Chongchong Qi
Orcid: 0000-0001-5189-1614
According to our database1,
Chongchong Qi
authored at least 15 papers
between 2018 and 2024.
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Bibliography
2024
Rapid estimation of soil Mn content by machine learning and soil spectra in large-scale.
Ecol. Informatics, 2024
MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease Recognition from Fundus Images.
CoRR, 2024
Classification of arsenic contamination in soil across the EU by vis-NIR spectroscopy and machine learning.
Int. J. Appl. Earth Obs. Geoinformation, 2024
2023
Optimization of neural network parameters in improvement of particulate matter concentration prediction of open-pit mining.
Appl. Soft Comput., November, 2023
2022
Improved Permeability Prediction of Porous Media by Feature Selection and Machine Learning Methods Comparison.
J. Comput. Civ. Eng., 2022
2021
Permeability prediction of porous media using a combination of computational fluid dynamics and hybrid machine learning methods.
Eng. Comput., 2021
2020
Metaheuristic Optimization Algorithms Hybridized With Artificial Intelligence Model for Soil Temperature Prediction: Novel Model.
IEEE Access, 2020
2019
Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq.
Comput. Electron. Agric., 2019
Distribution Characteristics of Fragments Size and Optimization of Blasting Parameters Under Blasting Impact Load in Open-Pit Mine.
IEEE Access, 2019
Proceedings of the 2019 IEEE International Conference on Real-time Computing and Robotics, 2019
2018
Back-Analysis Method for Stope Displacements Using Gradient-Boosted Regression Tree and Firefly Algorithm.
J. Comput. Civ. Eng., 2018
Comparative Study of Hybrid Artificial Intelligence Approaches for Predicting Hangingwall Stability.
J. Comput. Civ. Eng., 2018
Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study.
Comput. Ind. Eng., 2018
A hybrid method for improved stability prediction in construction projects: A case study of stope hangingwall stability.
Appl. Soft Comput., 2018
Evolutionary Random Forest Algorithms for Predicting the Maximum Failure Depth of Open Stope Hangingwalls.
IEEE Access, 2018