Advancing biosensors with machine learning
Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis.
Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved …
Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved …
Intrusion detection and prevention in fog based IoT environments: A systematic literature review
Abstract Currently, the Internet of Things is spreading in all areas that apply computing
resources. An important ally of the IoT is fog computing. It extends cloud computing and …
resources. An important ally of the IoT is fog computing. It extends cloud computing and …
An ensemble machine learning approach for forecasting credit risk of agricultural SMEs' investments in agriculture 4.0 through supply chain finance
Credit risk imposes itself as a significant barrier of agriculture 4.0 investments in the supply
chain finance (SCF) especially for Small and Medium-sized Enterprises. Therefore, it is …
chain finance (SCF) especially for Small and Medium-sized Enterprises. Therefore, it is …
Modeling of CO2 adsorption capacity by porous metal organic frameworks using advanced decision tree-based models
In recent years, metal organic frameworks (MOFs) have been distinguished as a very
promising and efficient group of materials which can be used in carbon capture and storage …
promising and efficient group of materials which can be used in carbon capture and storage …
Spatiotemporal PM2. 5 estimations in China from 2015 to 2020 using an improved gradient boosting decision tree
W He, H Meng, J Han, G Zhou, H Zheng, S Zhang - Chemosphere, 2022 - Elsevier
Abstract Fine particulate matter (PM 2.5) with spatiotemporal continuity can provide
important basis for the assessment of adverse effects on human health. In recent years …
important basis for the assessment of adverse effects on human health. In recent years …
Comparison of machine learning methods for estimating mangrove above-ground biomass using multiple source remote sensing data in the red river delta biosphere …
This study proposes a hybrid intelligence approach based on an extreme gradient boosting
regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2 …
regression and genetic algorithm, namely, the XGBR-GA model, incorporating Sentinel-2 …
The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods
In recent years, machine learning models based on big data have been introduced into
marketing in order to transform customer data into meaningful insights and to make strategic …
marketing in order to transform customer data into meaningful insights and to make strategic …
Improved estimation of coalbed methane content using the revised estimate of depth and CatBoost algorithm: A case study from southern Sichuan Basin, China
The coalbed methane blocks are always structurally and topographically complex, and there
are no models to accurately predict the coalbed methane content in the southern Sichuan …
are no models to accurately predict the coalbed methane content in the southern Sichuan …
[PDF][PDF] Anomaly Detection for Industrial Internet of Things Cyberattacks.
The evolution of the Internet of Things (IoT) has empowered modern industries with the
capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things …
capability to implement large-scale IoT ecosystems, such as the Industrial Internet of Things …
Machine learning-based predictive modelling for the enhancement of wine quality
The certification of wine quality is essential to the wine industry. The main goal of this work is
to develop a machine learning model to forecast wine quality using the dataset. We utilised …
to develop a machine learning model to forecast wine quality using the dataset. We utilised …