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1621 |
Probabilistic Study of Liquefaction Response of Soil Based on Standard Penetration Test (SPT) Data Using Machine Learning Techniques
Enthalten in Transportation infrastructure geotechnology Bd. 12, 3.12.2024, Nr. 1, date:1.2025: 1-32
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1622 |
Prognosis of air quality index and air pollution using machine learning techniques
Enthalten in Scientific reports Bd. 15, 17.7.2025, Nr. 1, date:12.2025: 1-13
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1623 |
Prospects of non-resonant di-Higgs searches and Higgs boson self-coupling measurement at the HE-LHC using machine learning techniques
Enthalten in Journal of high energy physics Bd. 2020, 28.12.2020, Nr. 12, date:12.2020: 1-47
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1624 |
Quantification of Sugarcane Crop Water Footprint Using Remote Sensing and Machine Learning Techniques: Case Study of Kolhapur District, Maharashtra, India
Enthalten in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. X-5/W2-2025, 2025: 249-256. 8 S.
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1625 |
Quantum data encoding: a comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy
Enthalten in EPJ Quantum Technology Bd. 11, 25.10.2024, Nr. 1, date:12.2024: 1-22
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1626 |
Radiomics for glioblastoma survival analysis in pre-operative MRI: exploring feature robustness, class boundaries, and machine learning techniques
Enthalten in Cancer imaging Bd. 20, 5.8.2020, Nr. 1, date:12.2020: 1-13
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1627 |
Ransomware Detection Service: Execution and Analysis Using Machine Learning Techniques
Enthalten in Wireless personal communications Bd. 133, 21.12.2023, Nr. 2, date:11.2023: 995-1009
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1628 |
Rapid classification of group B Streptococcus serotypes based on matrix-assisted laser desorption ionization-time of flight mass spectrometry and machine learning techniques
Enthalten in BMC bioinformatics Bd. 20, 24.12.2019, Nr. 19, date:12.2019: 1-17
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1629 |
Real-Time Traffic Congestion Information from Tweets Using Supervised and Unsupervised Machine Learning Techniques
Enthalten in Transportation in developing economies Bd. 5, 4.10.2019, Nr. 2, date:10.2019: 1-11
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1630 |
Recommended System for Predicting Traffic Accident Costs using Enhanced Machine Learning Techniques
Enthalten in Journal of electrical engineering & technology Bd. 20, 6.3.2025, Nr. 4, date:5.2025: 2651-2662
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