scholarly journals Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ying Yu ◽  
Yirui Wang ◽  
Shangce Gao ◽  
Zheng Tang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Rapid Development ◽  
Seasonal Trend ◽  
Moving Averages ◽  
Forecasting Methods ◽  
Tourism Economy ◽  
The Impact ◽  
D Model

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.

scholarly journals Are Chinese Residents Willing to Recycle Express Packaging Waste? Evidence from a Bayesian Regularized Neural Network Model

2018 ◽  
Vol 10 (11) ◽  
pp. 4152 ◽  
Author(s):  
Feng Dong ◽  
Yifei Hua
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Rapid Development ◽  
Situational Factors ◽  
Packaging Waste ◽  
The Neural Network ◽  
Selection Of Variables ◽  
The Impact ◽  
Recycling Behavior

While enriching people’s lives, the rapid development of online shopping has posed a severe challenge to the environment. Questionnaires focusing on the intention to recycle packaging waste are designed. These questionnaires contain first-level variables such as recycling behavior attitude, recycling behavior cognition, situational factors, historical recycling behavior, and recycling behavior intention. With the collected questionnaire data, a regression analysis is first conducted on the selection of variables and the effect of variable prediction. After ensuring the validity of the variables, 15 second-level variables are extracted into eight principal components using principal component analysis. These components serve as input to a Bayesian regularized neural network. Subsequently, a three-layer (8-15-1) neural network model is constructed; the trained neural network model achieves a high degree of fit between the predicted and measured values of the test set, thus further proving the rationality of the selected variables and the neural network model. Finally, this study uses the connection weights matrix of the neural network model and the Garson formula to analyze in depth the specific impact of each second-level variable on the intention to recycle packaging waste. Note that given the particularity of packaging waste recycling behavior, the impact on social norms, recycling behavior knowledge, values, and publicity on behavioral intentions in second-level variables is different from that obtained in similar previous studies.

Market orientation and performance: modelling a neural network

2009 ◽  
Vol 43 (3/4) ◽  
pp. 421-437 ◽  
Author(s):  
Manuela Silva ◽  
Luiz Moutinho ◽  
Arnaldo Coelho ◽  
Alzira Marques
Keyword(s):  
Neural Network ◽  
Market Orientation ◽  
Network Model ◽  
Neural Network Model ◽  
Performance Modelling ◽  
Network Modelling ◽  
Content Type ◽  
Market Intelligence ◽  
And Performance ◽  
The Impact

PurposeThis paper aims to investigate the impact of market orientation (MO) on performance using a neural network model in order to find new linkages and new explanations for this relationship.Design/methodology/approachThis investigation is based on a survey data collection from a sample of 192 Portuguese companies. A neural network model has been developed to identify the effects of each dimension of MO on each dimension of performance.FindingsRelationship among MO and performance was corroborated but MO's impact is poor and based on its first dimension, market intelligence generation.Research limitations/implicationsFurther research in this field should be conducted using other tools offered by neural network modelling.Practical implicationsManagers should give more attention to cross‐functional co‐ordination in order to improve market intelligence dissemination and responsiveness and, thus, global performance.Originality/valueThe paper presents the development of a neural network model to analyse this relationship.

Artificial intelligence to link environmental endocrine disruptors (EEDs) with bone diseases

2021 ◽  
Author(s):  
Khaled A. Al-Utaibi ◽  
M. Idrees ◽  
Ayesha Sohail ◽  
Fatima Arif ◽  
Alessandro Nutini ◽  
...  
Keyword(s):  
Neural Network ◽  
Bone Mass ◽  
Network Model ◽  
Neural Network Model ◽  
The United States ◽  
Environmental Stresses ◽  
Bone Diseases ◽  
Mineral Density ◽  
Weather Situation ◽  
The Impact

Our endocrine system is not only complex, but is also enormously sensitive to the imbalances caused by the environmental stressors, extreme weather situation, and other geographical factors. The endocrine disruptions are associated with the bone diseases. Osteoporosis is a bone disorder that occurs when bone mineral density and bone mass decrease. It affects women and men of all races and ethnic groups, causing bone weakness and the risk of fractures. Environmental stresses are referred to physical, chemical, and biological factors that can impact species productivity. This research aims to examine the impact of environmental stresses on bone diseases like osteoporosis and low bone mass (LBM) in the United States (US). For this purpose, we use an artificial neural network model to evaluate the correlation between the data. A multilayer neural network model is constructed using the Levenberg–Marquardt training algorithm, and its performance is evaluated by mean absolute error and coefficient of correlation. The data of osteoporosis and LBM cases in the US are divided into three groups, including gender group, age group, and race/ethnicity group. Each group shows a positive correlation with environmental stresses and thus the endocrinology.

Adaptive network architecture and firefly algorithm for biogas heating model aided by photovoltaic thermal greenhouse system

2018 ◽  
Vol 29 (7) ◽  
pp. 1073-1097 ◽  
Author(s):  
Gurinderpal Singh ◽  
VK Jain ◽  
Amanpreet Singh
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Architecture ◽  
Error Function ◽  
Learning Performance ◽  
Heating Efficiency ◽  
Heating Model ◽  
The Impact ◽  
Self Adaptive

The photovoltaic thermal greenhouse system highly supports the production of biogas. The system’s prime advantage is biogas heating and crop drying through varied directions of air flow. Further, it diminishes the upward loss of the system. This paper aims to model a practical greenhouse system for obtaining the precise estimation of the heating efficiency, given by the solar radiance. The simulation model adopts the self-adaptive firefly neural network model that applies on known experimental data. Therefore, the error function between the model outcome and the experimental outcome is substantially minimized. The performance analysis involves an effective comparative study on the root mean square error between the adopted self-adaptive firefly neural network model and the conventional models such as Levenberg–Marquardt neural network and firefly neural network. Later, the impact of self-adaptiveness, FF update and learning performance on attaining the knowledge regarding the characteristics of SAFF algorithm is analysed to yield better performance.

The Applications of Neural Networks in the Engineering Cost of Transmission Line

2013 ◽  
Vol 319 ◽  
pp. 485-490
Author(s):  
Hong Fei Sun ◽  
Qing Song Tang ◽  
Yu Ling Li
Keyword(s):  
Neural Network ◽  
Transmission Line ◽  
Network Model ◽  
Neural Network Model ◽  
Cost Estimation ◽  
Rapid Development ◽  
Electric Power Industry ◽  
Linear Mapping ◽  
Main Body ◽  
Estimation Model

With the rapid development of the electric power industry in recent years, the strengthening of the power construction market and the diversification of the main body of power investment, there appears a prominent question in front of the project owners——How to control and reduce construction costs? There are many methods to estimate the cost quickly and accurately. Among the common methods and some new ways which have appeared in recent years, people can find about seven types out of them, in which, neural network model is known for its versatility and adaptability. It does not exclude new sample. On the contrary, it improves its ability to generalize and forecast with the increasing number of samples. Therefore this paper establish a cost estimation model by introducing neural network which is based on the optimization of genetic algorithm, and expresses the relationship implied in the interior of data by using the network topology and parameters by studying a large number of samples so as to fit the conventional non-linear mapping relationship between the amount and cost of a transmission line project. The results show that the artificial neural network model has a significant effect on the project cost estimation. The introduction of neural network model will certainly promote the development of informatization of power project costs management.

scholarly journals Air Pollution Concentration Forecast Method Based on the Deep Ensemble Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Canyang Guo ◽  
Genggeng Liu ◽  
Chi-Hua Chen
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Rapid Development ◽  
Pearson Correlation ◽  
Stochastic Gradient Descent ◽  
Prediction Methods ◽  
Pollution Concentration

The global environment has become more polluted due to the rapid development of industrial technology. However, the existing machine learning prediction methods of air quality fail to analyze the reasons for the change of air pollution concentration because most of the prediction methods take more focus on the model selection. Since the framework of recent deep learning is very flexible, the model may be deep and complex in order to fit the dataset. Therefore, overfitting problems may exist in a single deep neural network model when the number of weights in the deep neural network model is large. Besides, the learning rate of stochastic gradient descent (SGD) treats all parameters equally, resulting in local optimal solution. In this paper, the Pearson correlation coefficient is used to analyze the inherent correlation of PM2.5 and other auxiliary data such as meteorological data, season data, and time stamp data which are applied to cluster for enhancing the performance. Extracted features are helpful to build a deep ensemble network (EN) model which combines the recurrent neural network (RNN), long short-term memory (LSTM) network, and gated recurrent unit (GRU) network to predict the PM2.5 concentration of the next hour. The weights of the submodel change with the accuracy of them in the validation set, so the ensemble has generalization ability. The adaptive moment estimation (Adam) an algorithm for stochastic optimization is used to optimize the weights instead of SGD. In order to compare the overall performance of different algorithms, the mean absolute error (MAE) and mean absolute percentage error (MAPE) are used as accuracy metrics in the experiments of this study. The experiment results show that the proposed method achieves an accuracy rate (i.e., MAE=6.19 and MAPE=16.20%) and outperforms the comparative models.

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