Results 91 to 100 of about 1,984 (174)

Improving prediction of PM _2.5 in Metro Manila using XGBoost with Optuna hyperparameter optimization

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Abstract. Reporting air pollution levels in Metro Manila, Philippines remains dependent on records from few ground monitoring stations. For impact studies on human health, grid-based pollution levels datasets will enhance the assessment of the exposure and risk of the local population with a finer spatial resolution.
Roseanne V. Ramos, Alvin Christopher G. Varquez   +1 more
openaire   +1 more source

Predicting the hydraulic conductivity of fly ash-clay landfill liners with interpretable ensemble learning

Cleaner Materials
The hydraulic conductivity (HC) is critical for assessing the long-term performance of landfill liners. The HC of compacted fly ash‒clay mixes was modeled using six tree-based ensembles, including RF, GBR, XGB, BR, HGBR, and ABR.
Manikanta Devarangadi, Syed Sadath Ali, Praveen Ashok Madalageri, Akash Biradar, Divesh Ranjan Kumar, Warit Wipulanusat   +5 more
doaj   +1 more source

Improving Machine Failure Prediction with Grey Wolf, Whale Optimization, and Optuna Techniques

Gazi University Journal of Science Part A: Engineering and Innovation
Machine failure prediction is crucial for minimizing downtime and optimizing maintenance strategies in industrial settings. This study aims to enhance the accuracy of machine failure prediction models by integrating advanced hyperparameter optimization techniques with feature selection methods.
openaire   +2 more sources

Gas Outburst Warning Method in Driving Faces: Enhanced Methodology through Optuna Optimization, Adaptive Normalization, and Transformer Framework. [PDF]

Sensors (Basel)
Yan Z, Qin Z, Fan J, Huang Y, Wang Y, Zhang J, Zhang L, Cao Y.   +7 more
europepmc   +1 more source

Prediction of Ligand Binding to Transthyretin Using Machine Learning Algorithms and Low-Dimensional Molecular Descriptors: A Tox24 Challenge Study. [PDF]

Chem Res Toxicol
Stefaniak F.
europepmc   +1 more source

Heart disease prediction using rough neutrosophic sets and dual-attention neural networks: RNS-OptiDANet. [PDF]

Front Artif Intell
Ashika T, Hannah Grace G.
europepmc   +1 more source

Accelerating photonic gas sensor design: machine learning-driven inverse optimization of silicon photonics Bragg gratings. [PDF]

Sci Rep
Khafagy M, Khafagy M, Swillam MA.
europepmc   +1 more source

Enhanced meta ensemble stacking approach with XGBoost and optuna based detection of Parkinson's disease. [PDF]

Front Digit Health
Joseph Raj AM, Kruthika SL, S A.
europepmc   +1 more source

OPTUNA Optimization Based CNN-LSTM Model for Predicting Electric Power Consumption

, 2020
Forecasting residential energy consumption using deep neural networks has been attempted in past researches. Typically, optimizing these networks relies on the operator’s prior knowledge. They are also affected by the size of the search space and the tuning parameters for the model.
openaire   +1 more source

Generative adversarial networks and hyperparameter-optimized XGBoost for enhanced heart disease prediction. [PDF]

Sci Rep
Begum SS, Swamy A, Dhanka S, Abu-Shareha AA, Khan M, Husain I.   +5 more
europepmc   +1 more source