scholarly journals A Predictive Pedestrian Crash Model Based on Artificial Intelligence Techniques

2021 ◽  
Vol 11 (23) ◽  
pp. 11364
Author(s):  
Monica Meocci ◽  
Valentina Branzi ◽  
Giulia Martini ◽  
Roberto Arrighi ◽  
Irene Petrizzo
Keyword(s):  
Prediction Model ◽  
Risk Level ◽  
Gradient Boosting ◽  
Accurate Analysis ◽  
Accident Prediction ◽  
Road Section ◽  
Linear Relationships ◽  
Potential Safety ◽  
Machine Learning Approach ◽  
Crash Model

Every year in Italy, there are about 20,000 road accidents involving pedestrians, with a significant number of injuries and deaths. Out of these, about 30% occur at pedestrian crossings, where pedestrians should be protected the most. Here, we propose a new accident prediction model to improve pedestrian safety assessments that allows us to accurately identify the sites with the largest potential safety improvements and define the best treatments to be applied. The accident prediction model was developed using the ISTAT dataset, including information about the fatal and injurious crashes that occurred in Italy in a 5-year period. The model allowed us to estimate the risk level of a road section through a machine-learning approach. Gradient Boosting seems to be an appropriate tool to fit classification models for its flexibility that allows us to capture non-linear relationships that would be difficult to detect via a classical approach. The results show the ability of the model to perform an accurate analysis of the sites included in the dataset. The locations analyzed have been classified based on the potential risk in the following three classes: High, medium, and low. The proposed model represents a solid and reliable tool for practitioners to perform accident analysis with pedestrian involvement.

scholarly journals Assessing the Relation between Mud Components and Rheology for Loss Circulation Prevention Using Polymeric Gels: A Machine Learning Approach

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1377
Author(s):  
Musaab I. Magzoub ◽  
Raj Kiran ◽  
Saeed Salehi ◽  
Ibnelwaleed A. Hussein ◽  
Mustafa S. Nasser
Keyword(s):  
Machine Learning ◽  
Rheological Properties ◽  
Nearest Neighbor ◽  
Drilling Fluid ◽  
Gradient Boosting ◽  
K Nearest Neighbor ◽  
Wide Range ◽  
Machine Learning Approach ◽  
Drilling Operations

The traditional way to mitigate loss circulation in drilling operations is to use preventative and curative materials. However, it is difficult to quantify the amount of materials from every possible combination to produce customized rheological properties. In this study, machine learning (ML) is used to develop a framework to identify material composition for loss circulation applications based on the desired rheological characteristics. The relation between the rheological properties and the mud components for polyacrylamide/polyethyleneimine (PAM/PEI)-based mud is assessed experimentally. Four different ML algorithms were implemented to model the rheological data for various mud components at different concentrations and testing conditions. These four algorithms include (a) k-Nearest Neighbor, (b) Random Forest, (c) Gradient Boosting, and (d) AdaBoosting. The Gradient Boosting model showed the highest accuracy (91 and 74% for plastic and apparent viscosity, respectively), which can be further used for hydraulic calculations. Overall, the experimental study presented in this paper, together with the proposed ML-based framework, adds valuable information to the design of PAM/PEI-based mud. The ML models allowed a wide range of rheology assessments for various drilling fluid formulations with a mean accuracy of up to 91%. The case study has shown that with the appropriate combination of materials, reasonable rheological properties could be achieved to prevent loss circulation by managing the equivalent circulating density (ECD).

scholarly journals Methodology and Models for Individuals’ Creditworthiness Management Using Digital Footprint Data and Machine Learning Methods

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1820
Author(s):  
Ekaterina V. Orlova
Keyword(s):  
Interest Rates ◽  
Additional Data ◽  
Risk Level ◽  
Gradient Boosting ◽  
Cluster Number ◽  
New Approach ◽  
Credit Risks ◽  
Stochastic Gradient Boosting ◽  
The Stability ◽  
Digital Footprints

This research deals with the challenge of reducing banks’ credit risks associated with the insolvency of borrowing individuals. To solve this challenge, we propose a new approach, methodology and models for assessing individual creditworthiness, with additional data about borrowers’ digital footprints to implement comprehensive analysis and prediction of a borrower’s credit profile. We suggest a model for borrowers’ clustering based on the method of hierarchical clustering and the k-means method, which groups actual borrowers having similar creditworthiness and similar credit risks into homogeneous clusters. We also design the model for borrowers’ classification based on the stochastic gradient boosting (SGB) method, which reliably determines the cluster number and therefore the risk level for a new borrower. The developed models are the basis for decision making regarding the decision about lending value, interest rates and lending terms for each risk-homogeneous borrower’s group. The modified version of the methodology for assessing individual creditworthiness is presented, which is to reduce the credit risks and to increase the stability and profitability of financial organizations.

Prediction Model for Prevalence of Type-2 Diabetes Mellitus Complications Using Machine Learning Approach

2019 ◽  
pp. 103-116 ◽  
Author(s):  
Muhammad Younus ◽  
Md Tahsir Ahmed Munna ◽  
Mirza Mohtashim Alam ◽  
Shaikh Muhammad Allayear ◽  
Sheikh Joly Ferdous Ara
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Prediction Model ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm

2021 ◽  
Vol 13 (21) ◽  
pp. 12302
Author(s):  
Xiwen Cui ◽  
Shaojun E ◽  
Dongxiao Niu ◽  
Bosong Chen ◽  
Jiaqi Feng
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Carbon Emission ◽  
Historical Data ◽  
Global Climate ◽  
Optimization Methods ◽  
Gradient Boosting ◽  
Target Values ◽  
Whale Optimization ◽  
Practical Implications

As the global temperature continues to rise, people have become increasingly concerned about global climate change. In order to help China to effectively develop a carbon peak target completion plan, this paper proposes a carbon emission prediction model based on the improved whale algorithm-optimized gradient boosting decision tree, which combines four optimization methods and significantly improves the prediction accuracy. This paper uses historical data to verify the superiority of the gradient boosting tree prediction model optimized by the improved whale algorithm. In addition, this study also predicted the carbon emission values of China from 2020 to 2035 and compared them with the target values, concluding that China can accomplish the relevant target values, which suggests that this research has practical implications for China’s future carbon emission reduction policies.

scholarly journals Accident Prediction Model at Unsignalized Intersection Using Multiple Linear Regression Method

2017 ◽  
Vol 1 (2) ◽  
pp. 379-389
Author(s):  
N. K. Oghoyafedo ◽  
J. O. Ehiorobo ◽  
Ebuka Nwankwo
Keyword(s):  
Linear Regression ◽  
Prediction Model ◽  
Multiple Linear Regression ◽  
Regression Method ◽  
Linear Regression Method ◽  
Design Standards ◽  
Accident Prediction ◽  
Geometric Characteristics ◽  
Unsignalized Intersections ◽  
Multiple Linear Regression Method

The issue of road accidents is an increasing problem in developing countries. This could be due to increasing road traffic/vehicle occupancy, geometric characteristics and road way condition. The factors influencing accidents occurrence are to be analysed for remedies. The purpose of this research is to develop an accident prediction model as a measure for future study, aid planning phase preceding the designed intervention, enhance the production of updated design standards to enable practitioners design unsignalized intersection for optimal safety, reduce the number of accidents at unsignalized intersections. Five intersections were selected randomly within Benin City and traffic count carried out at these intersections as well as geometric characteristics and roadway conditions. The prediction model was developed using multiple linear regression method and the standard error of estimate was computed to show how close the observed value is to the regression line. The model was validated using coefficient of multiple determination. The establishment of the relationship between accidents and traffic flow site characteristics on the other hand would enable improvement to be more realistically accessed. This study will also enhance the production of updated design standards to enable practitioners design unsignalized intersection for optimal safety, reduce the number of accidents at unsignalized intersections.

scholarly journals Identifying Children at Readmission Risk: At-Admission Versus Traditional At-Discharge Readmission Prediction Model

Healthcare ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1334
Author(s):  
Hasan Symum ◽  
José Zayas-Castro
Keyword(s):  
Prediction Model ◽  
Information Exchange ◽  
High Risk Patient ◽  
Machine Learning Algorithms ◽  
Polynomial Kernel ◽  
Gradient Boosting ◽  
Support Vector ◽  
Hospital Discharges ◽  
Discharge Model ◽  
Readmission Risk

The timing of 30-day pediatric readmissions is skewed with approximately 40% of the incidents occurring within the first week of hospital discharges. The skewed readmission time distribution coupled with delay in health information exchange among healthcare providers might offer a limited time to devise a comprehensive intervention plan. However, pediatric readmission studies are thus far limited to the development of the prediction model after hospital discharges. In this study, we proposed a novel pediatric readmission prediction model at the time of hospital admission which can improve the high-risk patient selection process. We also compared proposed models with the standard at-discharge readmission prediction model. Using the Hospital Cost and Utilization Project database, this prognostic study included pediatric hospital discharges in Florida from January 2016 through September 2017. Four machine learning algorithms—logistic regression with backward stepwise selection, decision tree, Support Vector machines (SVM) with the polynomial kernel, and Gradient Boosting—were developed for at-admission and at-discharge models using a recursive feature elimination technique with a repeated cross-validation process. The performance of the at-admission and at-discharge model was measured by the area under the curve. The performance of the at-admission model was comparable with the at-discharge model for all four algorithms. SVM with Polynomial Kernel algorithms outperformed all other algorithms for at-admission and at-discharge models. Important features associated with increased readmission risk varied widely across the type of prediction model and were mostly related to patients’ demographics, social determinates, clinical factors, and hospital characteristics. Proposed at-admission readmission risk decision support model could help hospitals and providers with additional time for intervention planning, particularly for those targeting social determinants of children’s overall health.

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