Environmental Health

Toxicity prediction and classification of Gunqile-7 with small sample based on transfer learning method.

Drug-induced diseases are the most important component of iatrogenic disease. It is the duty of doctors to provide a reasonable and safe dose of medication. Gunqile-7 is a Mongolian medicine with analgesic and anti-inflammatory effects. As a foreign substance in the body, even with reasonable medication, it may produce varying degrees of adverse reactions or toxic side effects. Since the cost of c...

Application of machine learning in prediction of Pb adsorption of biochar prepared by tube furnace and fluidized bed.

Data mining by machine learning (ML) has recently come into application in heavy metals purification from wastewater, especially in exploring lead removal by biochar that prepared using tube furnace (TF-C) and fluidized bed (FB-C) pyrolysis methods. In this study, six ML models including Random Forest Regression (RFR), Gradient Boosting Regression (GBR), Support Vector Regression (SVR), Kernel Rid...

Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition.

Most drugs are mainly metabolized by cytochrome P450 (CYP450), which can lead to drug-drug interactions (DDI). Specifically, time-dependent inhibition...

A fuzzy interval dynamic optimization model for surface and groundwater resources allocation under water shortage conditions, the case of West Azerbaijan Province, Iran.

The allocation of water in areas which face shortage of water especially during hot dry seasons is of utmost importance. This is normally affected by ...

Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability.

Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have deve...

Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG).

Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focu...

A hybrid prediction model of dissolved oxygen concentration based on secondary decomposition and bidirectional gate recurrent unit.

Dissolved oxygen is one of the important comprehensive indicators of river water quality, which reflects the degree of pollution in the water body. Mo...

Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data.

Identifying patients who benefit from a treatment is a key aspect of personalized medicine, which allows the development of individualized treatment r...

Feasibility and validity of using deep learning to reconstruct 12-lead ECG from three‑lead signals.

BACKGROUND: In the field of mobile health, portable dynamic electrocardiogram (ECG) monitoring devices often have a limited number of lead electrodes ...

BREATH-Net: a novel deep learning framework for NO prediction using bi-directional encoder with transformer.

Air pollution poses a significant challenge in numerous urban regions, negatively affecting human well-being. Nitrogen dioxide (NO) is a prevalent atm...

High-spatial resolution ground-level ozone in Yunnan, China: A spatiotemporal estimation based on comparative analyses of machine learning models.

Monitoring ground-level ozone concentrations is a critical aspect of atmospheric environmental studies. Given the existing limitations of satellite da...

An intelligent interval forecasting system based on fuzzy time series and error distribution characteristics for air quality index.

Due to the emergency environment pollution problems, it is imperative to understand the air quality and take effective measures for environmental gove...

Construction of an aerolysin-based multi-epitope vaccine against an machine learning and artificial intelligence-supported approach.

, a gram-negative coccobacillus bacterium, can cause various infections in humans, including septic arthritis, diarrhea (traveler's diarrhea), gastroe...

ToxMPNN: A deep learning model for small molecule toxicity prediction.

Machine learning (ML) has shown a great promise in predicting toxicity of small molecules. However, the availability of data for such predictions is o...

Using transfer learning-based plant disease classification and detection for sustainable agriculture.

Subsistence farmers and global food security depend on sufficient food production, which aligns with the UN's "Zero Hunger," "Climate Action," and "Re...

Predicting extremely low body weight from 12-lead electrocardiograms using a deep neural network.

Previous studies have successfully predicted overweight status by applying deep learning to 12-lead electrocardiogram (ECG); however, models for predi...

Rewiring care delivery through Digital Therapeutics (DTx): a machine learning-enhanced assessment and development (M-LEAD) framework.

BACKGROUND: Digital transformation has sparked profound change in the healthcare sector through the development of innovative digital technologies. Di...

Machine learning assisted methods for the identification of low toxicity inhibitors of Enoyl-Acyl Carrier Protein Reductase (InhA).

Tuberculosis (TB) is one of the life-threatening infectious diseases with prehistoric origins and occurs in almost all habitable parts of the world. T...

Addressing bias in artificial intelligence for public health surveillance.

Components of artificial intelligence (AI) for analysing social big data, such as natural language processing (NLP) algorithms, have improved the time...

Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges.

Heavy metals (HMs) enter waterbodies through various means, which, when exceeding a threshold limit, cause toxic effects both on the environment and i...