scholarly journals Statistical Analysis for Study of the Effect of Dark Clothing Color of Female Pedestrians on the Severity of Accident Using Machine Learning Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Seyed Mohsen Hosseinian ◽  
Vahid Najafi Moghaddam Gilani ◽  
Babak Mirbaha ◽  
Ali Abdi Kordani
Keyword(s):  
Road Safety ◽  
Prediction Accuracy ◽  
Weather Condition ◽  
Multiple Logistic Regression ◽  
Friedman Test ◽  
Independent Variables ◽  
Modeling Methods ◽  
Machine Learning Methods ◽  
Pedestrian Accidents ◽  
Mlp Model

The color and brightness of pedestrian clothing are among the factors that could increase the severity of their accidents due to the lack of visibility, especially at night. Today, as most Iranian females tend to wear hijab or dark clothing, the necessity of investigating female pedestrian accidents influenced by clothing color is important. Many studies have been performed to analyze the severity of pedestrian accidents, but a study has not yet been conducted to determine the effect of the dark clothing color of female pedestrians on the severity of accidents. Therefore, in this study, 12 independent variables affecting the severity of female pedestrian accidents such as clothing color, age, accident time, day, weather condition, education, pedestrian action, crossing facilities, crossing permit, job, road classification, and fault status were studied. Frequency analysis, Friedman test (FT), and Factor Analysis (FA) methods, as well as modeling methods of Multiple Logistic Regression (MLR) and Artificial Neural Networks (ANNs) using Multilayer Perceptron (MLP) and Radius Basis Function (RBF), were used. Results indicated that clothing color had a significant influence on pedestrian accidents, and chador and dark clothing color increased the probability of accidents, especially at night. The MLP model had a better prediction percentage than the rest, the prediction accuracy of which was 94.6%. Finally, safety solutions were presented according to the results to reduce pedestrian accidents and increase road safety.

Pengaruh Tekanan Waktu, Locus Of Control , Tindakan Supervisi, Dan Materialitas Terhadap Penghentian Prematur Atas Prosedur Audit Studi Kasus Pada Kantor Akuntan Publik Daerah Istimewa Yogyakarta Tahun 2015

2017 ◽  
Vol 24 (1) ◽  
pp. 35-53
Author(s):  
Sulastiningsih Sulastiningsih ◽  
Intan Ayu Candra
Keyword(s):  
Locus Of Control ◽  
Premature Termination ◽  
Sampling Methods ◽  
Time Pressure ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Test Reliability ◽  
Friedman Test ◽  
Independent Variables ◽  
Public Accountant

The purpose of this study is to prove: (1) Time pressure, locus of control, the action of supervision and materiality partially affect the premature termination of the audit procedures (2) Time pressure, locus of control, supervision and materiality simultaneously affect the premature termination on the audit procedures. This research was conducted in Public Accountant firm in Yogyakarta region of which total 12 samples of KAP, by distributing 105 questionnaires, and 57 questionnaires were returned (54%). 34 of the returned questionnaires can be processed (34%). The samples in this study were determined by using non-probability sampling, one of purposive sampling methods. Data analysis consisted of: (1) validity test, reliability test and classical assumption. The result showed that the instruments used are quite reliable and valid (2) multiple linear regression analysis. The results are (a) Some of independent variables partially affect premature termination of the audit procedure, while the action of supervision does not influence premature termination of audit procedures (b) All independent variables influence simultaneously to the premature termination of the audit procedures (c) All independent variables showed that as much as 55% it affects on premature termination of the audit procedures, the rest of it are influenced by other variables. (3) Friedman Test. The result shows that there are order of priority of audit procedures being terminated.

scholarly journals Modeling metallurgical responses of coal Tri-Flo separators by a novel BNN: a “Conscious-Lab” development

2021 ◽  
Author(s):  
Mehdi Alidokht ◽  
Samaneh Yazdani ◽  
Esmaeil Hadavandi ◽  
Saeed Chehreh Chelgani
Keyword(s):  
Laboratory Experiments ◽  
Scaling Up ◽  
Dense Medium ◽  
Support Vector ◽  
Coal Processing ◽  
Separation Device ◽  
Modeling Methods ◽  
Machine Learning Methods ◽  
Coal Washery ◽  
First Time

AbstractTri-flo cyclone, as a dense-medium separation device, is one of the most typical environmentally friendly industrial techniques in the coal washery plants. Surprisingly, no detailed investigation has been conducted to explore the effectiveness of tri-flo cyclone operating parameters on their representative metallurgical responses (yield and recovery). To fill this gap, this work for the first time in the coal processing sector is going to introduce a type of advanced intelligent method (boosted-neural network “BNN”) which is able to linearly and nonlinearly assess multivariable correlations among all variables, rank them based on their effectiveness and model their produced responses. These assessments and modeling were considered a new concept called “Conscious Laboratory (CL)”. CL can markedly decrease the number of laboratory experiments, reduce cost, save time, remove scaling up risks, expand maintaining processes, and significantly improve our knowledge about the modeled system. In this study, a robust monitoring database from the Tabas coal plant was prepared to cover various conditions for building a CL for coal tri-flo separators. Well-known machine learning methods, random forest, and support vector regression were developed to validate BNN outcomes. The comparisons indicated the accuracy and strength of BNN over the examined traditional modeling methods. In a sentence, generating a novel BNN within the CL concept can apply in various energy and coal processing areas, fill gaps in our knowledge about possible interactions, and open a new window for plants' fully automotive process.

scholarly journals Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang

2021 ◽  
Vol 13 (4) ◽  
pp. 769
Author(s):  
Xiaohang Li ◽  
Jianli Ding ◽  
Jie Liu ◽  
Xiangyu Ge ◽  
Junyong Zhang
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Accuracy ◽  
Lake Basin ◽  
Arid Areas ◽  
Annual Average ◽  
Machine Learning Methods ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

As an important evaluation index of soil quality, soil organic carbon (SOC) plays an important role in soil health, ecological security, soil material cycle and global climate cycle. The use of multi-source remote sensing on soil organic carbon distribution has a certain auxiliary effect on the study of soil organic carbon storage and the regional ecological cycle. However, the study on SOC distribution in Ebinur Lake Basin in arid and semi-arid regions is limited to the mapping of measured data, and the soil mapping of SOC using remote sensing data needs to be studied. Whether different machine learning methods can improve prediction accuracy in mapping process is less studied in arid areas. Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. The five different types of data are reconstructed by spatial data and divided into four spatial resolutions (10, 100, 300, and 500 m). Seven models are constructed and predicted by machine learning methods RF and Cubist. The results show that the prediction accuracy of RF model is better than that of Cubist model, indicating that RF model is more suitable for small areas in arid areas. Among the three data sources, Sentinel-1A has the highest SOC prediction accuracy of 0.391 at 10 m resolution under the RF model. The results of the importance of environmental variables show that the importance of Flow Accumulation is higher in the RF model and the importance of SLOP in the DEM derivative is higher in the Cubist model. In the prediction results, SOC is mainly distributed in oasis and regions with more human activities, while SOC is less distributed in other regions. This study provides a certain reference value for the prediction of small-scale soil organic carbon spatial distribution by means of remote sensing and environmental factors.

scholarly journals HUBUNGAN USIA DAN PARITAS TERHADAP KEJADIAN PRE EKLAMPSIA DI RSD BALUNG JEMBER

2021 ◽  
Vol 2 (3) ◽  
pp. 85-91
Author(s):  
Yuningsih
Keyword(s):  
Medical Records ◽  
Random Sampling ◽  
P Value ◽  
Multiple Logistic Regression ◽  
Women Of Childbearing Age ◽  
Childbearing Age ◽  
Chi Square ◽  
Cross Sectional ◽  
Independent Variables ◽  
Chi Square Test

One of the contributors to maternal and infant mortality is the incidence of preeclampsia that occurs during pregnancy. The cause of preeclampsia is still unknown, but it is suspected that age and parity are one of the triggers for this occurrence. Women of childbearing age who are nulliparous with extreme age under the age of less than 20 years and women with the age of more than 35 years are most commonly found to have preeclampsia. The design in this study is analytic with a cross sectional approach. The population of all mothers giving birth in the delivery room of RSD Balung Jember was 3594 in 2019. The number of samples taken using non-random sampling by purposive sampling was finally obtained by 97 respondents. In this study, the independent variables were maternal age and parity, while the dependent variable was the incidence of preeclampsia. The instrument used is medical records. The data is processed by editing, coding, processing and cleaning processes. Data were analyzed using multiple logistic regression. The results of the chi-square test for the age variable obtained that the Pearson chi-square value was 0.019 and the p value = 0.000 <0.05 from these results Ho was rejected, and the parity variable the Pearson chi- square value was 0.019 and the p value = 0.000 <0.05 from these results Ho is rejected. In conclusion, there is a relationship between age and preeclampsia, and there is a relationship between parity and preeclampsia.

scholarly journals Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia

2021 ◽  
Vol 14 (1) ◽  
pp. 160
Author(s):  
Najmeh Mozaffaree Pour ◽  
Tõnu Oja
Keyword(s):  
Land Cover ◽  
Driving Forces ◽  
Urban Expansion ◽  
Network Models ◽  
Influential Factors ◽  
Residential Areas ◽  
Neural Network Models ◽  
Independent Variables ◽  
The Core ◽  
Mlp Model

Estonia mainly experienced urban expansion after regaining independence in 1991. Employing the CORINE Land Cover dataset to analyze the dynamic changes in land use/land cover (LULC) in Estonia over 28 years revealed that urban land increased by 33.96% in Harju County and by 19.50% in Tartu County. Therefore, after three decades of LULC changes, the large number of shifts from agricultural and forest land to urban ones in an unplanned manner have become of great concern. To this end, understanding how LULC change contributes to urban expansion will provide helpful information for policy-making in LULC and help make better decisions for future transitions in urban expansion orientation and plan for more sustainable cities. Many different factors govern urban expansion; however, physical and proximity factors play a significant role in explaining the spatial complexity of this phenomenon in Estonia. In this research, it was claimed that urban expansion was affected by the 12 proximity driving forces. In this regard, we applied LR and MLP neural network models to investigate the prediction power of these models and find the influential factors driving urban expansion in two Estonian counties. Using LR determined that the independent variables “distance from main roads (X7)”, “distance from the core of main cities of Tallinn and Tartu land (X2)”, and “distance from water land (X11)” had a higher negative correlation with urban expansion in both counties. Indeed, this investigation requires thinking towards constructing a balance between urban expansion and its driving forces in the long term in the way of sustainability. Using the MLP model determined that the “distance from existing residential areas (X10)” in Harju County and the “distance from the core of Tartu (X2)” in Tartu County were the most influential driving forces. The LR model showed the prediction power of these variables to be 37% for Harju County and 45% for Tartu County. In comparison, the MLP model predicted nearly 80% of variability by independent variables for Harju County and approximately 50% for Tartu County, expressing the greater power of independent variables. Therefore, applying these two models helped us better understand the causative nature of urban expansion in Harju County and Tartu County in Estonia, which requires more spatial planning regulation to ensure sustainability.

scholarly journals Relation between Development of Cardiovascular Disease and the C242T CYBA Polymorphism of the NADPH Oxidase in ESRD Patients

2010 ◽  
Vol 29 (2) ◽  
pp. 89-93 ◽  
Author(s):  
Feng-Ying Tang ◽  
Ying Zhu ◽  
Gui Hua Wang ◽  
Xiong-Wei Xie
Keyword(s):  
Oxidative Stress ◽  
Cardiovascular Disease ◽  
Nadph Oxidase ◽  
Multiple Logistic Regression Analysis ◽  
Smoking History ◽  
Multiple Logistic Regression ◽  
Disease Group ◽  
Independent Variables ◽  
Group D ◽  
Esrd Patients

Background: The development of cardiovascular disease in ESRD patients is considered to be associated with oxidative stress. NAD(P)H oxidase has attracted attention as mechanisms of generating oxidative stress. We investigated the relation between the genotype of the C242T CYBA polymorphism of the NADPH oxidase and the development of cardiovascular disease in ESRD patients.Methods: A total of 289 ESRD patients were recruited and allocated to one of the two groups: patients without cardiovascular disease (group N; n=192) and patients developing cardiovascular disease (group D; n=97). The C242T CYBA polymorphism was determined by RFLP-PCR methods.Results: The frequency of the C242T CT+TT genotype was significantly lower in group D than in group N (9.1 vs. 20.2%). In multiple Logistic regression analysis, systolic blood pressure, smoking history and this gene polymorphism were shown to be independent variables for the development of cardiovascular disease in ESRD patients.Conclusions: These results suggest that assessment of the C242T CYBA polymorphism of the NADPH oxidase may be useful in identifying the risk for developing cardiovascular disease in ESRD patients.

Caries Risk Factors among Three-year Old Children in Chiba, Japan

1999 ◽  
Vol 11 (2) ◽  
pp. 109-112 ◽  
Author(s):  
Akio Tada ◽  
Yuichi Ando ◽  
Nobuhiro Hanada
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Dental Caries ◽  
Breast Feeding ◽  
Bottle Feeding ◽  
Multiple Logistic Regression ◽  
Regression Analyses ◽  
Caries Risk ◽  
Independent Variables ◽  
The Relationship

In order to predict the factors which affect the occurrence of dental caries in children after the age 18-months, we analyzed the relationship between the increment of the decayed, missing, and filled teeth (dmft) in children from 18-months to three-years of age and caries risk factors. Subjects were 392 infants who received both an 18-month-old check-up and a three-year-old check-up in Chiba city. Stepwise multiple logistic regression analyses were used to analyze the results with the increment of the dmft by various combinations of independent variables (sex, order of birth, sweets intake, beverage intake, tooth brushing and feeding). The most predictive factors for the increment of the dmft in upper anterior and molar were “breast feeding” and “bottle feeding” respectively. From these results, we concluded that bottle feeding and breast feeding were the risk factors for the increment of the dmft from the age of 18-months to three years.

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