Death in Sadistic Sexual Crimes: A Neural Network Analysis of Factors Associated With a Lethal Outcome

2022 ◽  
pp. 009385482110669
Author(s):  
Julien Chopin ◽  
Eric Beauregard ◽  
Park Dietz
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Network Models ◽  
Sexual Assaults ◽  
Neural Network Analysis ◽  
Neural Network Models ◽  
Lethal Outcome ◽  
Sexual Crimes ◽  
Factors Associated ◽  
Practical Implications

This study aims to determine the factors associated with the victim’s death in sadistic sexual crimes. Specifically, this article examined whether the lethal outcome is more likely to be associated with an escalation of violence during the crime-commission process, an instrumental motivation, or the manifestation of specific sadistic fantasies. We used a database including 735 cases of sadistic sexual assaults. Among this sample, 100 sadistic sexual assaults ended with a lethal outcome. Bivariate analyses, logistic regression, and neural network models were used to identify how the different factors predicted the lethal outcome of sadistic crimes. Our results show that the expression of sadistic behaviors associated with torture and/or bodily punishment plays a fundamental role in the lethal outcome of sadistic sexual crimes. Theoretical and practical implications are discussed.

Experimental study on the factors affecting squeak noise occurrence in automotive suspension bushings

2021 ◽  
pp. 095440702110241
Author(s):  
Byunghyun Kang ◽  
Cheol Choi ◽  
Daeun Sung ◽  
Seongho Yoon ◽  
Byoung-Ho Choi
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Influencing Factors ◽  
Frictional Force ◽  
Prediction Models ◽  
Statistical Tests ◽  
Network Models ◽  
Multiple Logistic Regression ◽  
Neural Network Models ◽  
Automotive Suspension

In this study, friction tests are performed, via a custom-built friction tester, on specimens of natural rubber used in automotive suspension bushings. By analyzing the problematic suspension bushings, the eleven candidate factors that influence squeak noise are selected: surface lubrication, hardness, vulcanization condition, surface texture, additive content, sample thickness, thermal aging, temperature, surface moisture, friction speed, and normal force. Through friction tests, the changes are investigated in frictional force and squeak noise occurrence according to various levels of the influencing factors. The degree of correlation between frictional force and squeak noise occurrence with the factors is determined through statistical tests, and the relationship between frictional force and squeak noise occurrence based on the test results is discussed. Squeak noise prediction models are constructed by considering the interactions among the influencing factors through both multiple logistic regression and neural network analysis. The accuracies of the two prediction models are evaluated by comparing predicted and measured results. The accuracies of the multiple logistic regression and neural network models in predicting the occurrence of squeak noise are 88.2% and 87.2%, respectively.

scholarly journals Combining the Performance Strengths of the Logistic Regression and Neural Network Models: A Medical Outcomes Approach

2003 ◽  
Vol 3 ◽  
pp. 455-476 ◽  
Author(s):  
Wun Wong ◽  
Peter J. Fos ◽  
Frederick E. Petry
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Disease Process ◽  
Network Models ◽  
Predictive Performance ◽  
Medical Outcomes ◽  
Neural Network Models ◽  
The Neural Network ◽  
Combined Use ◽  
Logistic Regression Method

The assessment of medical outcomes is important in the effort to contain costs, streamline patient management, and codify medical practices. As such, it is necessary to develop predictive models that will make accurate predictions of these outcomes. The neural network methodology has often been shown to perform as well, if not better, than the logistic regression methodology in terms of sample predictive performance. However, the logistic regression method is capable of providing an explanation regarding the relationship(s) between variables. This explanation is often crucial to understanding the clinical underpinnings of the disease process. Given the respective strengths of the methodologies in question, the combined use of a statistical (i.e., logistic regression) and machine learning (i.e., neural network) technology in the classification of medical outcomes is warranted under appropriate conditions. The study discusses these conditions and describes an approach for combining the strengths of the models.

Logistic regression using covariates obtained by product-unit neural network models

2007 ◽  
Vol 40 (1) ◽  
pp. 52-64 ◽  
Author(s):  
César Hervás-Martínez ◽  
Francisco Martínez-Estudillo
Keyword(s):  
Neural Network ◽  
Logistic Regression ◽  
Network Models ◽  
Neural Network Models
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