AIMC Topic: Family Characteristics
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The impact of intelligent devices utilization on household medical expenditure of older adults in China.
Scientific reports
Jul 2, 2025
With the rapid development of artificial intelligence, there is an increasing utilization of intelligent devices by older adults. The relationship between the utilization of intelligent devices and household medical expenditure has garnered widesprea...
Efficiency-Driven Adaptive Task Planning for Household Robot Based on Hierarchical Item-Environment Cognition.
IEEE transactions on cybernetics
Mar 21, 2025
Task planning focused on household robots represents a conventional yet complex research domain, necessitating the development of task plans that enable robots to execute unfamiliar household services. This area has garnered significant research inte...
Assessing fecal contamination from human and environmental sources using as an indicator in rural eastern Ethiopian households-a cross-sectional study from the EXCAM project.
Frontiers in public health
Jan 6, 2025
INTRODUCTION: Enteric pathogens are a leading causes of diarrheal deaths in low-and middle-income countries. The Exposure Assessment of Infections in Rural Ethiopia (EXCAM) project, aims to identify potential sources of bacteria in the genus and, m...
Unveiling predictive factors for household-level stunting in India: A machine learning approach using NFHS-5 and satellite-driven data.
Nutrition (Burbank, Los Angeles County, Calif.)
Dec 24, 2024
OBJECTIVES: Childhood stunting remains a significant public health issue in India, affecting approximately 35% of children under 5. Despite extensive research, existing prediction models often fail to incorporate diverse data sources and address the ...
Determinants of adoption of household water treatment in Haiti using two analysis methods: logistic regression and machine learning.
Journal of water and health
Aug 21, 2024
Household water treatment (HWT) is recommended when safe drinking water is limited. To understand determinants of HWT adoption, we conducted a cross-sectional survey with 650 households across different regions in Haiti. Data were collected on 71 dem...
A GA-stacking ensemble approach for forecasting energy consumption in a smart household: A comparative study of ensemble methods.
Journal of environmental management
Jun 12, 2024
The considerable amount of energy utilized by buildings has led to various environmental challenges that adversely impact human existence. Predicting buildings' energy usage is commonly acknowledged as encouraging energy efficiency and enabling well-...
Tailoring Household Disaster Preparedness Interventions to Reduce Health Disparities: Nursing Implications from Machine Learning Importance Features from the 2018-2020 FEMA National Household Survey.
International journal of environmental research and public health
Apr 23, 2024
Tailored disaster preparedness interventions may be more effective and equitable, yet little is known about specific factors associated with disaster household preparedness for older adults and/or those with African American/Black identities. This st...
How Lazy Are Pet Cats Really? Using Machine Learning and Accelerometry to Get a Glimpse into the Behaviour of Privately Owned Cats in Different Households.
Sensors (Basel, Switzerland)
Apr 19, 2024
Surprisingly little is known about how the home environment influences the behaviour of pet cats. This study aimed to determine how factors in the home environment (e.g., with or without outdoor access, urban vs. rural, presence of a child) and the s...
High-resolution rural poverty mapping in Pakistan with ensemble deep learning.
PloS one
Apr 4, 2023
High resolution poverty mapping supports evidence-based policy and research, yet about half of all countries lack the survey data needed to generate useful poverty maps. To overcome this challenge, new non-traditional data sources and deep learning t...
Predicting hospitalization following psychiatric crisis care using machine learning.
BMC medical informatics and decision making
Dec 10, 2020
BACKGROUND: Accurate prediction models for whether patients on the verge of a psychiatric criseis need hospitalization are lacking and machine learning methods may help improve the accuracy of psychiatric hospitalization prediction models. In this pa...
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