AIMC Topic: Young Adult

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Analyzing the impact of occupational exposures on male fertility indicators: A machine learning approach.

Reproductive toxicology (Elmsford, N.Y.)
Occupational exposures are critical factors affecting workers' reproductive health. This study investigates the impact of magnetic fields, electric fields, whole-body vibration, noise levels, and heat stress on male reproductive indicators using adva...

A Learning Paradigm for Selecting Few Discriminative Stimuli in Eye-Tracking Research.

IEEE transactions on pattern analysis and machine intelligence
Eye-tracking is a reliable method for quantifying visual information processing and holds significant potential for group recognition, such as identifying autism spectrum disorder (ASD). However, eye-tracking research typically faces the heterogeneit...

A heterogeneity analysis of health-related quality of life in early adults born very preterm or very low birthweight across the sociodemographic spectrum.

Social science & medicine (1982)
Preterm birth and very low birthweight (VP/VLBW) are associated with poorer health-related quality of life (HRQoL) outcomes extending into adulthood, yet it remains unclear how these effects differ across sociodemographic subgroups. This study aimed ...

An X-ray bone age assessment method for hands and wrists of adolescents in Western China based on feature fusion deep learning models.

International journal of legal medicine
The epiphyses of the hand and wrist serve as crucial indicators for assessing skeletal maturity in adolescents. This study aimed to develop a deep learning (DL) model for bone age (BA) assessment using hand and wrist X-ray images, addressing the chal...

Utilizing Artificial Intelligence: Machine Learning Algorithms to Develop a Preoperative Endometriosis Prediction Model.

Journal of minimally invasive gynecology
OBJECTIVE: To evaluate the predictive value of clinical features in the diagnosis of endometriosis by utilizing machine learning algorithms (MLAs), aiming to develop an accurate, explainable prediction model.

Machine learning algorithms to predict the risk of hyperlipidemia in people with HIV after starting HAART for 6 months.

AIDS (London, England)
OBJECTIVE: The purpose of this study was to use machine learning models to predict the risk of hyperlipidemia in people with HIV (PWH) for 6 months after starting HAART, to improve early intervention efforts and prevent further progression to cardiov...

Decision support system based on ensemble models in distinguishing epilepsy types.

Epilepsy & behavior : E&B
This study aimed to classify patients' focal (frontal, temporal, parietal, occipital), multifocal, and generalized epileptiform activities based on EEG findings using artificial intelligence models. The study included 575 patients followed in the Neu...

Factors affecting Artificial Intelligence usage intention among nursing students: Unified theory of acceptance and use of technology.

Nurse education today
BACKGROUND: Nursing students' acceptance and usage of AI are crucial for embracing and implementing the technology in nursing practice in the future. However, there is a lack of literature to examine the factors affecting AI usage intention among nur...

Moral psychological exploration of the asymmetry effect in AI-assisted euthanasia decisions.

Cognition
A recurring discrepancy in attitudes toward decisions made by human versus artificial agents, termed the Human-Robot moral judgment asymmetry, has been documented in moral psychology of AI. Across a wide range of contexts, AI agents are subject to gr...

Maintaining visual stability in naturalistic scenes: The roles of trans-saccadic memory and default assumptions.

Cognition
How is visual stability maintained across saccades? One theory poses the visual system has an underlying assumption that the visual world has not changed during the saccade, and scrutinization of trans-saccadic memory occurs only when there is strong...