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27361 |
Spatial modulation and recurrent neural network based equalizer for MIMO communication systems
Enthalten in International journal of information technology Bd. 16, 27.7.2024, Nr. 7, date:10.2024: 4573-4587
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27362 |
Spatial omics-based machine learning algorithms for the early detection of hepatocellular carcinoma
Enthalten in Communications medicine Bd. 4, 3.12.2024, Nr. 1, date:12.2024: 1-11
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27363 |
Spatial prediction of flood susceptible areas using machine learning methods in the Siahkhor Watershed of Kermanshah province
Enthalten in Earth science informatics Bd. 18, 10.12.2024, Nr. 1, date:1.2025: 1-13
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27364 |
Spatial prediction of forest fires in India: a machine learning approach for improved risk assessment and early warning systems
Enthalten in Environmental science and pollution research Bd. 32, 1.2.2025, Nr. 8, date:2.2025: 4856-4878
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27365 |
Spatial prediction of groundwater levels using machine learning and geostatistical models: a case study of coastal faulted aquifer systems in southeastern Tunisia
Enthalten in Hydrogeology journal Bd. 31, 22.8.2023, Nr. 6, date:9.2023: 1387-1404
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27366 |
SPATIAL PREDICTION OF RECEIVED SIGNAL STRENGTH FOR CELLULAR COMMUNICATION USING SUPPORT VECTOR MACHINE AND K-NEAREST NEIGHBOURS REGRESSION
Enthalten in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Bd. XLVIII-4/W9-2024, 2024: 291-297. 7 S.
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27367 |
Spatial prediction of soil contamination based on machine learning: a review
Enthalten in Frontiers of environmental science & engineering Bd. 17, 17.2.2023, Nr. 8, date:8.2023: 1-17
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27368 |
Spatial prediction of soil micronutrients using machine learning algorithms integrated with multiple digital covariates
Enthalten in Nutrient cycling in agroecosystems Bd. 127, 13.8.2023, Nr. 1, date:9.2023: 137-153
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27369 |
Spatial prediction of soil organic carbon stocks in an arid rangeland using machine learning algorithms
Enthalten in Environmental monitoring and assessment Bd. 193, 17.11.2021, Nr. 12, date:12.2021: 1-17
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27370 |
Spatial predictions of groundwater potential using automated machine learning (AutoML): a comparative study of feature selection and training sample size in Qinghai Province, China
Enthalten in Environmental science and pollution research Bd. 31, 1.12.2023, Nr. 1, date:1.2024: 1127-1145
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