Environmental Health

Air quality index prediction with optimisation enabled deep learning model in IoT application.

The development of industrial and urban places caused air pollution, which has resulted in a variety of effects on individuals and the atmosphere over the years. The measurement of the air quality index (AQI) depends on various environmental situations, such as emissions, dispersions, and chemical reactions. This paper developed the Internet of Things (IoT)-based Deep Learning (DL) technique for p...

High-throughput prediction of oral acute toxicity in Rat and Mouse of over 100,000 polychlorinated persistent organic pollutants (PC-POPs) by interpretable data fusion-driven machine learning global models.

This study utilized available oral acute toxicity data in Rat and Mouse for polychlorinated persistent organic pollutants (PC-POPs) to construct data fusion-driven machine learning (ML) global models. Based on atom-centered fragments (ACFs), the collected high-throughput data overcame the applicability limitations, enabling accurate toxicity prediction for a wide range of PC-POPs series compounds ...

Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning?

In this study, several machine learning (ML) models consisting of shallow learning (SL) models (e.g., random forest (RF), K-nearest neighbor (KNN), we...

Innovations in plastic remediation: Catalytic degradation and machine learning for sustainable solutions.

Plastic pollution is an extreme environmental threat, necessitating novel restoration solutions. The present investigation investigates the integratio...

Effects of the co-exposure of microplastic/nanoplastic and heavy metal on plants: Using CiteSpace, meta-analysis, and machine learning.

Micro/nanoplastics (MNPs) and heavy metals (HMs) coexist worldwide. Existing studies have reported different or even contradictory toxic effects of co...

Derivation of marine water quality criteria for copper based on artificial neural network model.

The water chemical effects of copper have been a focus in the study of water quality criteria (WQC). Currently, multiple regression models are commonl...

Multimodal Representation Learning via Graph Isomorphism Network for Toxicity Multitask Learning.

Toxicity is paramount for comprehending compound properties, particularly in the early stages of drug design. Due to the diversity and complexity of t...

An integrated three-stream network model for discriminating fish feeding intensity using multi-feature analysis and deep learning.

Feed costs constitute a significant part of the expenses in the aquaculture industry. However, feeding practices in fish farming often rely on the bre...

Imaging pollen using a Raspberry Pi and LED with deep learning.

The production of low-cost, small footprint imaging sensor would be invaluable for airborne global monitoring of pollen, which could allow for mitigat...

Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascular disease (CVD). Artificial intelligence-based an...

3DVar sectoral emission inversion based on source apportionment and machine learning.

Air quality models are increasingly important in air pollution forecasting and control. Sectoral emissions significantly impact the accuracy of air qu...

Artificial intelligence application for identifying toxic plant species: A case of poisoning with Datura stramonium.

INTRODUCTION: The management of plant poisonings in the emergency department (ED) presents various challenges. Foremost among these is the identificat...

Advancements and Applications of Artificial Intelligence in Pharmaceutical Sciences: A Comprehensive Review.

Artificial intelligence (AI) has revolutionized the pharmaceutical industry, improving drug discovery, development, and personalized patient care. Thr...

Deep learning assists early-detection of hypertension-mediated heart change on ECG signals.

Arterial hypertension is a major risk factor for cardiovascular diseases. While cardiac ultrasound is a typical way to diagnose hypertension-mediated ...

Machine learning-assisted source tracing in domestic-industrial wastewater: A fluorescence information-based approach.

An emergency water pollution incident poses a significant risk to the proper functioning of wastewater treatment plants, particularly in domestic-indu...

Sex dimorphism and hormesis response to polystyrene microplastic exposure in kinematics and metabolism of Drosophila model based on deep learning.

The emergence of microplastics (MPs) has become a significant focus of environmental pollution, prompting widespread concern regarding its potential t...

Artificial Intelligence-Enhanced Analysis of Genomic DNA Visualized with Nanoparticle-Tagged Peptides under Electron Microscopy.

DNA visualization has advanced across multiple microscopy platforms, albeit with limited progress in the identification of novel staining agents for e...

A novel ensemble approach with deep transfer learning for accurate identification of foodborne bacteria from hyperspectral microscopy.

The detection of foodborne bacteria is critical in ensuring both consumer safety and food safety. If these pathogens are not properly identified, it c...

A novel interpretable machine learning and metaheuristic-based protocol to predict and optimize ciprofloxacin antibiotic adsorption with nano-adsorbent.

The existence of antibiotics in water sources poses substantial hazards to both the environment and public health. To effectively monitor and combat t...

Predicting plateau atmospheric ozone concentrations by a machine learning approach: A case study of a typical city on the southwestern plateau of China.

Atmospheric ozone (O) has been placed on the priority control pollutant list in China's 14th Five-Year Plan. Due to their unique meteorological condit...