Environmental Health

Using a deep convolutional network to predict the longitudinal dispersion coefficient.

Given the interest in accurately predicting the Longitudinal Dispersion Coefficient (D) within the fields of hydraulic and water quality modeling, a wide range of methods have been used to estimate this parameter. In order to improve the accuracy of D predictions, this paper proposes the use of a Deep Convolutional Network (DCN), a sub-field of machine learning. The proposed deep neural network ar...

Bidirectional stochastic configuration network for regression problems.

To adapt to the reality of limited computing resources of various terminal devices in industrial applications, a randomized neural network called stochastic configuration network (SCN), which can conduct effective training without GPU, was proposed. SCN uses a supervisory random mechanism to assign its input weights and hidden biases, which makes it more stable than other randomized algorithms but...

GGL-Tox: Geometric Graph Learning for Toxicity Prediction.

Toxicity analysis is a major challenge in drug design and discovery. Recently significant progress has been made through machine learning due to its a...

Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping.

Recent developments in artificial intelligence have generated increasing interest to deploy automated image analysis for diagnostic imaging and large-...

Predicting the sorption efficiency of heavy metal based on the biochar characteristics, metal sources, and environmental conditions using various novel hybrid machine learning models.

Heavy metals in water and wastewater are taken into account as one of the most hazardous environmental issues that significantly impact human health. ...

A spectral characteristic analysis method for distinguishing heavy metal pollution in crops: VMD-PCA-SVM.

Exploring the characteristics and types of heavy metal pollution in crops has important implications for food security and human health. In this study...

Biosignal Sensors and Deep Learning-Based Speech Recognition: A Review.

Voice is one of the essential mechanisms for communicating and expressing one's intentions as a human being. There are several causes of voice inabili...

Feasibility of machine learning methods for predicting hospital emergency room visits for respiratory diseases.

The prediction of hospital emergency room visits (ERV) for respiratory diseases after the outbreak of PM is of great importance in terms of public hea...

A Review of Robotic Interventional Neuroradiology.

Robotic interventional neuroradiology is an emerging field with the potential to enhance patient safety, reduce occupational hazards, and expand syste...

New methods based on back propagation (BP) and radial basis function (RBF) artificial neural networks (ANNs) for predicting the occurrence of haloketones in tap water.

Haloketones (HKs) is one class of disinfection by-products (DBPs) which is genetically toxic and mutagenic. Monitoring HKs in drinking water is import...

The Design and Development of a Personalized Leisure Time Physical Activity Application Based on Behavior Change Theories, End-User Perceptions, and Principles From Empirical Data Mining.

Many adults do not reach the recommended physical activity (PA) guidelines, which can lead to serious health problems. A promising method to increase...

Recycled Iontronic from Discarded Chewed Gum for Personalized Healthcare Monitoring and Intelligent Information Encryption.

With the explosive development of smart wearable devices, a serious situation that a large amount of energy waste and environmental pollution caused b...

Smartwatch Electrocardiogram and Artificial Intelligence for Assessing Cardiac-Rhythm Safety of Drug Therapy in the COVID-19 Pandemic. The QT-logs study.

BACKGROUND: QTc interval monitoring, for the prevention of drug-induced arrhythmias is necessary, especially in the context of coronavirus disease 201...

Asthma-prone areas modeling using a machine learning model.

Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased....

Soft Robots for Ocean Exploration and Offshore Operations: A Perspective.

The ocean and human activities related to the sea are under increasing pressure due to climate change, widespread pollution, and growth of the offshor...

Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints.

Reproductive toxicity endpoints are a significant safety concern in the assessment of the adverse effects of chemicals in drug discovery. Computationa...

Novel Method Based on Hollow Laser Trapping-LIBS-Machine Learning for Simultaneous Quantitative Analysis of Multiple Metal Elements in a Single Microsized Particle in Air.

Elemental identification of individual microsized aerosol particles is an important topic in air pollution studies. However, simultaneous and quantita...

Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors.

Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, an...

Graph-Guided Regularized Regression of Pacific Ocean Climate Variables to Increase Predictive Skill of Southwestern U.S. Winter Precipitation.

Understanding the physical drivers of seasonal hydroclimatic variability and improving predictive skill remains a challenge with important socioeconom...

Data mining for pesticide decontamination using heterogeneous photocatalytic processes.

Pesticides are chemical compounds used to kill pests and weeds. Due to their nature, pesticides are potentially toxic to many organisms, including hum...