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Machine learning arbitrated prediction of disease prevalence due to air pollution over United Arab Emirates
Machine learning tools were used in the prediction of disease prevalence (bacterial, viral, and others) based on the pollutants like inhalable...
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Copper Stress Causes Shell Morphology Changes in Early Juvenile Anodonta woodiana Based on Geometric–Morphometric Analysis
In this study, the morphological characteristics of early juvenile shells of Anodonta woodiana , which were exposed to different concentrations of...
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Probabilistic classification of the severity classes of unhealthy air pollution events
Air pollution events can be categorized as extreme or non-extreme on the basis of their magnitude of severity. High-risk extreme air pollution events...
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Deep learning-based burned forest areas map** via Sentinel-2 imagery: a comparative study
In order to evaluate the effects of forest fires on the dynamics of the function and structure of ecosystems, it is necessary to determine burned...
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Comparison of multiple conventional and unconventional machine learning models for landslide susceptibility map** of Northern part of Pakistan
Landslide susceptibility study is a critically important topic throughout the globe owing to the social and financial catastrophes of landslides. The...
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Forecasting air pollutants using classification models: a case study in the Bay of Algeciras (Spain)
The main goal of this work is to obtain reliable predictions of pollutant concentrations related to maritime traffic (SO 2 , PM 10 , NO 2 , NO X , and NO) in...
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Assessing the impact of anthropogenic activities on land use and land cover changes in the semi-arid and arid regions of Algeria
Land use and land cover are critical factors that influence the environment and human societies. The dynamics of LULC have been constantly changing...
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Effectiveness of autoencoder for lake area extraction from high-resolution RGB imagery: an experimental study
The surface areas of lakes alter constantly due to many factors such as climate change, land use policies, and human interventions, and their surface...
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Performance comparison of deep learning and machine learning methods in determining wetland water areas using EuroSAT dataset
Wetlands are critical to the ecology because they maintain biodiversity and provide home for a variety of species. Researching, map**, and...
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Convolutional neural network with near-infrared spectroscopy for plastic discrimination
Plastic pollution is a global issue of increasing health concern, thus requiring innovative waste management. In particular, there is a need for...
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A comparison of statistical and machine learning methods for debris flow susceptibility map**
Debris flows destroys the facilities and seriously threatens human lives, especially in mountainous area. Susceptibility map** is the key for...
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Demi-decadal land use land cover change analysis of Mizoram, India, with topographic correction using machine learning algorithm
Mizoram (India) is part of UNESCO’s biodiversity hotspots in India that is primarily populated by tribes who engage in shifting agriculture. Hence,...
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Machine learning strengthened prediction of tracheal, bronchus, and lung cancer deaths due to air pollution
This work pointed out the use of machine learning tools to predict the effect of CO, O 3 , CH 4 , and CO 2 on TBL (tracheal, bronchus, and lung cancer)...
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GIS-based air quality modelling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms
Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore,...
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Data-driven techniques for temperature data prediction: big data analytics approach
For extrapolation, climate change and other meteorological analysis, a study of past and current weather events is a prerequisite. NASA (National...
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A comparative study of heterogeneous and homogeneous ensemble approaches for landslide susceptibility assessment in the Djebahia region, Algeria
This study aims to compare the performance of ensembles according to their inherent diversity in the context of landslide susceptibility assessment....
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Computational Methods for Predictive Toxicology: In Silico Toxicology
The emerging era of computational advancements is coming up with new ways to use toxicology without sacrificing living organisms. This requires...
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Predictive capability of rough set machine learning in tetracycline adsorption using biochar
Machine learning algorithms investigate relationships in data to deliver useful outputs. However, past models required complete datasets as a...
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Enhancement of nitrogen cycling and functional microbial flora by artificial inoculation of biological soil crusts in sandy soils of highway slopes
Biological soil crusts (BSCs) are common in arid and semi-arid ecosystems and enhance soil stability and fertility. Highway slopes severely deplete...
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Landslide susceptibility assessment of South Korea using stacking ensemble machine learning
BackgroundLandslide susceptibility assessment (LSA) is a crucial indicator of landslide hazards, and its accuracy is improving with the development...
