Machine Learning for industrial applications: A comprehensive literature review
Abstract Machine Learning (ML) is a branch of artificial intelligence that studies algorithms
able to learn autonomously, directly from the input data. Over the last decade, ML …
able to learn autonomously, directly from the input data. Over the last decade, ML …
[HTML][HTML] Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities
M Seyedan, F Mafakheri - Journal of Big Data, 2020 - Springer
Big data analytics (BDA) in supply chain management (SCM) is receiving a growing
attention. This is due to the fact that BDA has a wide range of applications in SCM, including …
attention. This is due to the fact that BDA has a wide range of applications in SCM, including …
Machine learning in manufacturing: advantages, challenges, and applications
The nature of manufacturing systems faces ever more complex, dynamic and at times even
chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an …
chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an …
[HTML][HTML] Using meta-learning for automated algorithms selection and configuration: an experimental framework for industrial big data
Advanced analytics are fundamental to transform large manufacturing data into resourceful
knowledge for various purposes. In its very nature, such “industrial big data” can relay its …
knowledge for various purposes. In its very nature, such “industrial big data” can relay its …
[HTML][HTML] Prediction of heterogeneous Fenton process in treatment of melanoidin-containing wastewater using data-based models
Predictive capability of response surface methodology (RSM) and ant colony optimization
combined with support vector regression (ACO-SVR) models are applied for determining …
combined with support vector regression (ACO-SVR) models are applied for determining …
Optimizing a multi-vendor multi-retailer vendor managed inventory problem: Two tuned meta-heuristic algorithms
The vendor-managed inventory (VMI) is a common policy in supply chain management
(SCM) to reduce bullwhip effects. Although different applications of VMI have been proposed …
(SCM) to reduce bullwhip effects. Although different applications of VMI have been proposed …
Low-discrepancy sequence initialized particle swarm optimization algorithm with high-order nonlinear time-varying inertia weight
Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm
motivated by intelligent collective behavior of some animals such as flocks of birds or …
motivated by intelligent collective behavior of some animals such as flocks of birds or …
Lattice constant prediction of cubic and monoclinic perovskites using neural networks and support vector regression
In the study of crystalline materials, the lattice constant (LC) of perovskites compounds play
important role in the identification of materials. It reveals various interesting properties. In this …
important role in the identification of materials. It reveals various interesting properties. In this …
Machine learning applications in supply chains: An emphasis on neural network applications
H Bousqaoui, S Achchab, K Tikito - 2017 3rd International …, 2017 - ieeexplore.ieee.org
Machine Learning or the ability of a machine to learn automatically has garnered a lot of
interest in the last years. It has proven to be a valuable tool for aiding decision makers and …
interest in the last years. It has proven to be a valuable tool for aiding decision makers and …
Seismic reliability assessment of RC structures including soil–structure interaction using wavelet weighted least squares support vector machine
M Khatibinia, MJ Fadaee, J Salajegheh… - Reliability Engineering & …, 2013 - Elsevier
An efficient metamodeling framework in conjunction with the Monte-Carlo Simulation (MCS)
is introduced to reduce the computational cost in seismic reliability assessment of existing …
is introduced to reduce the computational cost in seismic reliability assessment of existing …