AIMC Topic: Environment

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Modelling and evaluation of mechanical performance and environmental impacts of sustainable concretes using a multi-objective optimization based innovative interpretable artificial intelligence method.

Journal of environmental management
This study focuses on modelling sustainable concretes' mechanical and environmental properties with interpretable artificial intelligence-based automated rule extraction, management of waste materials, and meeting future prospects. In this context, 2...

Comparing machine learning approaches for estimating soil saturated hydraulic conductivity.

PloS one
Characterization of near (field) saturated hydraulic conductivity (Kfs) of the soil environment is among the crucial components of hydrological modeling frameworks. Since the associated laboratory/field experiments are time-consuming and labor-intens...

Barley Grain Proteome Assessment Using Multi-Environment Trial Data and Machine Learning.

Journal of agricultural and food chemistry
Proteomics can be used to assess individual protein abundances, which could reflect genotypic and environmental effects and potentially predict grain/malt quality. In this study, 79 barley grain samples (genotype-location-year combinations) from Cali...

Can artificial intelligence improve enterprise environmental performance: Evidence from China.

Journal of environmental management
Artificial intelligence needs to be embraced urgently by enterprises as a means to achieve green development and address the efficiency quagmire in the context of green, low-carbon and sustainable development. To estimate a corporation's pioneering p...

Explainable AI and optimized solar power generation forecasting model based on environmental conditions.

PloS one
This paper proposes a model called X-LSTM-EO, which integrates explainable artificial intelligence (XAI), long short-term memory (LSTM), and equilibrium optimizer (EO) to reliably forecast solar power generation. The LSTM component forecasts power ge...

Leaf rolling detection in maize under complex environments using an improved deep learning method.

Plant molecular biology
Leaf rolling is a common adaptive response that plants have evolved to counteract the detrimental effects of various environmental stresses. Gaining insight into the mechanisms underlying leaf rolling alterations presents researchers with a unique op...

Coping with the tale of natural resources and environmental inequality: an application of the machine learning tools.

Environmental science and pollution research international
With the rising momentum according to the environmentalist voices seeking climate justice for more equity and the importance of encouraging environmental justice mechanisms and tools, in this perspective, the objective of this study is to analyze in ...

The environmental impact of AI in the lab: a double-edged sword?

BioTechniques
Computational tools, particularly AI, are becoming more ubiquitous in scientific research; but what impact do they have on the environment?[Formula: see text].

Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model genotype-by-environment interactions. Complementing phenotypic traits and molecular markers with hi...

Analyzing intelligent tourism development and public services based on a fuzzy genetic hybrid system to promote environmental and cultural values.

PloS one
Environmental, cultural, and public service-dependent factors encourage the development of a country's tourism. In recent years, automated tourism development using statistical and accumulated data has been exploited to recommend attractive tourist f...