scholarly journals Research on Opioid Propagation in the United States Based on Artificial Neural Network

2019 ◽  
Vol 1314 ◽  
pp. 012204
Author(s):  
Zexi Lin ◽  
Bin Wang ◽  
Dongze Wu
Keyword(s):  
Neural Network ◽  
United States ◽  
Artificial Neural Network ◽  
The United States ◽  
Artificial Neural

scholarly journals The United States’ Clothing Imports from Asian Countries along the Belt and Road: An Extended Gravity Trade Model with Application of Artificial Neural Network

2020 ◽  
Vol 12 (18) ◽  
pp. 7433
Author(s):  
Danny Chi Kuen Ho ◽  
Eve Man Hin Chan ◽  
Tsz Leung Yip ◽  
Chi-Wing Tsang
Keyword(s):  
Neural Network ◽  
United States ◽  
Artificial Neural Network ◽  
Developing Countries ◽  
Trade Flow ◽  
The United States ◽  
Extended Gravity ◽  
Belt And Road ◽  
Artificial Neural ◽  
The Belt And Road

In 2013, China announced the Belt and Road Initiative (BRI), which aims to promote the connectivity of Asia, Europe, and Africa and deepen mutually beneficial economic cooperation among member countries. Past studies have reported a positive impact of the BRI on trade between China and its partner countries along the Belt and Road (B&R). However, less is known about its effect on the sectoral trade between the B&R countries and countries that show little support of the BRI. To address that gap, this study examines the changing patterns of clothing imports by the United States (US) from China and 14 B&R countries in Asia. An extended gravity model with a policy variable BRI is built to explain bilateral clothing trade flow. A panel regression model and artificial neural network (ANN) are developed based on the data collected from 1998 to 2018 and applied to predict the trade pattern of 2019. The results show a positive effect of the BRI on the clothing exports of some Asian developing countries along the B&R to the US and demonstrate the superior predictive power of the ANN. More research is needed to examine the balance between economic growth and the social and environmental sustainability of developing countries and to apply more advanced machine learning algorithms to examine global trade flow under the BRI.

scholarly journals Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

2019 ◽  
Vol 9 (21) ◽  
pp. 4690 ◽  
Author(s):  
Fatemeh Davoudi Kakhki ◽  
Steven A. Freeman ◽  
Gretchen A. Mosher
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
The United States ◽  
Work Environments ◽  
Safety Risk ◽  
Grain Elevators ◽  
Manufacturing Operations ◽  
Safety Risks ◽  
Artificial Neural

The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to severe injuries, due to the nature of the activities and workplace. Safety incidents in agro-manufacturing operations generally arise from a combination of factors, rather than a single cause, therefore, research on occupational incidents must look deeper into identifying the underlying causes, through the application of advanced analyses methods. In occupational safety, it is possible to estimate and predict probability of safety risks through developing artificial neural network predictive models. Due to the significance of safety risk assessment in the design and prioritization of effective prevention measures, this study aimed at classifying and predicting causes of occupational incidents in grain elevator agro-manufacturing operations in the Midwest region of the United States. Workers’ compensation claims data, from 2008 to 2016, were utilized for training multilayer perceptron (MLP) and radial basis function (RBF) neural networks. Both MLP and RBF models could predict the probability of safety risks with a high overall accuracy of 60%, 61%. Based on values of AUC (area under the curve) from the ROC (receiving operating charts), both models predicted the probability of individual safety risks with a high accuracy rate of between 71.5% and 99.2%. In addition, sensitivity analysis showed that nature of injury is the most significant determinant of safety risks probability, along with type of injury. The novelty of this study is the use of the artificial neural network methodology to analyze multi-level causes of occupational incidents as the sources of safety risks in bulk storage facilities. The results confirm that artificial neural networks are useful in safety risk estimation, and identifying the incidents’ risk factors. The implementation of safety measures in grain elevators can help in preventing occupational injuries, saving lives, and reducing the occurrence and severity of such incidents in industrial work environments.

Transport Energy Demand Modeling of the United States Using Artificial Neural Networks and Multiple Linear Regressions

2014 ◽  
Author(s):  
Arash Kialashaki ◽  
John Reisel
Keyword(s):  
Neural Network ◽  
United States ◽  
Energy Demand ◽  
Regression Models ◽  
The United States ◽  
Transportation Energy ◽  
Independent Variables ◽  
Transportation Sector ◽  
The Future ◽  
Artificial Neural

In 2009, the transportation sector was the second largest consumer of primary energy in the United States, following the electric power sector and followed by the industrial, residential, and commercial sectors. The pattern of energy use varies by sector. For example, petroleum provides 96% of the energy used for transportation but its share is much less in other sectors. While the United States consumes vast quantities of energy, it has also pledged to cut its greenhouse gas emissions by 2050. In order to assist in planning for future energy needs, the purpose of this study is to develop a model for transport energy demand that incorporates past trends. This paper describes the development of two types of transportation energy models which are able to predict the United States’ future transportation energy-demand. One model uses an artificial neural network technique (a feed-forward multilayer perceptron neural network coupled with back-propagation technique), and the other model uses a multiple linear regression technique. Various independent variables (including GDP, population, oil price, and number of vehicles) are tested. The future transport energy demand can then be forecast based on the application of the growth rate of effective parameters on the models. The future trends of independent variables have been predicted based on the historical data from 1980 using a regression method. Using the forecast of independent variables, the energy demand has been forecasted for period of 2010 to 2030. In terms of the forecasts generated, the models show two different trends despite their performances being at the same level during the model-test period. Although, the results from the regression models show a uniform increase with different slopes corresponding to different models for energy demand in the near future, the results from ANN express no significant change in demand in same time frame. Increased sensitivity of the ANN models to the recent fluctuations caused by the economic recession may be the reason for the differences with the regression models which predict based on the total long-term trends. Although a small increase in the energy demand in the transportation sector of the United States has been predicted by the models, additional factors need to be considered regarding future energy policy. For example, the United States may choose to reduce energy consumption in order to reduce CO2 emissions and meet its national and international commitments, or large increases in fuel efficiency may reduce petroleum demand.

Real-time prediction of Litho-facies from drilling data using an Artificial Neural Network: A comparative field data study with optimizing algorithms

2021 ◽  
pp. 1-22
Author(s):  
Romy Agrawal ◽  
Aashish Malik ◽  
Robello Samuel ◽  
Amit Saxena
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Gamma Ray ◽  
The United States ◽  
Stochastic Gradient Descent ◽  
Moment Estimation ◽  
Drilling Parameters ◽  
Proposed Model ◽  
Artificial Neural

Abstract The lithology of the formation is known to affect the drilling operation. Litho-facies help in the quantification of the formation properties, which optimizes the drilling parameters. The proposed work uses the artificial neural network algorithm and an optimizer to develop a working model for predicting the lithology of any formation within the study area in real-time. The proposed model is trained using the formation data comprising 15-dependent variables from the Eagleford region of the United States of America. It builds a method for measuring or forecasting litho-facies in real-time when drilling through a formation. It uses general drilling parameters for better precision, including Rate of Penetration, Rotation per minute, Surface Torque, Differential Pressure, Gamma Ray Correlation, and a d-exponent correlation function. The proposed model compares and assesses various first-order optimization algorithm's efficiency, such as Adaptive Moment Estimation, Adaptive Gradient, Root Mean Square Propagation, and Stochastic Gradient Descent with traditional artificial neural network in quantitative litho-facies detection. The model can predict the complex lithology for vertical/inclined/horizontal wellbores in real-time, making it a novel algorithm in the industry. The developed algorithm illustrates an accuracy of 86 % using Adam optimizer when tested with the existing data and improves as the model is trained with more data.

Development of an Artificial Neural Network-Based Procedure for the Verification of Traffic Speed Deflectometer Measurements

2021 ◽  
pp. 036119812110057
Author(s):  
Hossam Abohamer ◽  
Mostafa A. Elseifi ◽  
Zia U. A. Zihan ◽  
Zhong Wu ◽  
Nathan Kebede ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
The United States ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Measuring Device ◽  
Spatial Coverage ◽  
Artificial Neural ◽  
Traffic Speed ◽  
Artificial Neural Network Ann

Since the 1980s, the falling weight deflectometer (FWD) has been the primary deflection-measuring device in the United States to evaluate the structural conditions of in-service pavements. However, the stop and go nature of the FWD limits its application at the network level. In the early 2000s, the traffic speed deflectometer (TSD) was introduced as an alternate deflection-measuring device for network-level applications. TSD collects deflection measurements while traveling at traffic speed, which provides improved spatial coverage and no traffic disturbance. The verification of TSD measurements is of great interest as many agencies move toward widespread implementation. This study aims at developing a reliable and straightforward procedure for the verification of TSD measurements using limited FWD measured deflection measurements. The verification procedure employs a trained artificial neural network (ANN) model to shift TSD deflections to their corresponding FWD deflections. The ANN model was trained and verified based on FWD and TSD measurements from two deflection-testing programs. The developed model accurately predicted FWD measurements with a coefficient of determination (R2) of 0.994. The suitability of the proposed verification procedure was evaluated using statistical and engineering-based measures and showed acceptable accuracy. Results also validated that the proposed method could be used to verify TSD measurements before its use for conducting deflection measurements at the network level.

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