Environmental Health

Generating a long-term (2003-2020) hourly 0.25° global PM dataset via spatiotemporal downscaling of CAMS with deep learning (DeepCAMS).

Generating a long-term high-spatiotemporal resolution global PM dataset is of great significance for environmental management to mitigate the air pollution concerns worldwide. However, the current long-term (2003-2020) global reanalysis dataset Copernicus Atmosphere Monitoring Service (CAMS) reanalysis has drawbacks in fine-scale research due to its coarse spatiotemporal resolution (0.75°, 3-h). H...

Application of artificial intelligence models for prediction of groundwater level fluctuations: case study (Tehran-Karaj alluvial aquifer).

The nonlinear groundwater level fluctuations depend on the interaction of many factors such as evapotranspiration, precipitation, groundwater abstraction, and hydrogeological characteristics, making groundwater level prediction a complex task. Groundwater level changes are among the most critical issues in water resource management, which can be predicted to effectively provide management solution...

Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique.

Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely uncommon form of the illness that almost always resul...

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery.

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solu...

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs.

The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely...

Machine learning-assisted non-target analysis of a highly complex mixture of possible toxic unsymmetrical dimethylhydrazine transformation products with chromatography-mass spectrometry.

Unsymmetrical dimethylhydrazine (UDMH) is a toxic and environmentally hostile compound that was massively introduced to the environment during previou...

Classification of multi-lead ECG with deep residual convolutional neural networks.

. Automatic electrocardiogram (ECG) interpretation based on deep learning methods is attracting increasing attention. In this study, we propose a nove...

Intelligent Measurement and Analysis of Sewage Treatment Parameters based on Fuzzy Neural Algorithm with ARM9 Core CPU.

After entering the new century, the state continues to increase the construction of urban sewage treatment projects in response to the deteriorating w...

Research on soft sensing method of photosynthetic bacteria fermentation process based on ant colony algorithm and least squares support vector machine.

Photosynthetic bacteria wastewater treatment is an efficient water pollution treatment method, but photosynthetic bacteria fermentation is a multivari...

Feasibility Analysis of Mutual Benefit Cooperation between Environment-Embedded Art Design Education and Local SMEs Development Based on Improved Grey Analysis.

With the continuous progress of the economic era, both art and design education and local small and medium-sized enterprises are facing the crisis of ...

Applications of Intelligent Model to Analyze the Green Finance for Environmental Development in the Context of Artificial Intelligence.

Green finance can be referred to as financial investments made on sustainable projects and policies that focus on a sustainable economy. The procedure...

cACP-DeepGram: Classification of anticancer peptides via deep neural network and skip-gram-based word embedding model.

Cancer is a Toxic health concern worldwide, it happens when cellular modifications cause the irregular growth and division of human cells. Several tra...

Automatic ECG classification and label quality in training data.

Within the PhysioNet/Computing in Cardiology Challenge 2021, we focused on the design of a machine learning algorithm to identify cardiac abnormalitie...

Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives.

Over the past few decades, data-driven machine learning (ML) has distinguished itself from hypothesis-driven studies and has recently received much at...

In silico prediction of chemical aquatic toxicity by multiple machine learning and deep learning approaches.

Fish is one of the model animals used to evaluate the adverse effects of a chemical exposed to the ecosystem. However, its low throughput and relevant...

Optimization of Dechlorination Experiment Design Using Lightweight Deep Learning Model.

This exploration intends to remove chloride ions in production and life, enhance buildings' durability, and protect the natural environment from pollu...

HARNU-Net: Hierarchical Attention Residual Nested U-Net for Change Detection in Remote Sensing Images.

Change detection (CD) is a particularly important task in the field of remote sensing image processing. It is of practical importance for people when ...

Exploring Low-Toxicity Chemical Space with Deep Learning for Molecular Generation.

Creating a wide range of new compounds that not only have ideal pharmacological properties but also easily pass long-term toxicity evaluation is still...

Research on Cyanobacterial-Bloom Detection Based on Multispectral Imaging and Deep-Learning Method.

Frequent outbreaks of cyanobacterial blooms have become one of the most challenging water ecosystem issues and a critical concern in environmental pro...

Heart age estimated using explainable advanced electrocardiography.

Electrocardiographic (ECG) Heart Age conveying cardiovascular risk has been estimated by both Bayesian and artificial intelligence approaches. We hypo...