scholarly journals Classification of Fatigued and Drunk Driving Based on Decision Tree Methods: A Simulator Study

2019 ◽  
Vol 16 (11) ◽  
pp. 1935 ◽  
Author(s):  
Ying Yao ◽  
Xiaohua Zhao ◽  
Hongji Du ◽  
Yunlong Zhang ◽  
Guohui Zhang ◽  
...  
Keyword(s):  
Decision Tree ◽  
Regression Tree ◽  
Drunk Driving ◽  
Driving Performance ◽  
Classification And Regression Tree ◽  
Vehicle Speed ◽  
Road Geometry ◽  
Lane Position ◽  
Straight Segments

It is a commonly known fact that both alcohol and fatigue impair driving performance. Therefore, the identification of fatigue and drinking status is very important. In this study, each of the 22 participants finished five driving tests in total. The control condition, serving as the benchmark in the five driving tests, refers to alert driving. The other four test conditions include driving with three blood alcohol content (BAC) levels (0.02%, 0.05%, and 0.08%) and driving in a fatigued state. The driving scenario included straight and curved roads. The straight roads connected the curved ones with radii of 200 m, 500 m, and 800 m with two turning directions (left and right). Driving performance indicators such as the average and standard deviation of longitudinal speed and lane position were selected to identify drunk driving and fatigued driving. In the process of identification, road geometry (straight segments, radius, and direction of curves) was also taken into account. Alert vs. abnormal and fatigued vs. drunk driving with various BAC levels were analyzed separately using the Classification and Regression Tree (CART) model, and the significance of the variables on the binary response variable was determined. The results showed that the decision tree could be used to distinguish normal driving from abnormal driving, fatigued driving, and drunk driving based on the indexes of vehicle speed and lane position at curves with different radii. The overall accuracy of classification of “alert” and “abnormal” driving was 90.9%, and that of “fatigued” and “drunk” driving was 94.4%. The accuracy was relatively low in identifying different BAC degrees. This experiment is designed to provide a reference for detecting dangerous driving states.

scholarly journals Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data

2003 ◽  
Vol 2003 (5) ◽  
pp. 308-314 ◽  
Author(s):  
Antonia Vlahou ◽  
John O. Schorge ◽  
Betsy W. Gregory ◽  
Robert L. Coleman
Keyword(s):  
Ovarian Cancer ◽  
Decision Tree ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Mass Spectral ◽  
Protein Mass ◽  
Proteomic Data ◽  
Decision Tree Classification ◽  
Seldi Proteinchip

Recent reports from our laboratory and others support the SELDI ProteinChip technology as a potential clinical diagnostic tool when combined withn-dimensional analyses algorithms. The objective of this study was to determine if the commercially available classification algorithm biomarker patterns software (BPS), which is based on a classification and regression tree (CART), would be effective in discriminating ovarian cancer from benign diseases and healthy controls. Serum protein mass spectrum profiles from 139 patients with either ovarian cancer, benign pelvic diseases, or healthy women were analyzed using the BPS software. A decision tree, using five protein peaks resulted in an accuracy of 81.5% in the cross-validation analysis and 80%in a blinded set of samples in differentiating the ovarian cancer from the control groups. The potential, advantages, and drawbacks of the BPS system as a bioinformatic tool for the analysis of the SELDI high-dimensional proteomic data are discussed.

Driving Performance under Nighttime Conditions of Visual Degradation

1967 ◽  
Vol 9 (5) ◽  
pp. 427-432 ◽  
Author(s):  
Francisco Matanzo ◽  
Thomas H. Rockwell
Keyword(s):  
Driving Performance ◽  
Task Group ◽  
Vehicle Speed ◽  
Dependent Variables ◽  
Visual Degradation ◽  
Four Levels ◽  
Instrumented Vehicle ◽  
Lane Position ◽  
Nighttime Driving ◽  
Very High

Nighttime driving performance was studied in relation to four different driving tasks and four levels of visual degradation. Four matched but task-differentiated groups of four Ss each drove an instrumented vehicle at night on a superhighway. The four levels of visual degradation presented the roadway to the driver at overall luminance levels of 5.228 mL, 2.688 mL, 0.755 mL, and 0.168 mL. The two dependent variables were vehicle speed and vehicle distance from the white shoulder line. The visual degradation caused the Ss to slow down and position the vehicle slightly farther away from the shoulder. It was found that a driver also is capable of driving at a constant speed and of maintaining a constant lane position at very high degrees of visual degradation. These results were explained by the different instructions given to each task group.

scholarly journals Analisis Kecenderungan Keterlambatan Pembayaran Pengecekan Kapal di Pelabuhan Regional Riau

2019 ◽  
Vol 2 (2) ◽  
pp. 92-98
Author(s):  
Hespri Yomeldi ◽  
Moh Roufiq Azmy ◽  
Ryche Pranita
Keyword(s):  
Logistic Regression ◽  
Decision Tree ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Ministry Of Health ◽  
Tree Algorithm ◽  
Health Checks ◽  
Depth Study ◽  
Classification And Regression ◽  
Port Health

Ship health checks must be carried out which function to provide a sailing permit. The implementation of ship health checks is carried out in collaboration with the ministry of health and transportation. The implementation of the activity, commonly known as Port Health Quarantine Clearance (PHQC) requires time to check and the ship makes a payment check to be able to issue a sailing permit. The problem that arises in the field is that the ship delays PHQC payments and then impacts on the buildup of ships in the port, besides that officers also need longer time to process the issuance of sailing permits. This of course has an impact on other port services such as dwelling time and scheduled departures that can be delayed. In overcoming this problem, an in-depth study is needed to analyze the trend of late payment of ship health checks, what variables influence it and how treatment is done to overcome these problems. Using logistic regression and decision tree with Classification and Regression Tree algorithm , a model is then developed that determines the variables that affect the delay of the ship making PHQC payments.

scholarly journals Detection of Subclinical Keratoconus Using a Novel Combined Tomographic and Biomechanical Model Based on an Automated Decision Tree

2021 ◽  
Author(s):  
Peng Song ◽  
Shengwei Ren ◽  
Yu Liu ◽  
Pei Li ◽  
Qingyan Zeng
Keyword(s):  
Decision Tree ◽  
Amplitude Ratio ◽  
Regression Tree ◽  
Biomechanical Model ◽  
Study Data ◽  
Classification And Regression Tree ◽  
Stiffness Parameter ◽  
Chi Square ◽  
Interaction Detection ◽  
Cart Model

Abstract The aim of this study was to develop a predictive model for subclinical keratoconus (SKC) based on decision tree (DT) algorithms. A total of 194 eyes (including 105 normal eyes and 89 SKC) were included in the double-center retrospective study. Data were separately used for training and validation databases. The baseline variables were derived from tomography and biomechanical imaging. DT models were generated in the training database using Chi-square automatic interaction detection (CHAID) and classification and regression tree (CART) algorithms. The discriminating rules of the CART model selected variables of the Belin/Ambrósio deviation (BAD-D), stiffness parameter at first applanation (SPA1), back eccentricity (Becc), and maximum pachymetric progression index in order, while the CHAID model selected BAD-D, deformation amplitude ratio, SPA1, and Becc. The CART model allowed discrimination between normal and SKC eyes with 92.2% accuracy, which was higher than that of the CHAID model (88.3%), BAD-D (82.0%), Corvis biomechanical index (CBI, 77.3%), and tomographic and biomechanical index (TBI, 78.1%). The discriminating performance of the CART model was validated with 92.4% accuracy, while the CHAID model was validated with 86.4% accuracy in the validation database. Thus, the CART model using tomography and biomechanical imaging was an excellent model for SKC screening and provided easy-to-understand discriminating rules.

scholarly journals Beef Tenderness Prediction by a Combination of Statistical Methods: Chemometrics and Supervised Learning to Manage Integrative Farm-To-Meat Continuum Data

Foods ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 274 ◽  
Author(s):  
Mohammed Gagaoua ◽  
Valérie Monteils ◽  
Sébastien Couvreur ◽  
Brigitte Picard
Keyword(s):  
Decision Tree ◽  
Supervised Learning ◽  
Predictive Accuracy ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Beef Tenderness ◽  
Interaction Detection ◽  
Fiber Area ◽  
Total Collagen ◽  
Protected Designation Of Origin

This trial aimed to integrate metadata that spread over farm-to-fork continuum of 110 Protected Designation of Origin (PDO)Maine-Anjou cows and combine two statistical approaches that are chemometrics and supervised learning; to identify the potential predictors of beef tenderness analyzed using the instrumental Warner-Bratzler Shear force (WBSF). Accordingly, 60 variables including WBSF and belonging to 4 levels of the continuum that are farm-slaughterhouse-muscle-meat were analyzed by Partial Least Squares (PLS) and three decision tree methods (C&RT: classification and regression tree; QUEST: quick, unbiased, efficient regression tree and CHAID: Chi-squared Automatic Interaction Detection) to select the driving factors of beef tenderness and propose predictive decision tools. The former method retained 24 variables from 59 to explain 75% of WBSF. Among the 24 variables, six were from farm level, four from slaughterhouse level, 11 were from muscle level which are mostly protein biomarkers, and three were from meat level. The decision trees applied on the variables retained by the PLS model, allowed identifying three WBSF classes (Tender (WBSF ≤ 40 N/cm2), Medium (40 N/cm2 < WBSF < 45 N/cm2), and Tough (WBSF ≥ 45 N/cm2)) using CHAID as the best decision tree method. The resultant model yielded an overall predictive accuracy of 69.4% by five splitting variables (total collagen, µ-calpain, fiber area, age of weaning and ultimate pH). Therefore, two decision model rules allow achieving tender meat on PDO Maine-Anjou cows: (i) IF (total collagen < 3.6 μg OH-proline/mg) AND (µ-calpain ≥ 169 arbitrary units (AU)) AND (ultimate pH < 5.55) THEN meat was very tender (mean WBSF values = 36.2 N/cm2, n = 12); or (ii) IF (total collagen < 3.6 μg OH-proline/mg) AND (µ-calpain < 169 AU) AND (age of weaning < 7.75 months) AND (fiber area < 3100 µm2) THEN meat was tender (mean WBSF values = 39.4 N/cm2, n = 30).

Using machine learning techniques to develop prediction models for detecting unpaid credit card customers

2020 ◽  
Vol 39 (5) ◽  
pp. 6073-6087
Author(s):  
Meltem Yontar ◽  
Özge Hüsniye Namli ◽  
Seda Yanik
Keyword(s):  
Decision Tree ◽  
Credit Card ◽  
Banking Sector ◽  
Performance Metrics ◽  
Prediction Models ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Cart Algorithm

Customer behavior prediction is gaining more importance in the banking sector like in any other sector recently. This study aims to propose a model to predict whether credit card users will pay their debts or not. Using the proposed model, potential unpaid risks can be predicted and necessary actions can be taken in time. For the prediction of customers’ payment status of next months, we use Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification and Regression Tree (CART) and C4.5, which are widely used artificial intelligence and decision tree algorithms. Our dataset includes 10713 customer’s records obtained from a well-known bank in Taiwan. These records consist of customer information such as the amount of credit, gender, education level, marital status, age, past payment records, invoice amount and amount of credit card payments. We apply cross validation and hold-out methods to divide our dataset into two parts as training and test sets. Then we evaluate the algorithms with the proposed performance metrics. We also optimize the parameters of the algorithms to improve the performance of prediction. The results show that the model built with the CART algorithm, one of the decision tree algorithm, provides high accuracy (about 86%) to predict the customers’ payment status for next month. When the algorithm parameters are optimized, classification accuracy and performance are increased.

Remote Sense Image Classification Based on CART Algorithm

2013 ◽  
Vol 864-867 ◽  
pp. 2782-2786
Author(s):  
Bao Hua Yang ◽  
Shuang Li
Keyword(s):  
Remote Sensing ◽  
Decision Tree ◽  
Regression Tree ◽  
Remote Sensing Image ◽  
Classification And Regression Tree ◽  
Classification Method ◽  
Remote Sense ◽  
Data Source ◽  
Cart Algorithm ◽  
Tree Method

This papers deals with the study of the algorithm of classification method based on decision tree for remote sensing image. The experimental area is located in the Xiangyang district, the data source for the 2010 satellite images of SPOT and TM fusion. Moreover, classification method based on decision tree is optimized with the help of the module of RuleGen and applied in regional remote sensing image of interest. The precision of Maximum likelihood ratio is 95.15 percent, and 94.82 percent for CRAT. Experimental results show that the classification method based on classification and regression tree method is as well as the traditional one.

scholarly journals Modeling and Testing Landslide Hazard Using Decision Tree

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mutasem Sh. Alkhasawneh ◽  
Umi Kalthum Ngah ◽  
Lea Tien Tay ◽  
Nor Ashidi Mat Isa ◽  
Mohammad Subhi Al-Batah
Keyword(s):  
Decision Tree ◽  
Surface Area ◽  
Slope Angle ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Slope Aspect ◽  
Tree Model ◽  
Texture Surface ◽  
Data Set ◽  
Exhaustive Chaid

This paper proposes a decision tree model for specifying the importance of 21 factors causing the landslides in a wide area of Penang Island, Malaysia. These factors are vegetation cover, distance from the fault line, slope angle, cross curvature, slope aspect, distance from road, geology, diagonal length, longitude curvature, rugosity, plan curvature, elevation, rain perception, soil texture, surface area, distance from drainage, roughness, land cover, general curvature, tangent curvature, and profile curvature. Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. Four models were created using Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CRT), and Quick-Unbiased-Efficient Statistical Tree (QUEST). Twenty-one factors were extracted using digital elevation models (DEMs) and then used as input variables for the models. A data set of 137570 samples was selected for each variable in the analysis, where 68786 samples represent landslides and 68786 samples represent no landslides. 10-fold cross-validation was employed for testing the models. The highest accuracy was achieved using Exhaustive CHAID (82.0%) compared to CHAID (81.9%), CRT (75.6%), and QUEST (74.0%) model. Across the four models, five factors were identified as most important factors which are slope angle, distance from drainage, surface area, slope aspect, and cross curvature.

scholarly journals Nursing Staffing Is Associated With Mobility and Food Intake in Older Hospitalized Patients

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 138-138
Author(s):  
Orly Tonkikh ◽  
Efrat Shadmi ◽  
Anna Zisberg
Keyword(s):  
Older Adults ◽  
Food Intake ◽  
Decision Tree ◽  
Regression Tree ◽  
Advanced Practice Nurses ◽  
Classification And Regression Tree ◽  
Advanced Practice ◽  
Skill Mix ◽  
Practice Nurses ◽  
Regression Tree Analysis

Abstract Hospitalization processes related to patient mobility and food-intake significantly affect outcomes of older adults. Nurses are the front-line personnel responsible for promoting performance of these functioning-preserving processes. The degree to which nursing skill-mix is related to their performance is unclear. We investigated the association between staffing and hospitalization processes in a cohort of 836 older adults aged 70+ admitted to internal units for non-disabling conditions. Mobility and food-intake were assessed within 2 days of admission using validated questionnaires. Nurse-patient ratios and nursing skill-mix (i.e. registered nurses, nurse aides, and advanced practice nurses) were assessed using administrative and payroll/roster data. Decision-trees were developed for mobility and food-intake applying classification and regression tree analysis. The mobility decision-tree identified four characteristics that subdivided the patients into eight segments (nodes) (pre-admission functioning, sex, malnutrition risk and percent of advanced practice nurses). The food-intake decision-tree identified five characteristics (pre-admission functioning, sex, chronic morbidity, age and percent of nurse aids) that subdivided the patients into ten nodes. Percent of advanced practice nurses and the percent of nurse aids classified low functioning patients: higher percent of advanced practice nurses (&gt;30% vs. ≤30%) was associated with higher probability of walking in corridors (20.7%) versus inside the room (4.3%), and higher percent of nurse aids (&gt;23% vs. ≤23%) was associated with higher probability of eating more than half of the served meals (83.9%) versus others (66.3%). This study shows that staffing levels are associated with better performance of functioning-preserving processes. Future studies should investigate staffing interventions improving functioning-preserving processes.

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