Enhanced lithology identification with few-shot well-logging data using a Confidence-Enhanced Semi-Supervised Meta-Learning Approach

Measurement, Год журнала: 2025, Номер unknown, С. 116762 - 116762

Опубликована: Янв. 1, 2025

Язык: Английский

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Machine Learning Assisted Wavelength Recognition in Cu₂O/Si Self-Powered Photodetector Arrays for Advanced Image Sensing Applications

ACS Applied Electronic Materials, Год журнала: 2025, Номер 7(1), С. 225 - 235

Опубликована: Янв. 1, 2025

The ability of a photodetector array (PDA) to detect multiple wavelengths significantly expands its range potential applications. However, effectively detecting and distinguishing between different wavelength bands remains challenge for these arrays. This study introduces an approach recognition in PDAs by integrating machine learning techniques with solution-processed Cu2O/Si heterojunction photodetectors. We propose simple solution-processing method fabricate PDA consisting 4 × p-Cu2O/n-Si photodiodes. involves low-power UV irradiation molecular precursor film containing Cu (II) complexes produce p-type Cu2O thin on Si substrate. A UV-shielding glass plate is used as patterning mask, water wash away the UV-shielded areas. Using techniques, we classify various light, including UV, visible, near-infrared, accurately predict their corresponding photocurrents heterojunction. Notably, enables clear identification images across light wavelengths. paves way advanced applications multispectral imaging sensing technologies.

Язык: Английский

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Support vector machine-based prediction model for the compressive strength for concrete reinforced with waste plastic and fly ash

Asian Journal of Civil Engineering, Год журнала: 2025, Номер unknown

Опубликована: Янв. 6, 2025

Язык: Английский

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Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

Journal of Human Earth and Future, Год журнала: 2024, Номер 5(2), С. 216 - 242

Опубликована: Июнь 1, 2024

The management and monitoring of land use in geothermal fields are crucial for the sustainable utilization water resources, as well striking a balance between production renewable energy preservation environment. This study primarily compared Support Vector Machine (SVM) Random Forest (RF) machine learning methods, using satellite imagery from Landsat 8 Sentinel 2 2021 2023, to monitor Patuha area. objective is improve practices by accurately categorizing different cover types. comparative analysis assessed efficacy these techniques upholding sustainability regions. examined application SVM RF techniques, with particular emphasis on parameter refinement model assessment, enhance classification accuracy. By employing Kernlab e1071 algorithm comparison, research sought produce precise Land Use Model Map, which underscores significance advanced analytical environmental management. approach was utmost importance improving reinforcing practices. evaluation methods demonstrates superiority terms accuracy, stability, precision, particularly intricate urban settings, hence establishing it preferred tasks demanding high reliability. areas alignment Sustainable Development Goals (SDGs) 6 15, fosters conservation ecosystems. Doi: 10.28991/HEF-2024-05-02-06 Full Text: PDF

Язык: Английский

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Prediction of hydrogen solubility in aqueous solution using modified mixed effects random forest based on particle swarm optimization for underground hydrogen storage

International Journal of Hydrogen Energy, Год журнала: 2024, Номер 87, С. 373 - 388

Опубликована: Сен. 9, 2024

Язык: Английский

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An integrated GEE and machine learning framework for detecting ecological stability under land use/land cover changes

Global Ecology and Conservation, Год журнала: 2024, Номер 53, С. e03010 - e03010

Опубликована: Май 27, 2024

Ecological stability (ES) is recognized as a crucial factor for sustainable development at global and regional scales. However, the importance of this was not considered significant. Hence, main aim study to introduce new approach that focuses on detecting ES over Maharloo watershed in Iran. To achieve goal, we extracted land use cover (LULC) data from Google Earth Engine (GEE) platform by applying random forest (RF) machine learning method, which obtained Kappa statistics 0.85, 0.86, 0.87 years 2002, 2013, 2023, respectively. We identified both stable unstable regions based LULC changes employed them using forecast ES. The most important predictors ecological were elevation, soil organic carbon index, precipitation, salinity. results research revealed certain areas within have experienced instability recent years, with gardens showing highest percentage (60.65%) among all land-use categories. performance validation our model suggest are reliable (AUC = 0.86). This offers detailed maps trends, offering valuable insights decision makers support landscape conservation restoration efforts. Overall, findings contribute more comprehensive understanding dynamics provide efforts other regions.

Язык: Английский

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Assessing rural land use in contemporary China: Data compilation and methodology

Heliyon, Год журнала: 2024, Номер 10(11), С. e31939 - e31939

Опубликована: Июнь 1, 2024

Язык: Английский

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Evaluating the performance of random forest, support vector machine, gradient tree boost, and CART for improved crop-type monitoring using greenest pixel composite in Google Earth Engine

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(4)

Опубликована: Март 19, 2025

Язык: Английский

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Long-term tracking of urban structure and analysis of its impact on urban heat stress: a case study of Xi’an, China

Ecological Indicators, Год журнала: 2025, Номер 174, С. 113418 - 113418

Опубликована: Апрель 9, 2025

Язык: Английский

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Using machine learning techniques to identify major determinants of electricity usage in residential buildings of Pakistan

Journal of Building Engineering, Год журнала: 2025, Номер 100, С. 111800 - 111800

Опубликована: Янв. 7, 2025

Язык: Английский

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