Environmental Health

Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index.

In recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and one multilinear regression (MLR) are considered for the prediction of water quality index (WQI...

2020 ACR Presidential Address: Quality, Ownership, and Our Role as Physicians.

A story from long ago reminds us of the importance of quality in our practices, of taking ownership of our patients, and of our role as physicians. The coronavirus disease 2019 (COVID-19) pandemic has disrupted our practices. Before the pandemic, many practices were stretched thin by the amount of work that needed to be done. The work stoppage in many locations brought an unwelcome pause but gives...

An ensemble learning based hybrid model and framework for air pollution forecasting.

As advance of economy and industry, the impact of air pollution has gradually gained attention. In order to predict air quality, there were many studi...

Inside pandora's box: a systematic review of the assessment of the perceived quality of chatbots for people with disabilities or special needs.

People with disabilities or special needs can benefit from AI-based conversational agents (i.e., chatbots) that are used for competence training and ...

A comparative analysis of saponin-enriched fraction from (Moench) Garcke, (Gaertn) and (Santapau and Fernandes): an hemolytic and cytotoxicity evaluation.

To explore the newer saponin resources, toxicity of saponin-enriched fraction (SEF) extracted from (SV) was evaluated for first time and compared wit...

Identification of Potential PBT/POP-Like Chemicals by a Deep Learning Approach Based on 2D Structural Features.

Identifying potential persistent organic pollutants (POPs) and persistent, bioaccumulative, and toxic (PBT) substances from industrial chemical invent...

Response surface methodology and artificial neural network for remediation of acid orange 7 using TiO-P25: optimization and modeling approach.

The primary responsibility for continuously discharging toxic organic pollutants into water bodies and open environments is the increase in industrial...

Bisphenol S is a haemodialysis-associated xenobiotic that is less toxic than bisphenol A.

BACKGROUND: Bisphenol S (BPS) is a structural analogue of bisphenol A (BPA) that is found in the environment. BPS may accumulate in anuric patients du...

Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils.

In the last decade essential oils have attracted scientists with a constant increase rate of more than 7% as witnessed by almost 5000 articles. Among ...

A Machine Learning-Based Approach for Predicting Patient Punctuality in Ambulatory Care Centers.

Late-arriving patients have become a prominent concern in several ambulatory care clinics across the globe. Accommodating them could lead to detriment...

Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India.

A new version of the fuzzy logic model, called the co-active neuro fuzzy inference system (CANFIS), is introduced for predicting standardized precipit...

Privileged Scaffold Analysis of Natural Products with Deep Learning-based Indication Prediction Model.

Natural products play a vital role in the drug discovery and development process as an important source of reliable and novel lead structures. But the...

Automatic Triage of 12-Lead ECGs Using Deep Convolutional Neural Networks.

BACKGROUND The correct interpretation of the ECG is pivotal for the accurate diagnosis of many cardiac abnormalities, and conventional computerized in...

RadAtlas 1.0: a knowledgebase focusing on radiation-associated genes.

Ionizing radiation has very complex biological effects, such as inducing damage to DNA and proteins, ionizing water molecules to produce toxic free r...

Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network.

The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart. ...

Identification of Risk Factors Associated with Obesity and Overweight-A Machine Learning Overview.

Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, econo...

Machine learning approaches to predict peak demand days of cardiovascular admissions considering environmental exposure.

BACKGROUND: Accumulating evidence has linked environmental exposure, such as ambient air pollution and meteorological factors, to the development and ...

Developing successful environmental decision support systems: Challenges and best practices.

Environmental decision support systems (EDSSs), or DSS applied in the environmental field, have been developed for over 40 years now. However, most of...

Revealing cytotoxic substructures in molecules using deep learning.

In drug development, late stage toxicity issues of a compound are the main cause of failure in clinical trials. In silico methods are therefore of hig...

Automatic diagnosis of the 12-lead ECG using a deep neural network.

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs)...