Environmental Health

An improved adaptive neuro fuzzy inference system model using conjoined metaheuristic algorithms for electrical conductivity prediction.

Precise prediction of water quality parameters plays a significant role in making an early alert of water pollution and making better decisions for the management of water resources. As one of the influential indicative parameters, electrical conductivity (EC) has a crucial role in calculating the proportion of mineralization. In this study, the integration of an adaptive hybrid of differential ev...

Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification.

Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract task that relies on the experience of understanding ECG waveform meaning, including P-wave, QRS-complex, and T-wave. Such a manual process depends on signal quality and the number of leads. ECG signal classification based on deep learning (DL) has prod...

Environmentally sustainable development and use of artificial intelligence in health care.

Artificial intelligence (AI) can transform health care by delivering medical services to underserved areas, while also filling gaps in health care pro...

Relationships Among Arsenic-Related Traits, Including Rice Grain Arsenic Concentration and Straighthead Resistance, as Revealed by Genome-Wide Association.

There is global concern that rice grains and foods can contain harmful amounts of arsenic (As), motivating breeders to produce cultivars that restrict...

Investigation of intra - event variations of total, dissolved and truly dissolved metal concentrations in highway runoff and a gross pollutant trap - bioretention stormwater treatment train.

Metals in stormwater can be toxic to organisms, particularly when occurring in truly dissolved form (fraction <3 kDa). Here, using 153 samples collect...

A new pairwise deep learning feature for environmental microorganism image analysis.

Environmental microorganism (EM) offers a highly efficient, harmless, and low-cost solution to environmental pollution. They are used in sanitation, m...

A CT image feature space (CTIS) loss for restoration with deep learning-based methods.

Deep learning-based methods have been widely used in medical imaging field such as detection, segmentation and image restoration. For supervised learn...

Online learning-empowered smart management for AO process in sewage treatment processes.

Using artificial intelligence method to describe general working process is a more meaningful and widely used idea in various practical projects. At t...

Human health risk identification of petrochemical sites based on extreme gradient boosting.

Petrochemical industry is a key industry of soil pollution, which presents great effects on human health and the ecological environment. It is of grea...

Implementation of an early warning system with hyperspectral imaging combined with deep learning model for chlorine in refuse derived fuels.

Chlorine content is one of the most important parameters in Refuse Derived Fuels (RDFs) used as a fuel in cement kilns. The main problem with the use ...

IoT-based automated water pollution treatment using machine learning classifiers.

Water is one of the most vital sources for the survival of life. In the globe, the accessibility of water in safe and healthy ways is a major concern....

Automatic quantification and classification of microplastics in scanning electron micrographs via deep learning.

Microplastics quantification and classification are demanding jobs to monitor microplastic pollution and evaluate the potential health risks. In this ...

Predicting daily pore water pressure in embankment dam: Empowering Machine Learning-based modeling.

Dam safety assessment is important to implement the appropriate measures to avoid a dam break disaster as part of the water reservoirs management proc...

Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology.

Vibration-based damage detection in civil structures using data-driven methods requires sufficient vibration responses acquired with a sensor network....

A deep learning model to detect novel pore-forming proteins.

Many pore-forming proteins originating from pathogenic bacteria are toxic against agricultural pests. They are the key ingredients in several pesticid...

Enzyme-Photocatalyst Tandem Microrobot Powered by Urea for Escherichia coli Biofilm Eradication.

Urinary-based infections affect millions of people worldwide. Such bacterial infections are mainly caused by Escherichia coli (E. coli) biofilm format...

Recurrent Neural Network Based Link Quality Prediction for Fluctuating Low Power Wireless Links.

One of the main methods for link quality prediction is to predict the physical layer parameters first, and then evaluate the link quality based on the...

Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River.

Runoff forecasting is useful for flood early warning and water resource management. In this study, backpropagation (BP) neural network, generalized re...

Rapid in measurements of brown tide algae cell concentrations using fluorescence spectrometry and generalized regression neural network.

The frequent occurrence of brown tide pollution in recent years has brought great losses to the economy of coastal areas. Therefore, accurate and effi...

Feature Identification With a Heuristic Algorithm and an Unsupervised Machine Learning Algorithm for Prior Knowledge of Gait Events.

The purpose of this study was to compare a heuristic feature identification algorithm with output from the Beta Process Auto Regressive Hidden Markov ...