Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions
Fake news and disinformation (FNaD) are increasingly being circulated through various
online and social networking platforms, causing widespread disruptions and influencing …
online and social networking platforms, causing widespread disruptions and influencing …
Parameter investigation of support vector machine classifier with kernel functions
A Tharwat - Knowledge and Information Systems, 2019 - Springer
Support vector machine (SVM) is one of the well-known learning algorithms for classification
and regression problems. SVM parameters such as kernel parameters and penalty …
and regression problems. SVM parameters such as kernel parameters and penalty …
A machine-learning framework for predicting multiple air pollutants' concentrations via multi-target regression and feature selection
S Masmoudi, H Elghazel, D Taieb, O Yazar… - Science of the Total …, 2020 - Elsevier
Air pollution is considered one of the biggest threats for the ecological system and human
existence. Therefore, air quality monitoring has become a necessity in urban and industrial …
existence. Therefore, air quality monitoring has become a necessity in urban and industrial …
Prediction of arch dam deformation via correlated multi-target stacking
Majority of the existing dam deformation monitoring models focus on the prediction of
individual displacement, and ignore the spatial correlation of data. In this study, we propose …
individual displacement, and ignore the spatial correlation of data. In this study, we propose …
Performing multi-target regression via a parameter sharing-based deep network
Multi-target regression (MTR) comprises the prediction of multiple continuous target
variables from a common set of input variables. There are two major challenges when …
variables from a common set of input variables. There are two major challenges when …
A new data characterization for selecting clustering algorithms using meta-learning
BA Pimentel, AC De Carvalho - Information Sciences, 2019 - Elsevier
Meta-learning has been successfully used for algorithm recommendation tasks. It uses
machine learning to induce meta-models able to predict the best algorithms for a new …
machine learning to induce meta-models able to predict the best algorithms for a new …
Spatiotemporal clustering analysis and zonal prediction model for deformation behavior of super-high arch dams
W Cao, Z Wen, H Su - Expert Systems with Applications, 2023 - Elsevier
Super-high arch dams are affected by similar environmental factors, and there is some
spatial and temporal correlation among the deformation measurement points, while the …
spatial and temporal correlation among the deformation measurement points, while the …
Heuristics for online three-dimensional packing problems and algorithm selection framework for semi-online with full look-ahead
In online three-dimensional packing problems (3D-PPs), unlike offline problems, items arrive
sequentially and require immediate packing decisions without any information about the …
sequentially and require immediate packing decisions without any information about the …
[HTML][HTML] Multi-target prediction of wheat flour quality parameters with near infrared spectroscopy
SB Junior, SM Mastelini, APAC Barbon… - … processing in agriculture, 2020 - Elsevier
Near Infrared (NIR) spectroscopy is an analytical technology widely used for the non-
destructive characterisation of organic samples, considering both qualitative and …
destructive characterisation of organic samples, considering both qualitative and …
A parametric study of 3D printed polymer gears
Y Zhang, K Mao, S Leigh, A Shah, Z Chao… - The International Journal …, 2020 - Springer
The selection of printing parameters for 3D printing can dramatically affect the dynamic
performance of components such as polymer spur gears. In this paper, the performance of …
performance of components such as polymer spur gears. In this paper, the performance of …