The factors complicating the specification of requirements for artificial intelligence systems (AIS) and their verification for the AIS creation and modernization are analyzed. The harmonization of definitions and building of a hierarchy of AIS characteristics for regulation of the development of techniques and tools for standardization, as well as evaluation and provision of requirements during the creation and implementation of AIS, is extremely important. The study aims to develop and demonstrate the use of quality models for artificial intelligence (AI), AI platform (AIP), and AIS based on the definition and ordering of characteristics. The principles of AI quality model development and its sequence are substantiated. Approaches to formulating definitions of AIS characteristics, methods of representation of dependencies, and hierarchies of characteristics are given. The definitions and harmonization options of hierarchical relations between 46 characteristics of AI and AIP are suggested. The quality models of AI, AIP, and AIS presented in analytical, tabular, and graph forms, are described. The so-called basic models with reduced sets of the most important characteristics are presented. Examples of AIS quality models for UAV video navigation systems and decision support systems for diagnosing diseases are described.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9269736 | PMC |
http://dx.doi.org/10.3390/s22134865 | DOI Listing |
Front Psychol
December 2024
School of Philosophy and Sociology, Jilin University, Changchun, China.
Introduction: Knowledge sharing is an effective means of knowledge management in colleges and universities, which is of great significance for improving the quality and efficiency of universities and enhancing the balanced development of educational resources. The present study investigated the influence students' proactive personalities drive knowledge-sharing activities, and examined the significance of class climate and learning engagement as mediating factors, utilizing the perspectives of social exchange theory (SET) and the job demands and resources model (JD-R) .
Methods: A convenience sampling method was employed to survey 1,053 Chinese college students, and evaluated them using the Proactive Personality Scale (PPS), Learning Engagement Scale (LES), Class Climate Scale (CCS), and Knowledge Sharing Behavior Scale (KSBS).
Front Cardiovasc Med
December 2024
School of Public Health, College of Health Science and Medicine, Wolaita Sodo University, Sodo, Ethiopia.
Background: More than 23 million deaths and 36.5% of disability-adjusted life-years are the result of the direct effects of unhealthy behavior alone. Daily behaviors have strong implications for health outcomes and quality of life.
View Article and Find Full Text PDFPatterns (N Y)
December 2024
Zhejiang University, Hangzhou, China.
As the parameter size of large language models (LLMs) continues to expand, there is an urgent need to address the scarcity of high-quality data. In response, existing research has attempted to make a breakthrough by incorporating federated learning (FL) into LLMs. Conversely, considering the outstanding performance of LLMs in task generalization, researchers have also tried applying LLMs within FL to tackle challenges in relevant domains.
View Article and Find Full Text PDFJAMIA Open
February 2025
Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, IN 46202, United States.
Objective: Measurement of health-related social needs (HRSNs) is complex. We sought to develop and validate computable phenotypes (CPs) using structured electronic health record (EHR) data for food insecurity, housing instability, financial insecurity, transportation barriers, and a composite-type measure of these, using human-defined rule-based and machine learning (ML) classifier approaches.
Materials And Methods: We collected HRSN surveys as the reference standard and obtained EHR data from 1550 patients in 3 health systems from 2 states.
Biol Imaging
December 2024
Visual Information Laboratory, University of Bristol, Bristol, UK.
Optical coherence tomography (OCT) and confocal microscopy are pivotal in retinal imaging, offering distinct advantages and limitations. OCT offers rapid, noninvasive imaging but can suffer from clarity issues and motion artifacts, while confocal microscopy, providing high-resolution, cellular-detailed color images, is invasive and raises ethical concerns. To bridge the benefits of both modalities, we propose a novel framework based on unsupervised 3D CycleGAN for translating unpaired OCT to confocal microscopy images.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!