Results 101 to 110 of about 983,800 (290)

A Comprehensive Study on K-Fold Cross-Validation for Hyperparameter Tuning of Deep Neural Network

Journal of Engineering
Nested cross-validation is widely used for evaluating hyperparameter-tuning strategies, but its effectiveness depends strongly on the choice of fold numbers, particularly the outer and inner.
Siwaporn Kunnapapdeelert, Tossawat Chantanom, Nicharee Manakitrungrueng, Nguyen Trong Tri Duc, Jirachai Buddhakulsomsiri, Pornprom Ateetanan, Pham Duc Tai   +6 more
doaj   +1 more source

A New Hyperparameter Tuning Framework for Regression Tasks in Deep Neural Network: Combined-Sampling Algorithm to Search the Optimized Hyperparameters

Mathematics
This paper introduces a novel hyperparameter optimization framework for regression tasks called the Combined-Sampling Algorithm to Search the Optimized Hyperparameters (CASOH).
Tiep Huu Nguyen, Hae-Yong Jeong, Kyungdoo Kim, Mung Xuan Nguyen, Nhu-Ngoc Dao, Hoai-Nam Tran, Van-Khanh Hoang, A. Nguyễn, M. Vu   +8 more
semanticscholar   +1 more source

Machine Learning for Green Solvents: Assessment, Selection and Substitution

Advanced Science, EarlyView.
Environmental regulations have intensified demand for green solvents, but discovery is limited by Solvent Selection Guides (SSGs) that quantify solvent sustainability. Training a machine learning model on GlaxoSmithKline SSG, a database of sustainability metrics for 10,189 solvents, GreenSolventDB is developed. Integrated with Hansen solubility metrics,
Rohan Datta, Janhavi Nistane, Abhishek Sose, Harikrishna Sahu, Rampi Ramprasad   +4 more
wiley   +1 more source

Hybrid photovoltaic/thermal performance prediction based on machine learning algorithms with hyper-parameter tuning

International Journal of Sustainable Energy
A hybrid Photovoltaic/Thermal(PV/T) approach is proposed in this study based on extensive research and a comparative analysis of several hyperparameter tuning methods. The models analyzed are Linear Regression (LR), Random Forest (RF), XGBoost Regression,
Karthikeyan Ganesan, Satheeshkumar Palanisamy, Valarmathi Krishnasamy, Ayodeji Olalekan Salau, Vinoth Rathinam, Sankar Ganesh Seeni Nayakkar   +5 more
doaj   +1 more source

Microscale Mapping of Fiber Strain and Damage in Composite Wrinkled Laminates Using Computed Tomography Assisted Wide‐Angle X‐Ray Scattering

Advanced Science, EarlyView.
This study combines full‐field tomography with diffraction mapping to quantify radial (ε002$\varepsilon _{002}$) and axial (ε100$\varepsilon _{100}$) lattice strain in wrinkled carbon‐fiber specimens for the first time. Radial microstrain gradients (−14.5 µεMPa$\varepsilon \mathrm{MPa}$−1) are found to signal damage‐prone zones ahead of failure, which ...
Hoang Minh Luong, James Trevarthen, Igor Dolbnya, Wadwan Singhapong, Nicholas Pilato, Richard Butler, Jiraphant Srisuriyachot, Alexander J. G. Lunt   +7 more
wiley   +1 more source

Federated Learning With Automated Dual-Level Hyperparameter Tuning

IEEE Open Journal of Signal Processing
Federated Learning (FL) is a decentralized machine learning (ML) approach where multiple clients collaboratively train a shared model over several update rounds without exchanging local data.
Rakib Ul Haque, Panagiotis Markopoulos
doaj   +1 more source

Neural Fields for Highly Accelerated 2D Cine Phase Contrast MRI

Advanced Science, EarlyView.
ABSTRACT 2D cine phase contrast (CPC) MRI provides quantitative information on blood velocity and flow within the human vasculature. However, data acquisition is time‐consuming, motivating the reconstruction of the velocity field from undersampled measurements to reduce scan times. In this work, neural fields are proposed as a continuous spatiotemporal
Pablo Arratia, Martin J. Graves, Mary McLean, Carolin Pirkl, Carola‐Bibiane Schönlieb, Timo Schirmer, Florian Wiesinger, Matthias J. Ehrhardt   +7 more
wiley   +1 more source

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

Advanced Science, EarlyView.
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju, Adithya Umanath Rai, Jose Thomson, Marzie Ghorbani, Shujing Zhao, Nick Birbilis   +5 more
wiley   +1 more source

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

Advanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan, Hengheng Zhao, Haotian Wang, Zhanjie Liu, Qian Li, Weifeng Zhang, Qingsong Zhao, Liqun Zhang, Jun Liu   +8 more
wiley   +1 more source

Hyperparameter Tuning of Load-Forecasting Models Using Metaheuristic Optimization Algorithms—A Systematic Review

Mathematics
Load forecasting is an integral part of the power industries. Load-forecasting techniques should minimize the percentage error while prediction future demand. This will inherently help utilities have an uninterrupted power supply.
U. Mumtahina, S. Alahakoon, Peter J. Wolfs   +2 more
semanticscholar   +1 more source