Results 71 to 80 of about 151,967 (341)

Optimizing SVM for argan tree classification using Sentinel-2 data: A case study in the Sous-Massa Region, Morocco

Revista de Teledetección
The development of efficient classifiers for land cover remains challenging due to the presence of hyperparameters in the model. Conventional approaches rely on manual tuning, which is both time-consuming and impractical, often leading to suboptimal ...
Abdelhak El Kharki, Jamila Mechbouh, Miriam Wahbi, Otmane Yazidi Alaoui, Hakim Boulaassal, Mustapha Maatouk, Omar El Kharki   +6 more
doaj   +1 more source

SHAP-Backed Hybrid Ensemble Model for Rice and Wheat Forecasting in Data-Scarce Environments [PDF]

International Journal of Mathematical, Engineering and Management Sciences
As the issue of agricultural sustainability has continued to increase, there has been a need to use data based solutions to improve agricultural productivity.
Harendra Singh Negi, Sushil Chandra Dimri   +1 more
doaj   +1 more source

I Choose You: Automated Hyperparameter Tuning for Deep Learning-Based Side-Channel Analysis

IEEE Transactions on Emerging Topics in Computing
Today, the deep learning-based side-channel analysis represents a widely researched topic, with numerous results indicating the advantages of such an approach.
Li-Chao Wu, Guilherme Perin, S. Picek
semanticscholar   +1 more source

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

Advanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang, Adrian Ehrenhofer, Thomas Wallmersperger   +2 more
wiley   +1 more source

On Hyperparameter Optimization for Deep Learning [PDF]

, 2020
Deep learning has recently achieved many breakthroughs. Neural networks - the models behind deep learning - have a large number of hyperparameters whose correct settings are crucial to obtain optimal performance.
Hertel, Lars Heinrich
core   +1 more source

A Bayesian Approach Based on Bayes Minimum Risk Decision for Reliability Assessment of Web Service Composition

Future Internet, 2020
Web service composition is the process of combining and reusing existing web services to create new business processes to satisfy specific user requirements. Reliability plays an important role in ensuring the quality of web service composition. However,
Yang Song, Yawen Wang, Dahai Jin
doaj   +1 more source

Automatic hyperparameter selection in Autodock [PDF]

2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2018
Autodock is a widely used molecular modeling tool which predicts how small molecules bind to a receptor of known 3D structure. The current version of AutoDock uses meta-heuristic algorithms in combination with local search methods for doing the conformation search.
Rakhshani, Hojjat, Idoumghar, Lhassane, Lepagnot, Julien, Brévilliers, Mathieu, Keedwell, Edward   +4 more
openaire   +5 more sources

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

A Novel Deep Learning Architecture Optimization for Multiclass Classification of Alzheimer’s Disease Level

IEEE Access
Alzheimer’s disease is a neurodegenerative disorder prevalent in older adults, and early diagnosis is crucial for effective treatment. A deep learning model can automatically classify Alzheimer’s disease from magnetic resonance imaging to ...
Mahir Kaya, Yasemin Cetin-Kaya
doaj   +1 more source