On hyperparameter optimization of machine learning algorithms: Theory and practice
Abstract Machine learning algorithms have been used widely in various applications and
areas. To fit a machine learning model into different problems, its hyper-parameters must be …
areas. To fit a machine learning model into different problems, its hyper-parameters must be …
Big-data science in porous materials: materials genomics and machine learning
By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
Optimizing machine learning algorithms for landslide susceptibility mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A comparative study of baseline …
Algorithms for machine learning have found extensive use in numerous fields and
applications. One important aspect of effectively utilizing these algorithms is tuning the …
applications. One important aspect of effectively utilizing these algorithms is tuning the …
Retrieval of total precipitable water from Himawari-8 AHI data: A comparison of random forest, extreme gradient boosting, and deep neural network
Total precipitable water (TPW), a column of water vapor content in the atmosphere, provides
information on the spatial distribution of moisture. The high-resolution TPW, together with …
information on the spatial distribution of moisture. The high-resolution TPW, together with …
The potential of deep learning to reduce complexity in energy system modeling
CS Köhnen, J Priesmann, L Nolting… - … Journal of Energy …, 2022 - Wiley Online Library
In order to cope with increasing complexity in energy systems due to rapid changes and
uncertain future developments, the evaluation of multiple scenarios is essential for sound …
uncertain future developments, the evaluation of multiple scenarios is essential for sound …
How Does Neural Network Model Capacity Affect Photovoltaic Power Prediction? A Study Case
CHT Andrade, GCG Melo, TF Vieira, ÍBQ Araújo… - Sensors, 2023 - mdpi.com
The use of models capable of forecasting the production of photovoltaic (PV) energy is
essential to guarantee the best possible integration of this energy source into traditional …
essential to guarantee the best possible integration of this energy source into traditional …
A data-driven approach for the health prognosis of high-speed train wheels
Polygonal wear is one of the most critical failure modes of high-speed train wheels that
would significantly compromise the safety and reliability of high-speed train operation …
would significantly compromise the safety and reliability of high-speed train operation …
Analysis of the effectiveness of metaheuristic methods on Bayesian optimization in the classification of visual field defects
Bayesian optimization (BO) is commonly used to optimize the hyperparameters of transfer
learning models to improve the model's performance significantly. In BO, the acquisition …
learning models to improve the model's performance significantly. In BO, the acquisition …
[HTML][HTML] Design of experiments coupled with Bayesian optimisation for nanolubricant formulation
S Elsoudy, S Akl, AA Abdel-Rehim, N Munyebvu… - Colloids and Surfaces A …, 2024 - Elsevier
Nanoparticle-enhanced lubricants (nanolubricants) achieve tribological properties that can
signficantly outperform the unmodified base oil. However, this performance enhancement is …
signficantly outperform the unmodified base oil. However, this performance enhancement is …
Bayesian optimization and hierarchical forecasting of non-weather-related electric power outages
OO Owolabi, DA Sunter - Energies, 2022 - mdpi.com
Power outage prediction is important for planning electric power system response,
restoration, and maintenance efforts. It is important for utility managers to understand the …
restoration, and maintenance efforts. It is important for utility managers to understand the …