neural network analysis
Recently Published Documents

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.

Race is commonly used as a proxy for multiple features including socioeconomic status. It is critical to dissociate these factors, identify mechanisms that impact infant outcomes, such as birthweight, and direct appropriate interventions and shape public policy. Demographic, socioeconomic, and clinical variables were used to model infant birthweight. Non-linear neural networks better model infant birthweight than linear models (R^2=0.172 vs. R^2=0.145, p-value=0.005). In contrast to linear models, non-linear models ranked income, neighborhood disadvantage, and experiences of discrimination higher in importance while modeling birthweight than race. Consistent with extant social science literature, findings suggest race is a linear proxy for non-linear factors. The ability to disentangle and identify the source of effects for socioeconomic status and other social factors that often correlate with race is critical for the ability to appropriately target interventions and public policies designed to improve infant outcomes as well as point out the disparities in these outcomes.

The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and their body parts (head/ears, and tail) by using machine learning algorithms (feature pyramid network). In the second part of the study, the goal was to improve the detection of tail posture (tail straight and curled) during activity (standing/moving around) by the use of neural network analysis (YOLOv4). Our dataset (n = 583 images, 7579 pig posture) was annotated in Labelbox from 2D video recordings of groups (n = 12–15) of weaned pigs. The model recognized each individual pig’s body with a precision of 96% related to threshold intersection over union (IoU), whilst the precision for tails was 77% and for heads this was 66%, thereby already achieving human-level precision. The precision of pig detection in groups was the highest, while head and tail detection precision were lower. As the first study was relatively time-consuming, in the second part of the study, we performed a YOLOv4 neural network analysis using 30 annotated images of our dataset for detecting straight and curled tails. With this model, we were able to recognize tail postures with a high level of precision (90%).

The database of studies of 82 patients with acute pancreatitis are presented. Using neural network analysis, the most indicative parameters for predicting acute pancreatitis were revealed: indexes of Kalf-Kalif intoxication modified by Kostyuchenko and Khomich, Reis, Garkavi, the ratio of leukocytes to ESR, leukocyte index, general intoxication index; sonographic parameters – the size of the head of the pancreas, the diameter of the splenic vein, the presence of free fluid in the abdominal cavity; biochemical parameters – blood amylase concentration, urine diastase. When conducting clustering in a multidimensional feature space, a Kohonen neural network was created. All analyzed objects were effectively divided into 3 clusters. The most severe and prognostically unfavorable is cluster 1, which included data from 30 patients, with the maximum mortality rate and maximum hospital stay.

Healthcare managers consider the rewards and performances of employees as central elements of their activities due to the challenges caused by the phenomenon of healthcare employees’ emigrating to higher-income countries, which has reduced patient satisfaction and led to a negative image of hospitals. In this context, this paper analyzes how employee rewards influence the employees’ self-perceived performances in the hospital units of the emergency medical system in Romania. Using structural equation modeling, we analyzed the relationships between the investigated variables, showing that financial motivation and the recognition of employees’ merits are central to employees’ self-perceived performances. Ensuring equity also has a positive impact on how the reward package is established and managed. While financial rewards are the most important incentives to increase efforts to exhibit higher performances, recognition has a long-term motivational effect.

The highly infectious nature of the COVID-19 virus has made the use of contactless payment methods a health exigency. Yet, consumers are resisting using mobile payments (m-payments) during the pandemic, a confounding behavior that needs to be better understood. The present study explicates this behavior by examining consumer resistance to m-payments during the COVID-19 pandemic. In addition, it provides more granular findings by measuring three levels of resistance/non-adoption, namely, postponement, opposition, and rejection. In this way, the study adds depth to the literature, which has largely examined resistance at an aggregate level to yield generic findings. Toward this end, the study draws upon the Innovation Resistance Theory (IRT) to propose that usage, value, risk, tradition, and image barriers influence the three levels of resistance/non-adoption differently. An artificial neural network analysis (ANN) of the data collected from 406 non-users of m-payments confirmed that the influence of the five barriers varies for the three levels of resistance/non-adoption. The results further suggest that the usage barrier is the most significant contributor to opposition and rejection intentions toward m-payments, whereas the image barrier is the most influential for postponement intentions. This study thus makes a useful contribution to theory and practice.