36 results on '"Chen, Qiusong"'
Search Results
2. Tree-based machine learning models for enhanced large-scale soil Mn classification by integrating visible-near infrared spectroscopy
3. Using cemented paste backfill to tackle the phosphogypsum stockpile in China: A down-to-earth technology with new vitalities in pollutant retention and CO2 abatement
4. Challenges and future perspectives of machine learning in industrial solid ashes management
5. FIELD: fast mobility evaluation and environmental index for solid ashes with machine learning
6. Identifying the amorphous content in solid ashes: a machine learning approach using an international dataset
7. The reactivity classification of coal fly ash based on the random forest method
8. The application of clustering algorithms for industrial solid ashes based on physicochemical properties
9. Background of machine learning
10. Legal framework for ashes
11. Ash management, recycling, and sustainability
12. The accurate production forecast of solid ashes: application and comparison of machine learning techniques
13. Emerging innovative techniques for ash management
14. Machine learning modeling methodology for industrial solid ash
15. Forecasting the uniaxial compressive strength of solid ash-based concrete
16. Alkali activation of blast furnace slag using Bayer red mud as an alternative activator to prepare cemented paste backfill
17. Hydration process and fluoride solidification mechanism of multi-source solid waste-based phosphogypsum cemented paste backfill under CaO modification
18. Leveraging visible-near-infrared spectroscopy and machine learning to detect nickel contamination in soil: Addressing class imbalances for environmental management
19. Classification of arsenic contamination in soil across the EU by vis-NIR spectroscopy and machine learning
20. Comparative analysis of machine learning algorithms for identifying cobalt contamination in soil using spectroscopy
21. Enhanced solidification/stabilization (S/S) of fluoride in smelting solid waste-based phosphogypsum cemented paste backfill utilizing biochar: Mechanisms and performance assessment
22. Improved classification of soil As contamination at continental scale: Resolving class imbalances using machine learning approach
23. Identifying mining-induced chromium contamination in soil through visible-near infrared spectroscopy and machine learning
24. Mechanical and microstructural analysis of cemented tailings backfill by copper slag through alkaline activation emphasizing red mud
25. Mechanical properties of eco-cement mortar containing MgO-modified phosphorous slag
26. Artificial intelligence-based prediction model for the elemental occurrence form of tailings and mine wastes
27. Effect of enhanced pozzolanic activity in nickel slag modified with Al2O3 on mechanical properties of cemented fine tailings backfill
28. Guidance and review: Advancing mining technology for enhanced production and supply of strategic minerals in China
29. Central hyperthyroidism due to an ectopic TSH-secreting pituitary tumor: a case report and literature review
30. Management Solutions and Stabilization of a Pre-Existing Concealed Goaf Underneath an Open-Pit Slope.
31. Using cemented paste backfill to tackle the phosphogypsum stockpile in China: A down-to-earth technology with new vitalities in pollutant retention and CO2 abatement.
32. Artificial Intelligence Models for Predicting Ground Vibrations in Deep Underground Mines to Ensure the Safety of Their Surroundings.
33. Identifying Nickel Contamination in Soil Using Vis-Nir Data and Machine Learning: Dealing with Imbalanced Datasets
34. Experimental Investigation on Mechanical Properties of Eco-Cement Mortar Containing Mgo-Modified Phosphorous Slag
35. Characterization, Concentration, and Speciation of Metal Elements in Copper Slag: Implications for Secondary Metal Recovery.
36. Study of the Critical Safe Height of Goaf in Underground Metal Mines.
Catalog
Books, media, physical & digital resources
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.