AIMC Topic: Environment

Clear Filters Showing 1 to 10 of 158 articles

Leveraging LLMs for Environmental Complexity: Structured Fine-Tuning Data Sets and Deployment Strategies.

Environmental science & technology
Generative artificial intelligence, especially large language models (LLMs), could accelerate environmental analysis, but deployment is hindered by two gaps: limited structured domain knowledge and unclear strategies matched to environmental decision...

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...

Enhancing enviromics based predictions in common bean multi-environment trials.

Scientific reports
Enviromic approaches enhance predictive models by incorporating environmental data into selection frameworks. By integrating factor analytic (FA) models, enviromics, and Geographic Information Systems (GIS), the GIS-FA method was proposed to improve ...

Optimizing selective breeding of livestock and forage crops to reduce the environmental impacts of grass-based dairy production by combining life cycle assessment and machine learning.

The Science of the total environment
Global demand for ruminant milk-based products is increasing, contributing to increases in associated environmental impacts. Yet, most efforts to reduce the total environmental impact of dairy production are based on livestock breeding and manipulati...

Environmental adaptations in metagenomes revealed by deep learning.

BMC biology
BACKGROUND: Deep learning has emerged as a powerful tool in the analysis of biological data, including the analysis of large metagenome data. However, its application remains limited due to high computational costs, model complexity, and difficulty e...

The Two Tales of AI: A Global assessment of the environmental impacts of artificial intelligence from a multidimensional policy perspective.

Journal of environmental management
Despite the rapid global expansion of artificial intelligence (AI), its environmental impacts across socio-economic contexts remain underexplored. This study evaluates the relationship between AI intensity and environmental performance across 56 coun...

Machine learning models highlight environmental and genetic factors associated with the Arabidopsis circadian clock.

Nature communications
The circadian clock of plants contributes to their survival and fitness. However, understanding clock function at the transcriptome level and its response to the environment requires assaying across high resolution time-course experiments. Generating...

Strategic forecasting of renewable energy production for sustainable electricity supply: A machine learning approach considering environmental, economic, and oil factors in Türkiye.

PloS one
Providing electricity needs from renewable energy sources is an important issue in the energy policies of countries. Especially changes in energy usage rates make it necessary to use renewable energy resources to be sustainable. The electricity usage...

Considering the Social and Economic Sustainability of AI.

Science and engineering ethics
In recent years, the notion of 'sustainable AI' has emerged as a new topic within the wider debate on artificial intelligence (AI). Although sustainability is usually understood as having three dimensions - the environment, society, and the economy -...

Optimization of a multi-environmental detection model for tomato growth point buds based on multi-strategy improved YOLOv8.

Scientific reports
Tomato growing points and flower buds serve as vital physiological indicators influencing yield quality, yet their detection remains challenging in complex facility environments. This study develops an improved YOLOv8 model for robust flower bud dete...