Application of artificial neural networks to predict the heavy metal contamination in the Bartin River
In this study, copper (Cu), iron (Fe), zinc (Zn), manganese (Mn), nickel (Ni), and lead (Pb)
analyses were performed, and the results were modelled by artificial neural networks (ANN) …
analyses were performed, and the results were modelled by artificial neural networks (ANN) …
Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σProfiles Molecular …
Polyhydroxyalkanoates (PHAs) are an emerging type of bioplastic that have the potential to
replace petroleum-based plastics. They are biosynthetizable, biodegradable, and …
replace petroleum-based plastics. They are biosynthetizable, biodegradable, and …
Enhancing precision in PANI/Gr nanocomposite design: robust machine learning models, outlier resilience, and molecular input insights for superior electrical …
This study employs various machine learning algorithms to model the electrical conductivity
and gas sensing responses of polyaniline/graphene (PANI/Gr) nanocomposites based on a …
and gas sensing responses of polyaniline/graphene (PANI/Gr) nanocomposites based on a …
Assessment of PTEs in water resources by integrating HHRISK code, water quality indices, multivariate statistics, and ANNs
JC Agbasi, JC Egbueri - Geocarto international, 2022 - Taylor & Francis
The use of contaminated water for drinking and sanitary purposes can be detrimental to
human health. In this article, the Human Health Risk (HHRISK) code was applied, alongside …
human health. In this article, the Human Health Risk (HHRISK) code was applied, alongside …
[HTML][HTML] A neural network-based predictive model for the thermal conductivity of hybrid nanofluids
Nanofluids are known to have immense potential for heat transfer applications because of
their unique thermophysical properties when compared to the conventional heat transfer …
their unique thermophysical properties when compared to the conventional heat transfer …
Machine learning-quantitative structure property relationship (ML-QSPR) method for fuel physicochemical properties prediction of multiple fuel types
A machine learning-quantitative structure property relationship (ML-QSPR) method is
proposed to predict 15 fuel physicochemical properties of 23 fuel types. QSPR-UOB 3.0 …
proposed to predict 15 fuel physicochemical properties of 23 fuel types. QSPR-UOB 3.0 …
A systematic modeling methodology of deep neural network‐based structure‐property relationship for rapid and reliable prediction on flashpoints
Deep neural networks (DNNs) based quantitative structure–property relationship (QSPR)
studies are receiving increasing attention due to their excellent performances. A systematic …
studies are receiving increasing attention due to their excellent performances. A systematic …
Intelligent soft computational models integrated for the prediction of potentially toxic elements and groundwater quality indicators: a case study
JC Agbasi, JC Egbueri - Journal of sedimentary environments, 2023 - Springer
Reports have shown that potentially toxic elements (PTEs) in air, water, and soil systems
expose humans to carcinogenic and non-carcinogenic health risks. In southeastern Nigeria …
expose humans to carcinogenic and non-carcinogenic health risks. In southeastern Nigeria …
A systematic method for selecting molecular descriptors as features when training models for predicting physiochemical properties
Abstract Machine learning has proven to be a powerful tool for accelerating biofuel
development. Although numerous models are available to predict a range of properties …
development. Although numerous models are available to predict a range of properties …
Quantitative structural assessments of potential meprin β inhibitors by non-linear QSAR approaches and validation by binding mode of interaction analysis
The Zn2+-dependent endopeptidase meprin β is an astacin family metalloenzyme that
belongs to the metzincin superclass of metalloproteases. The presence of a wide variety of …
belongs to the metzincin superclass of metalloproteases. The presence of a wide variety of …