarly journals Using of Artificial Neural Networks in Support System of Forensic Building-Technical Expertise

2020 ◽  
Vol 9 (4) ◽  
pp. 3162-3168 ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Technical Condition ◽  
Fuzzy Neural Networks ◽  
Impact Factors ◽  
Technical Expertise ◽  
Structural Elements ◽  
Resonance Theory ◽  
Artificial Neural ◽  
The Impact

The article contains an analysis of the order of forensic building-technical expertise and expert research to determine the reasons for the deterioration of the technical condition of the structural elements of buildings. The conditions for forming expert conclusions about the possible correlation between the appearance of negative changes in the technical condition of the structural elements that have become the subject of forensic building-technical expertise and the various factors of influence of the environment are investigated. In doing so, the focus is on the impact factors associated with carrying out renovation work in adjacent premises. In addition, issues related to the fuzzy uncertainty of the different nature of the expert researches are highlighted. Some of these problems are proposed to be solved by the using of artificial neural networks in the fuzzy subsystem of the system of support of forensic building-technical expertise. It is shown that a considerable part of the materials of forensic building-technical expertise and expert research is represented by photographs of injuries. Fixation of damaged structures is reflected in the plans of premises and schemes of placement of structures in the buildings. The graphic information of the research materials is accompanied by textual information, the processing of which requires the use of models and methods of fuzzy mathematics. The fragment of the knowledge base is provided, which contains information on the geometric parameters of damage to building structures and an example of a fuzzy rule that reflects an expert conclusion. The expediency of using fuzzy neural networks of adaptive resonance theory of the Cascade ARTMAP category is substantiated. Cascade ARTMAP memory card schematic is shown.

AN EFFECTIVE MODEL FOR CARBON DIOXIDE EMISSIONS PREDICTION: COMPARISON OF ARTIFICIAL NEURAL NETWORKS LEARNING ALGORITHMS

2014 ◽  
Vol 13 (03) ◽  
pp. 1450014
Author(s):  
LAZIM ABDULLAH ◽  
HERRINI MOHD PAUZI
Keyword(s):  
Carbon Dioxide ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Carbon Dioxide Emissions ◽  
Learning Algorithms ◽  
Impact Factors ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Lm Algorithm

This paper intends to compare various learning algorithms available for training the multi-layer perceptron (MLP) type of artificial neural networks (ANNs). By using different learning algorithms, this study investigates the performances of gradient descent (GD) algorithm; Levenberg-Marquardt (LM) algorithm; and also Boyden, Fletcher, Goldfarb and Shannon (BFGS) algorithm to predict the emissions of carbon dioxide ( CO 2) in Malaysia. The impact factors of emissions, such as energy use; gross domestic product per capita; population density; combustible renewable and waste; also CO 2 intensity were employed in developing all ANN models investigated in this study. A wide variety of standard statistical performance evaluation measures were employed to evaluate the performances of various ANN models developed. The results obtained in this study indicate that the LM algorithm outperformed both BFGS and GD algorithms.

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