scholarly journals The Role of Economic Freedom in Interpreting Corruption Perception

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
SERPİL KILIÇ DEPREN ◽  
Gülçin Yangın
Keyword(s):  
Machine Learning ◽  
Government Spending ◽  
Economic Freedom ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Tax Burden ◽  
Policy Makers ◽  
Factors Affecting ◽  
Critical Thresholds ◽  
Corruption Perception

The main purpose of the study is to examine the nexus between corruption and economic freedom to determine the most influencing factors to be focused on to reduce corruption. With this aim, two different machine learning algorithms are performed to find out the single effect, two-way, and three-way interaction effects of factors affecting corruption. As a result of the analysis, tax burden, government integrity, and government spending are the main indicators to be focused on to improve corruption steadily. Besides, critical thresholds of the tax burden, government integrity, and government spending are 83.3, 50.9, and 40.6, respectively. Since there are a limited number of studies to predict corruption by machine learning algorithms in the extant literature, this research provides highly detailed information to policy-makers where they can focus on reducing corruption perception.

scholarly journals Factors and Predictors of Length of stay in Offenders diagnosed with schizophrenia- A Machine-learning-based approach

2020 ◽  
Author(s):  
Johannes Kirchebner ◽  
Moritz Günther ◽  
Martina Sonnweber ◽  
Alice King ◽  
Steffen Lau
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Learning Algorithms ◽  
Evaluation Criteria ◽  
Selection Procedure ◽  
Legal Aspects ◽  
Influential Factors ◽  
Machine Learning Algorithms ◽  
Forward Selection ◽  
Factors Affecting

Abstract Background: Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns. Due to the confounded nature of factors affecting length of stay of psychiatric offender patients, prior research has called for the application of a new statistical methodology better accommodating this data structure. The present study attempts to investigate factors contributing to long-term hospitalization of schizophrenic offenders referred to a Swiss forensic institution, using machine learning algorithms that are better suited than conventional methods to detect nonlinear dependencies between variables. Methods: In this retrospective file and registry study, multidisciplinary notes of 143 schizophrenic offenders were reviewed using a structured protocol on patients’ characteristics, criminal and medical history and course of treatment. Via a forward selection procedure, the most influential factors for length of stay were preselected. Machine learning algorithms then identified the most efficient model for predicting length-of-stay. Results: Two factors have been identified as being particularly influential for a prolonged forensic hospital stay, both of which are related to aspects of the index offense, namely (attempted) homicide and the extent of the victim's injury. The results are discussed in light of previous research on this topic. Conclusions: In this study, length of stay was determined by legal considerations, but not by factors that can be influenced therapeutically. Results emphasize that forensic risk assessments should be based on different evaluation criteria and not merely on legal aspects.

scholarly journals A Daily Covid-19 Cases Prediction System using Data Mining and Machine Learning Algorithm

2021 ◽  
Author(s):  
Yiqi Jack Gao ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Hospital Admissions ◽  
Polynomial Regression ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Policy Makers ◽  
Diverse Range ◽  
Using Data

The start of 2020 marked the beginning of the deadly COVID-19 pandemic caused by the novel SARS-COV-2 from Wuhan, China. As of the time of writing, the virus had infected over 150 million people worldwide and resulted in more than 3.5 million global deaths. Accurate future predictions made through machine learning algorithms can be very useful as a guide for hospitals and policy makers to make adequate preparations and enact effective policies to combat the pandemic. This paper carries out a two pronged approach to analyzing COVID-19. First, the model utilizes the feature significance of random forest regressor to select eight of the most significant predictors (date, new tests, weekly hospital admissions, population density, total tests, total deaths, location, and total cases) for predicting daily increases of Covid-19 cases, highlighting potential target areas in order to achieve efficient pandemic responses. Then it utilizes machine learning algorithms such as linear regression, polynomial regression, and random forest regression to make accurate predictions of daily COVID-19 cases using a combination of this diverse range of predictors and proved to be competent at generating predictions with reasonable accuracy.

scholarly journals Factors and predictors of length of stay in offenders diagnosed with schizophrenia- A machine-learning-based approach

2020 ◽  
Author(s):  
Johannes Kirchebner ◽  
Moritz Günther ◽  
Martina Sonnweber ◽  
Alice King ◽  
Steffen Lau
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Learning Algorithms ◽  
Evaluation Criteria ◽  
Selection Procedure ◽  
Legal Aspects ◽  
Influential Factors ◽  
Machine Learning Algorithms ◽  
Forward Selection ◽  
Factors Affecting

Abstract Background: Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns. Due to the confounded nature of factors affecting length of stay of psychiatric offender patients, prior research has called for the application of a new statistical methodology better accommodating this data structure. The present study attempts to investigate factors contributing to long-term hospitalization of schizophrenic offenders referred to a Swiss forensic institution, using machine learning algorithms that are better suited than conventional methods to detect nonlinear dependencies between variables. Methods: In this retrospective file and registry study, multidisciplinary notes of 143 schizophrenic offenders were reviewed using a structured protocol on patients’ characteristics, criminal and medical history and course of treatment. Via a forward selection procedure, the most influential factors for length of stay were preselected. Machine learning algorithms then identified the most efficient model for predicting length-of-stay. Results: Two factors have been identified as being particularly influential for a prolonged forensic hospital stay, both of which are related to aspects of the index offense, namely (attempted) homicide and the extent of the victim's injury. The results are discussed in light of previous research on this topic. Conclusions: In this study, length of stay was determined by legal considerations, but not by factors that can be influenced therapeutically. Results emphasize that forensic risk assessments should be based on different evaluation criteria and not merely on legal aspects.

scholarly journals Factors And Predictors Of Length Of Stay In Offenders Diagnosed With Schizophrenia- A Machine-learning-based Approach

2020 ◽  
Author(s):  
Johannes Kirchebner ◽  
Moritz Günther ◽  
Alice King ◽  
Steffen Lau
Keyword(s):  
Machine Learning ◽  
Length Of Stay ◽  
Learning Algorithms ◽  
Selection Procedure ◽  
Machine Learning Algorithms ◽  
Forward Selection ◽  
Factors Affecting ◽  
Forensic Hospital ◽  
Psychiatric Hospitalizations ◽  
Forensic Psychiatric

Abstract Background Prolonged forensic psychiatric hospitalizations have raised ethical, economic, and clinical concerns. Due to the confounded nature of factors affecting length of stay of psychiatric offender patients, prior research has called for the application of a new statistical methodology better accommodating this data structure. The present study seeks to examine factors responsible for prolonged detentions of schizophrenic offenders referred to a Swiss forensic hospital using machine learning algorithms more apt to reveal non-linear interdependencies between variables.Methods In this retrospective file and registry study, multidisciplinary notes of 143 schizophrenic delinquents were reviewed by using a structured protocol on patients’ characteristics, criminal and medical history and course of treatment. Via a forward selection procedure, the most influential predictors for length of stay were preselected. Machine learning algorithms identified the most efficient model for predicting length-of-stay.Results/ Conclusions Ten factors prolonging forensic hospitalization were identified: Six were related to aspects of the index offence (index offence, number of crimes, extend of injury to the victim of the offence), two were related to psychopathology at admission or even prior to that (hallucinations in psychiatric history), one alluded to the course of therapy (self-harming during inpatient treatment), and one referred to biographical aspects (poverty during childhood/ adolescence). Results are discussed in light of earlier reports on the subject.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

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