scholarly journals Adaptive Modeling of Prediction of Telecommunications Network Throughput Performances in the Domain of Motorway Coverage

2021 ◽  
Vol 11 (8) ◽  
pp. 3559
Author(s):  
Milorad K. Banjanin ◽  
Mirko Stojčić ◽  
Dejan Drajić ◽  
Zoran Ćurguz ◽  
Zoran Milanović ◽  
...  
Keyword(s):  
Coefficient Of Determination ◽  
Adaptive Model ◽  
Average Throughput ◽  
Final Model ◽  
Data Throughput ◽  
Average Accuracy ◽  
Telecommunications Network ◽  
Mlp Model ◽  
Average User ◽  
Mlr Model

The main goal of this paper is to create an adaptive model based on multilayer perceptron (MLP) for prediction of average downlink (DL) data throughput per user and average DL data throughput per cell within an LTE network technology and in a geo-space that includes a segment of the Motorway 9th January with the access roads. The accuracy of model prediction is estimated based on relative error (RE). With multiple trainings and testing of 30 different variants of the MLP model, with different metaparameters the final model was chosen whose average accuracy for the Cell Downlink Average Throughput variable is 89.6% (RE = 0.104), while for the Average User Downlink Throughput variable the average accuracy is 88% (RE = 0.120). If the coefficient of determination is observed, the results showed that the accuracy of the best selected prediction model for the first variable is 1.4% higher than the accuracy of the prediction of the selected model for the second dependent variable. In addition, the results showed that the performance of the MLP model expressed over R2 was significantly better compared to the reference multiple linear regression (MLR) model used.

Determination of number of check dams by artificial neural networks in arid regions of Iran

2015 ◽  
Vol 72 (6) ◽  
pp. 952-959 ◽  
Author(s):  
Seyed Ali Asghar Hashemi ◽  
Hamed Kashi
Keyword(s):  
Curve Number ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Effective Parameters ◽  
Check Dams ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Mlp Model

An artificial neural network (ANN) model with six hydrological factors including time of concentration (TC), curve number, slope, imperviousness, area and input discharge as input parameters and number of check dams (NCD) as output parameters was developed and created using GIS and field surveys. The performance of this model was assessed by the coefficient of determination R2, root mean square error (RMSE), values account and mean absolute error (MAE). The results showed that the computed values of NCD using ANN with a multi-layer perceptron (MLP) model regarding RMSE, MAE, values adjustment factor (VAF), and R2 (1.75, 1.25, 90.74, and 0.97) for training, (1.34, 0.89, 97.52, and 0.99) for validation and (0.53, 0.8, 98.32, and 0.99) for test stage, respectively, were in close agreement with their respective values in the watershed. Finally, the sensitivity analysis showed that the area, TC and curve number were the most effective parameters in estimating the number of check dams.

PREDICTION THE AVERAGE ANNUAL CONCENTRATION OF PHOSPHATE IN RIVERS USING ANN AND MLR MODELS

2018 ◽  
Vol 28 (4) ◽  
pp. 1265-1269
Author(s):  
Lidija Stamenković
Keyword(s):  
Water Quality ◽  
Sustainable Development ◽  
Regional Cooperation ◽  
Model Performance ◽  
Anthropogenic Sources ◽  
Coefficient Of Determination ◽  
The European Union ◽  
Test Dataset ◽  
Water Pollutants ◽  
Mlp Model

Clean and quality water is one of the main aims in achieving the seventeen adopted Sustainable Development Goals. That means taking legislative measures at national and international level for the preservation of water quality, prevention of pollution and continuous monitoring. According to global and regional cooperation which is stated by numerous legal regulations, all member countries have obligation to monitoring water quality and submit annual report. For that reason it is very important to obtain accurate and precise data of all monitored water pollutants. In developing countries, because of poor and underdeveloped environmental infrastructure, obtained data for pollutants could have a certain percentage of uncertainty. In order to avoid that problem it is very important the existence of a larger number of models to estimate concentration of water pollutants, including concentration of phosphate. Due to the increasing influence of anthropogenic sources on the content of phosphorus, first in the soil and then in water sources, and negative effects caused by the eutrophication process, it is crucial to monitor the state of water in terms of the presence of phosphate. In the present study, in order to obtain one optimal model to predict the average annual concentration of phosphate in rivers was used two different approaches. In the first approach, was used artificial neural network (ANN), two different ANN architecture: Multilayer Perceptron (MLP) and Radial Basis Function (RBF). In the second approach were used two type of Multiple Linear Regression (MLR) technique, simultaneous and stepwise MLR. Available data used in this study were obtained from Eurostat-the statistical office of the European Union, for 19 European countries for the period from 2004 to 2012. For all created models was used the same dataset. As input variables were used sustainable development indicators, in total 10. All selected inputs were chosen on the basis of their influence on phosphate concentration in rivers and also reviewing of adequate literature. As model performance indicators in this study was used Mean Absolute Error (MAE) and coefficient of determination (R2). Results of all created models first at all showed that comparing two ANN models, better performances on training and test dataset shows MLP model. On test dataset, MLP model gives satisfactory prediction with value of coefficient of determination 0,657. On the other side, developed MLR models show significantly poorer results. Taking into account performances of created models, it is clearly that MLP model shows the best predictive results. On the basis of results of MLP model, could be recognized that it can be an alternative model for prediction annual concentration of phosphate in rivers, and significant tool in water pollutant monitoring.

scholarly journals Developing a fuzzy logic model for predicting soil infiltration rate based on soil texture properties

Water SA ◽  
2019 ◽  
Vol 45 (3 July) ◽  
Author(s):  
Ahmed Z Dewidar ◽  
Hussein Al-Ghobari ◽  
Abed Alataway
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Model ◽  
Clay Content ◽  
Infiltration Rate ◽  
Influential Factor ◽  
Coefficient Of Determination ◽  
Soil Infiltration ◽  
Infiltration Process ◽  
Soil Infiltration Rate ◽  
Mlr Model

The prediction of the soil infiltration rate is advantageous in hydrological design, watershed management, irrigation, and other agricultural studies. Various techniques have been widely used for this with the aim of developing more accurate models; however, the improvement of the prediction accuracy is still an acute problem faced by decision makers in many areas. In this paper, an intelligent model based on a fuzzy logic system (FLS) was developed to obtain a more accurate predictive model for the soil infiltration rate than that generated by conventional methods. The input variables that were considered in the fuzzy model included the silt and clay contents. The developed fuzzy model was tested against both the observed data and multiple linear regression (MLR). The comparison of the developed fuzzy model and MLR model indicated that the fuzzy model can simulate the infiltration process quite well. The coefficient of determination, root mean square error, mean absolute error, model efficiency, and overall index of the fuzzy model were 0.953, 1.53, 1.28, 0.953, and 0.954, respectively. The corresponding MLR model values were 0.913, 2.37, 1.92, 0.913, and 0.914, respectively. The sensitivity results indicated that the clay content is the most influential factor when the FLS-based modelling approach is used for predicting the soil infiltration rate.

scholarly journals Improving Surface Melt Estimation over Antarctica Using Deep Learning: A Proof-of-Concept over the Larsen Ice Shelf

2021 ◽  
Author(s):  
Zhongyang Hu ◽  
Peter Kuipers Munneke ◽  
Stef Lhermitte ◽  
Maaike Izeboud ◽  
Michiel van den Broeke
Keyword(s):  
Deep Learning ◽  
Climate Model ◽  
High Surface ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Climate Modelling ◽  
Ice Shelf ◽  
Surface Melt ◽  
Mlp Model ◽  
Larsen Ice Shelf

Abstract. Accurately estimating surface melt volume of the Antarctic Ice Sheet is challenging, and has hitherto relied on climate modelling, or on observations from satellite remote sensing. Each of these methods has its limitations, especially in regions with high surface melt. This study aims to demonstrate the potential of improving surface melt simulations by deploying a deep learning model. A deep-learning-based framework has been developed to correct surface melt from the regional atmospheric climate model version 2.3p2 (RACMO2), using meteorological observations from automatic weather stations (AWSs), and surface albedo from satellite imagery. The framework includes three steps: (1) training a deep multilayer perceptron (MLP) model using AWS observations; (2) correcting moderate resolution imaging spectroradiometer (MODIS) albedo observations, and (3) using these two to correct the RACMO2 surface melt simulations. Using observations from three AWSs at the Larsen B and C Ice Shelves, Antarctica, cross-validation shows a high accuracy (root mean square error = 0.95 mm w.e. per day, mean absolute error = 0.42 mm w.e. per day, and coefficient of determination = 0.95). Moreover, the deep MLP model outperforms conventional machine learning models (e.g., random forest regression, XGBoost) and a shallow MLP model. When applying the trained deep MLP model over the entire Larsen Ice Shelf, the resulting, corrected RACMO2 surface melt shows a better correlation with the AWS observations for two out of three AWSs. However, for one location (AWS 18) the deep MLP model does not show improved agreement with AWS observations, likely due to the heterogeneous drivers of melt within the corresponding coarse resolution model pixels. Our study demonstrates the opportunity to improve surface melt simulations using deep learning combined with satellite albedo observations. On the other hand, more work is required to refine the method, especially for complicated and heterogeneous terrains.

scholarly journals Prediction English word concreteness ratios by their multidimensional Perceptual and action strength norms

2021 ◽  
Author(s):  
Mohsen Dolatabadi
Keyword(s):  
Missing Values ◽  
Cross Validation ◽  
English Word ◽  
Optimum Number ◽  
Final Model ◽  
Word Norms ◽  
Word Concreteness ◽  
Participant Rating ◽  
Fold Cross Validation ◽  
Mlr Model

Many dataset resulted by participant rating for word norms also concreteness ratio areavailable. However infrequent word and none words concreteness rank is rare. Here we usedLancaster sensory motor words’ norms , to predict word concreteness ratios of Brysbaerdataset. After removing missing values and collinear variables, we employed a SW-MLR forchoosing optimum number of norms to make a prediction MLR model. Finally we validate ourmodel using 10-fold cross-validation. The final model could predict concreteness by RMSE0.5123 and R-square 0.7262.

scholarly journals Estimating aboveground biomass for Eucalyptus saligna Sm. and Eucalyptus camaldulensis Dehn in the center region of Costa Rica

2014 ◽  
Vol 11 (27) ◽  
pp. 22 ◽  
Author(s):  
Richard Navarro-Camacho ◽  
Edwin Esquivel-Segura ◽  
Elemer Briceño-Elizondo ◽  
Dagoberto Arias-Aguilar
Keyword(s):  
Costa Rica ◽  
Aboveground Biomass ◽  
Accurate Determination ◽  
Eucalyptus Camaldulensis ◽  
Coefficient Of Determination ◽  
Eucalyptus Saligna ◽  
Final Model ◽  
Growth Production ◽  
Change Mitigation

The contribution of forests as climate change mitigation sinks through growth production calls for the accurate determination of their biomass production, therefore it is necessary to to evaluate variables such as weight of dry leaves, diameter at breast height (DBH) , diameter at stump height (DSH) and total height and their effect on individual aboveground biomass. The analysis was conducted at theTechnological Institute of Costa Rica (TEC) located in the province of Cartago- Sampling consisted on 31 sampling of<em> Eucalyptus saligna</em> and <em>Eucalyptus camaldulensis</em>, in order to estimate a linear regression model to predictaverage tree biomass. The final model obtained for biomass was Biomasa = e^2,6915+2,1338*√DSHi, with a coefficient of determination of 0,9061. We recommend a study to help determine the biomass and soil organic matter to provide a complete inventory of biomass for a given area.

PENGARUH PERBAIKAN BERKESINAMBUNGAN TERHADAP KINERJA KARYAWAN (STUDI KASUS DI PT.RENTANG BUANA NIAGAMAKMUR TASIKMALAYA)

2019 ◽  
Vol 3 (1) ◽  
pp. 31-41
Author(s):  
Sri Sudiarti
Keyword(s):  
Data Analysis ◽  
Continuous Improvement ◽  
Research Method ◽  
Employee Performance ◽  
Sampling Technique ◽  
Positive Influence ◽  
Performance Criteria ◽  
Coefficient Of Determination ◽  
Analysis Techniques ◽  
Simple Regression

The objectives of this research are to know and to analyze about the effect of Continuous Improvement on the performance of employees at PT. Rentang Buana Niagamakmur (PT.RBN) Tasikmalaya. Research method which applied in this research is survey research method, while data collecting technique is done by through questionaire. Sampling technique applies sample is accidental sampling technique and the size sample is 55 respondents. Data analysis techniques used in the study is simple regression technique, analysis of the coefficient of determination  and t test. The results showed that the Continuous Improvement  including both criteria, including employee performance criteria, as well as Continuous Improvement  has a positive influence on employee performance of 76,4% in PT . Rentang Buana Niagamakmur (PT.RBN) Tasikmalaya.

FAKTOR-FAKTOR YANG MEMPENGARUHI KUALITAS AUDIT

2019 ◽  
Vol 5 (1) ◽  
pp. 50-63
Author(s):  
Heru Heryanto ◽  
Nur Laela ◽  
Riana R Dewi
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Audit Quality ◽  
Positive Influence ◽  
T Test ◽  
Coefficient Of Determination ◽  
Analysis Tool ◽  
Validity And Reliability ◽  
Test Analysis ◽  
Analysis Technique

This study aims to determine the significance of the influence of competence, independence, professionalism, auditor experience, accountability, and auditor's knowledge of audit quality. This study uses a questionnaire with a population and sample, namely all auditors who work at the Public Accounting Office (KAP) in the Special Region of Yogyakarta and Surakarta. Sampling techniques using Convenience Sampling with a sample of 61 respondents. The data used in this study is a questionnaire using a Likert scale 1 to 5. The data analysis technique used in this study is multiple linear regression using the SPSSprogram for Windows. The analysis tool in this study using validity and reliability, the classical assumption (normality test, multicollinearity, heteroscedasticity test and autocorrelation test) while the data were analyzed using multiple linear regression test, t test, F test and the coefficient of determination (R2).Based on the results of the t-test analysis performed, it shows that there is a positive influence of competence, independence, professionalism, auditor experience, accountability, and auditor's knowledge of audit quality and simultaneously competency, independence, professionalism, auditor experience, accountability, and auditor knowledge variables affect quality audit

IMPACT OF AIS AND STRESSED OUT AN EMPLOYEES TO THEIR PERFORMANCE AT PT EFRATA RETAILINDO

2019 ◽  
Vol 5 (1) ◽  
pp. 22-30
Author(s):  
Wiwit Ayu Retno Sari ◽  
Suhendro Suhendro ◽  
R. Riana Dewi
Keyword(s):  
Information Systems ◽  
Work Stress ◽  
Quantitative Research ◽  
Linear Regression Analysis ◽  
Accounting Information ◽  
Multiple Linear Regression Analysis ◽  
Primary Data ◽  
Coefficient Of Determination ◽  
Accounting Information System ◽  
Accounting Information Systems

This research aims to test the influence of accounting information system and work stress on performance of employees of PT Efrata Retailindo. The type of research used in this research is quantitative research. The source of the data in the research is primary data. The population in this study are all employees of PT Efrata Retailindo totalling 47 people. Sampling techniques in the study using a purposive sample. While the data collection method used is to use the questionnaire to all employees of PT Efrata Retailindo. Data analysis techniques using multiple linear regression analysis. Based on the results of the study it can be concluded that work stress had no effect on performance of employees of PT Efrata Retailindo, while information systems accounting effect on the performance of the employees of PT Efrata Retailindo. The value of the coefficient of determination (R2) amounting to 0.106. This indicates that variansi on a variable performance practice undertaken by the company PT Efrata Retailindo of 10.6% can be explained by work stress variables and accounting information systems, while the remaining 89.4% explained by other factors outside the researched.

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