scholarly journals Artificial neural networks in modelling seasonal tourism demand - case study of Croatia

2020 ◽  
Vol 8 (1) ◽  
pp. 19-39
Author(s):  
Maja Gregorić ◽  
Tea Baldigara
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Test Procedure ◽  
Model Performance ◽  
Percentage Error ◽  
Accuracy Evaluation ◽  
Tourism Demand ◽  
Forecasting Accuracy ◽  
Artificial Neural

The purpose of this paper is to design an artificial neural network in the attempt to define the data generating process of the number of German tourist arrivals in Croatia considering the strong seasonal character of empirical data. The presence of seasonal unit roots in tourism demand determinants is analysed using the approach developed by Hylleberg, Engle, Granger and Yoo – Hegy test. The study is based on seasonality analysis and Artificial Neural Networks approach in building a model which intend to describe the behaviour of the German tourist flows to Croatia. Different neural network architectures were trained and tested, and after the modelling phase, the forecasting accuracy and model performances were analysed. Model performance and forecasting accuracy evaluation was tested using the mean absolute percentage error. Based on the augmented HEGY test procedure it can be concluded the German tourist arrivals to the Republic of Croatia have nonstationary behaviour associated with the zero frequency and seasonal frequency. Taking this into consideration, in the analysis of the phenomenon it is necessary to consider its seasonal character. Given the importance of the tourism for Croatian economic development, the research results could be useful, for both, researchers and practitioners, in the process of planning and routing the future Croatian hotel industry development and improvement of business performances.

Prediction of 2-D Lift Motions Using Artificial Neural Networks: An Evolutionary Approach

2005 ◽  
Vol 49 (14) ◽  
pp. 1405-1409
Author(s):  
Miguel A. Perez ◽  
Maury A. Nussbaum
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Model Performance ◽  
Human Movement ◽  
Two Dimensional ◽  
Modeling Paradigm ◽  
Neural Network Structure ◽  
Artificial Neural

Movement prediction is an important aspect of human simulation, where more efficient and accurate models are needed. Artificial neural networks could potentially serve as a modeling option in this realm. This investigation evaluates the performance of a particular artificial neural network structure in modeling sagittally symmetric two-dimensional lifting and lowering movements. Model performance was evaluated using three training datasets, each consisting of distinct representation levels of the overall dataset. Results are discussed in terms of their practical meaning, and suggestions for future improvements in the modeling scheme are provided. Overall, artificial neural networks show promise as a modeling paradigm for the prediction of human movement.

PERBANDINGAN ARIMA DAN ARTIFICIAL NEURAL NETWORKS DALAM PERAMALAN JUMLAH POSITIF COVID-19 DI DKI JAKARTA

2021 ◽  
Vol 10 (3) ◽  
pp. 288-301
Author(s):  
Tri Wahyuni ◽  
Indahwati Indahwati ◽  
Kusman Sadik
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Root Mean Square Error ◽  
Moving Average ◽  
Percentage Error ◽  
Cumulative Number ◽  
Mean Square ◽  
Forecasting Accuracy ◽  
Autoregressive Integrated Moving Average ◽  
Artificial Neural

DKI Jakarta is the center of the spread of Covid-19. This is indicated by the higher cumulative number of Covid-19 positive in DKI Jakarta compared to other provinces. The high number of cases in DKI Jakarta is a concern for all groups, so it is necessary to do forecasting to predict the number of Covid-19 positive in the next period. Accurate forecasting is needed to get better results. This study compares the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) methods in predicting the number of Covid-19 positive in DKI Jakarta. Forecasting accuracy is calculated using the value of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and correlation. The results show that the best model for forecasting the number of Covid-19 positive in DKI Jakarta is ARIMA(0,1,1) with drift, with a MAPE value of 15.748, an RMSE of 268.808, and the correlation between the forecast value and the actual value of 0.845. Forecasting using ARIMA(0,1,1) with drift and BP(3,10,1) models produces the best forecast for the long forecasting period of the next six weeks.

scholarly journals Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Petr Maca ◽  
Pavel Pech
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Model ◽  
Neural Network Model ◽  
Multilayer Perceptron ◽  
Standardized Precipitation Index ◽  
Model Performance ◽  
Drought Indices ◽  
Artificial Neural

The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.

scholarly journals Comparison of Artificial Neural Network and Box- Jenkins Models to Predict the Number of Patients with Hypertension in Kalar

2020 ◽  
Vol 33 (4) ◽  
pp. 110
Author(s):  
Layla A. Ahmed
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Percentage Error ◽  
Mean Square ◽  
Data Set ◽  
Intelligent Technique ◽  
Number Of Patients ◽  
Artificial Neural

    Artificial Neural Network (ANN) is widely used in many complex applications. Artificial neural network is a statistical intelligent technique resembling the characteristic of the human neural network.  The prediction of time series from the important topics in statistical sciences to assist administrations in the planning and make the accurate decisions, so the aim of this study is to analysis the monthly hypertension in Kalar for the period (January 2011- June 2018) by applying an autoregressive –integrated- moving average model  and artificial neural networks and choose the best and most efficient model for patients with hypertension in Kalar through the comparison between neural networks and Box- Jenkins models on a data set for predict. Comparisons between the models has been performed using Criterion indicator Akaike information Criterion, mean square of error,  root mean square of error, and mean absolute percentage error, concluding that the prediction for patients with hypertension by using artificial neural networks model is the best.

scholarly journals Modeliranje i prognoziranje broja zaposlenih u turizmu i hotelskoj industriji u Republici Hrvatskoj primjenom modela umjetnih neuronskih mreža

2020 ◽  
Vol 10 (2) ◽  
pp. 3-20
Author(s):  
Tea Baldigara
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Percentage Error ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Prognostic Power ◽  
Modelling And Forecasting

The paper investigates the performance and prognostic power of artificial neural network models in modelling and forecasting of time series of seasonal character. Models of artificial neural networks have been applied in modelling and forecasting the monthly total number of employees, the number of employed men and the number of employed women in the activity of providing accommodation services and preparing and serving food and beverages in the Republic of Croatia. The obtained modelling results have been compared with the results obtained by applying some of the traditionally used quantitative models in the analysis of seasonal time series, such as the Holt-Winters model of triple exponential smoothing and the seasonal multiplicative model of exponential trend. The evaluation of the performance and prognostic power of individual models was performed by comparing the average absolute and average absolute percentage error and the correlation coefficient between the actual and estimated values, and the predicted values were compared with the actual values. The evaluation of the obtained results showed that the selected model of acyclic multilayer perceptron is suitable for modelling and forecasting time series of seasonal character. The comparison of prognostic powers and actual and projected values of the number of employees suggests that the designed model of the artificial neural network is very reliable. This indicates that the models of artificial neural networks have great application potentials in the domain of modelling and forecasting of time series of a seasonal character.

scholarly journals The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 500
Author(s):  
László Keresztes ◽  
Evelin Szögi ◽  
Bálint Varga ◽  
Viktor Farkas ◽  
András Perczel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector ◽  
User Needs ◽  
Computational Technique ◽  
Linear Support Vector Machine ◽  
Β Sheets ◽  
Artificial Neural ◽  
Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

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.

Performance Estimation of Direct Methanol Fuel Cell Using Artificial Neural Network

2015 ◽  
Author(s):  
M. A. Rafe Biswas ◽  
Melvin D. Robinson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fuel Cell ◽  
Direct Methanol Fuel Cell ◽  
Cell Voltage ◽  
Operating Conditions ◽  
Direct Methanol ◽  
Artificial Neural ◽  
Methanol Fuel Cell

A direct methanol fuel cell can convert chemical energy in the form of a liquid fuel into electrical energy to power devices, while simultaneously operating at low temperatures and producing virtually no greenhouse gases. Since the direct methanol fuel cell performance characteristics are inherently nonlinear and complex, it can be postulated that artificial neural networks represent a marked improvement in performance prediction capabilities. Artificial neural networks have long been used as a tool in predictive modeling. In this work, an artificial neural network is employed to predict the performance of a direct methanol fuel cell under various operating conditions. This work on the experimental analysis of a uniquely designed fuel cell and the computational modeling of a unique algorithm has not been found in prior literature outside of the authors and their affiliations. The fuel cell input variables for the performance analysis consist not only of the methanol concentration, fuel cell temperature, and current density, but also the number of cells and anode flow rate. The addition of the two typically unconventional variables allows for a more distinctive model when compared to prior neural network models. The key performance indicator of our neural network model is the cell voltage, which is an average voltage across the stack and ranges from 0 to 0:8V. Experimental studies were carried out using DMFC stacks custom-fabricated, with a membrane electrode assembly consisting of an additional unique liquid barrier layer to minimize water loss through the cathode side to the atmosphere. To determine the best fit of the model to the experimental cell voltage data, the model is trained using two different second order training algorithms: OWO-Newton and Levenberg-Marquardt (LM). The OWO-Newton algorithm has a topology that is slightly different from the topology of the LM algorithm by the employment of bypass weights. It can be concluded that the application of artificial neural networks can rapidly construct a predictive model of the cell voltage for a wide range of operating conditions with an accuracy of 10−3 to 10−4. The results were comparable with existing literature. The added dimensionality of the number of cells provided insight into scalability where the coefficient of the determination of the results for the two multi-cell stacks using LM algorithm were up to 0:9998. The model was also evaluated with empirical data of a single-cell stack.

scholarly journals Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

2014 ◽  
Vol 38 (6) ◽  
pp. 1681-1693 ◽  
Author(s):  
Braz Calderano Filho ◽  
Helena Polivanov ◽  
César da Silva Chagas ◽  
Waldir de Carvalho Júnior ◽  
Emílio Velloso Barroso ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Kappa Index ◽  
Class Prediction ◽  
Soil Class ◽  
Serra Do Mar ◽  
The Neural Network ◽  
Artificial Neural ◽  
Local Geology

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Sign in / Sign up

Export Citation Format

Share Document