A dynamic ensemble learning with multi-objective optimization for oil prices prediction

J Hao, Q Feng, J Yuan, X Sun, J Li - Resources Policy, 2022 - Elsevier
Accurately predicting oil prices is a challenging task since its complex fluctuation
characteristics. This paper innovatively introduces the “metabolism” mechanism and sliding …

[HTML][HTML] All ridership is local: Accessibility, competition, and stop-level determinants of daily bus boardings in Portland, Oregon

B Cui, J DeWeese, H Wu, DA King, D Levinson… - Journal of Transport …, 2022 - Elsevier
Research on accessibility, a measure of ease of reaching potential opportunities, has
advanced significantly, but the adoption of these measures by public transport agencies has …

[HTML][HTML] A stacking heterogeneous ensemble learning method for the prediction of building construction project costs

U Park, Y Kang, H Lee, S Yun - Applied sciences, 2022 - mdpi.com
The accurate cost estimation of a construction project in the early stage plays a very
important role in successfully completing the project. In the initial stage of construction, when …

Machine Learning Methods to Improve Crystallization through the Prediction of Solute–Solvent Interactions

A Kandaswamy, SP Schwaminger - Crystals, 2024 - mdpi.com
Crystallization plays a crucial role in defining the quality and functionality of products across
various industries, including pharmaceutical, food and beverage, and chemical …

Predicting crowdfunding success with visuals and speech in video ads and text ads

OM Al-Qershi, J Kwon, S Zhao, Z Li - European Journal of Marketing, 2022 - emerald.com
Purpose For the case of many content features, This paper aims to investigate which content
features in video and text ads more contribute to accurately predicting the success of …

[HTML][HTML] On fast simulation of dynamical system with neural vector enhanced numerical solver

Z Huang, S Liang, H Zhang, H Yang, L Lin - Scientific Reports, 2023 - nature.com
The large-scale simulation of dynamical systems is critical in numerous scientific and
engineering disciplines. However, traditional numerical solvers are limited by the choice of …

[HTML][HTML] A Reinforcement Learning Approach for Ensemble Machine Learning Models in Peak Electricity Forecasting

W Pannakkong, VT Vinh, NNM Tuyen… - Energies, 2023 - mdpi.com
Electricity peak load forecasting plays an important role in electricity generation capacity
planning to ensure reliable power supplies. To achieve high forecast accuracy, multiple …

머신러닝을적용한경륜경기순위예측및평가에관한연구: 2016~ 2022 년출주표정보및경주결과활용

김필수, 이상현, 전성삼 - 한국스포츠산업경영학회지, 2023 - dbpia.co.kr
본 연구는 기계학습 (machine learning) 을 적용하여 경륜 경주의 경기 순위를 예측하고, 해당
예측에 활용된 각각의 AI 알고리즘 성능을 비교· 분석하기 위하여 실시되었다. 이를 위해 …

[HTML][HTML] A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes

D Manfre Jaimes, M Zamudio López, H Zareipour… - Forecasting, 2023 - mdpi.com
This paper proposes a new hybrid model to forecast electricity market prices up to four days
ahead. The components of the proposed model are combined in two dimensions. First, on …

An aggressive driving state recognition model using EEG based on stacking ensemble learning

L Yang, Q Zhao - Journal of Transportation Safety & Security, 2024 - Taylor & Francis
An aggressive driving state impacts drivers' decisions, which could potentially lead to
accidents. Real-time recognition of driving state is particularly important for improving road …