Results 91 to 100 of about 2,559 (181)

A Deep Reinforcement Learning Methodology to Balance Geoprivacy and Local Search Utility in Location‐Based Services

Transactions in GIS, Volume 30, Issue 5, August 2026.
ABSTRACT The research work addresses the need to preserve the user's privacy in urban contexts without relying on pure contemporary approaches—like k‐anonymity or stochastic noise. We present a privacy preserving methodology that adapts to varying spatial contexts of users.
Omid Reza Abbasi, Franz Welscher, Johannes Scholz   +2 more
wiley   +1 more source

Prediction of soil conditioner dosages for shield tunneling in sandy soil based on machine learning

Case Studies in Construction Materials
Inadequate soil conditioning during Earth Pressure Balance shield (EPBS) tunneling in sandy strata frequently causes operational issues. This study developed a data-driven framework integrating 15 operational and geological parameters from Shenyang Metro
Keqi Liu, Xueyi Liang, Wen Zhao, Xiumei Cao, Hezheng Pei   +4 more
doaj   +1 more source

Temperature Prediction of Wet Clutch Friction Pair Based on Optuna-LSTM Neural Network

Applied Sciences
As critical actuating components in vehicular transmission systems, wet clutches exhibit strongly nonlinear thermal responses in their friction pairs during engagement operations.
Yuqi Yang, Chengyun Su, Zhifei Wang, Chao Zhou, Aolong Zhang   +4 more
doaj   +1 more source

Application of FCEEMD-TSMFDE and adaptive CatBoost in fault diagnosis of complex variable condition bearings

Scientific Reports
The mode mixing problem and inherent mode function selection bias in Fast Ensemble Empirical Mode Decomposition (FEEMD) result in ineffective extraction of fault components during the denoising stage, the loss of coarse-grained information in Multiscale ...
Min Mao, Bingwei Xu, Yuhuan Sun, Kairong Tan, Yuran Wang, Chao Zhou, Chengjiang Zhou, Jingzong Yang   +7 more
doaj   +1 more source

Optimizing Machine Learning Models for Urban Sciences: A Comparative Analysis of Hyperparameter Tuning Methods

Urban Science
Advancing urban scholarship and addressing pressing challenges such as gentrification, housing affordability, and urban sprawl require robust predictive models.
Tris Kee, Winky K.O. Ho
doaj   +1 more source

The Optuna–LightGBM–XGBoost Model: A Novel Approach for Estimating Carbon Emissions Based on the Electricity–Carbon Nexus

Applied Sciences
With the challenge posed by global warming, accurately estimating and managing carbon emissions becomes a key step for businesses, especially power generation companies, to reduce their environmental impact.
Yuanhang Cai, Jianxin Feng, Yanqing Wang, Yuanming Ding, Yue Hu, Hui Fang   +5 more
doaj   +1 more source

Deep Learning Framework With Optuna-Based Hyperparameter Tuning for Predicting Dry Turning Process Performance of 42CrMo4 Steel

IEEE Access
This study presents an integrated experimental and data-driven modeling framework for predicting key machining responses such as tool wear (TW), material removal rate (MRR), and surface roughness (Ra) during dry turning of 42CrMo4 alloy steel ...
Vasanth Siva Kumar, Mogana Priya Chinnasamy, Swetha R Kumar   +2 more
doaj   +1 more source

CO₂ Emission Prediction for Coal-Fired Power Plants by Random Forest-Recursive Feature Elimination-Deep Forest-Optuna Framework

Energies
As the greenhouse effect intensifies, China faces pressure to manage CO2 emissions. Coal-fired power plants are a major source of CO2 in China. Traditional CO2 emission accounting methods of power plants are deficient in computational efficiency and ...
Kezhi Tu, Yanfeng Wang, Xian Li, Xiangxi Wang, Zhenzhong Hu, Bo Luo, Liu Shi, Minghan Li, Guangqian Luo, Hong Yao   +9 more
doaj   +1 more source

Modified Prophet+Optuna Prediction Method for Sales Estimations

International Journal of Advanced Computer Science and Applications, 2022
Kohei Arai, Ikuya Fujikawa, Yusuke Nakagawa, Tatsuya Momozaki, Sayuri Ogawa   +4 more
openaire   +1 more source

Forecasting grocery item sales using gradient boosting models: A study of GridSearchCV, RandomizedSearchCV, and optuna optimization approaches

Ain Shams Engineering Journal
This study introduces predictive modeling based on Extreme Gradient Boosting (XGBoost), which utilizes Optuna for hyperparameter optimization and evaluates performance against GridSearchCV and RandomizedSearchCV using a 500-day dataset.
Puja Supakar, Mitali Sarkar, Biswajit Sarkar   +2 more
doaj   +1 more source