AIMC Topic: Environmental Health

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Artificial Intelligence in Environment and Human Health: Progress, Opportunities and Challenges.

Current environmental health reports
The rapid advancement of artificial intelligence (AI) presents unprecedented opportunities and challenges for assessing planetary health, particularly in environmental health. As a key determinant of human well-being, the environment significantly in...

Integrating machine learning and geospatial approaches for multi-hazard vulnerability mapping: implications for environmental health and contaminant risk in fragile ecosystems.

Environmental geochemistry and health
High-altitude ecosystems face growing threats from natural hazards and human activities, intensifying socio-economic and environmental risks. The Nilgiris District, Tamil Nadu, is a hotspot where steep terrain, fragile ecosystems, climate variability...

Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review.

Current environmental health reports
PURPOSE OF REVIEW: Geospatial analysis is an essential tool for research on the role of environmental exposures and health, and critical for understanding impacts of environmental risk factors on diseases with long latency (e.g. cardiovascular diseas...

Machine learningdriven framework for realtime air quality assessment and predictive environmental health risk mapping.

Scientific reports
This research introduces a practical and innovative approach for real-time air quality assessment and health risk prediction, focusing on urban, industrial, suburban, rural, and traffic-heavy environments. The framework integrates data from multiple ...

Artificial intelligence: A key fulcrum for addressing complex environmental health issues.

Environment international
Environmental health (EH) is a complex and interdisciplinary field dedicated to the examination of environmental behaviours, toxicological effects, health risks, and strategies for mitigating harmful environmental factors. Traditional EH research inv...

Unlocking the potential of Eudrilus eugeniae in mitigating the pollution risk of pesticides and heavy metals: Fostering machine learning tactics to optimize environmental health.

The Science of the total environment
Agro-industrial waste management remains a critical challenge in sustainable development, particularly due to contamination with heterogeneous micropollutants such as heavy metals (HMs), pesticides, and polyphenols. This study explores an innovative ...

An integrated deep-learning model for smart waste classification.

Environmental monitoring and assessment
Efficient waste management is essential for human well-being and environmental health, as neglecting proper disposal practices can lead to financial losses and the depletion of natural resources. Given the rapid urbanization and population growth, de...

Optimization strategy of community planning for environmental health and public health in smart city under multi-objectives.

Frontiers in public health
As population density increases, environmental hygiene and public health become increasingly severe. As the space where residents stay for the longest time and have the most profound impact on their physical and mental health, the quality of the envi...

Augmented reality-enabled human-robot collaboration to balance construction waste sorting efficiency and occupational safety and health.

Journal of environmental management
Construction waste sorting (CWS) is highly recommended as a key step for construction waste management. However, current CWS involves humans' manual hand-picking, which poses significant threats to their occupational safety and health (OSH). Robotic ...

Advances and applications of machine learning and deep learning in environmental ecology and health.

Environmental pollution (Barking, Essex : 1987)
Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecolog...