What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Prospectivity mapping, Geochemistry, Mineralogy, Data mining and Spatial analysis are his primary areas of study.
Emmanuel John M. Carranza combines subjects such as Data-driven and Random forest with his study of Prospectivity mapping.
His Geochemistry research incorporates themes from Mineralization, Stockwork and Fault.
Emmanuel John M. Carranza has researched Mineralogy in several fields, including Sediment, Hydrothermal circulation, Mineral exploration, Frequency distribution and Anomaly.
Emmanuel John M. Carranza has included themes like Fuzzy set, Membership function, Fuzzy logic, Artificial intelligence and Plot in his Data mining study.
His Spatial analysis study combines topics from a wide range of disciplines, such as Prediction rate and Geographic information system.
His most cited work include:
- Arsenic geochemistry and health (414 citations)
- Geochemical Anomaly and Mineral Prospectivity Mapping in Gis (311 citations)
- Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN) (249 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Geochemistry, Prospectivity mapping, Mineralogy, Mineralization and Mineral exploration.
His Geochemistry research focuses on Galena and how it relates to δ34S and Evaporite.
His Prospectivity mapping study also includes fields such as
- Data mining, which have a strong connection to Fuzzy logic,
- Spatial analysis together with Geographic information system.
His studies in Mineralogy integrate themes in fields like Sediment, Anomaly and Principal component analysis.
Mineralization is a subfield of Hydrothermal circulation that he explores.
His Mineral exploration study frequently draws connections to adjacent fields such as Mineral deposit.
He most often published in these fields:
- Geochemistry (71.29%)
- Prospectivity mapping (25.81%)
- Mineralogy (27.10%)
What were the highlights of his more recent work (between 2019-2021)?
- Geochemistry (71.29%)
- Prospectivity mapping (25.81%)
- Mineral resource classification (13.23%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Geochemistry, Prospectivity mapping, Mineral resource classification, Mineral exploration and Mineral.
His Geochemistry research is multidisciplinary, incorporating elements of Tetradymite, Fluid inclusions and Petzite.
His Prospectivity mapping study incorporates themes from Mineral deposit, Data mining, Support vector machine, Fuzzy logic and Random forest.
His Mineral resource classification research includes elements of Ellipse, Window and Sustainable development.
His study in Mineral exploration is interdisciplinary in nature, drawing from both Multi-source, Earth science, Kriging and Interpolation.
His Metamorphic rock course of study focuses on Mineralization and Mineralogy.
Between 2019 and 2021, his most popular works were:
- Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm (15 citations)
- 3D Mineral Prospectivity Mapping with Random Forests: A Case Study of Tongling, Anhui, China (11 citations)
- Modeling of Cu-Au prospectivity in the Carajás mineral province (Brazil) through machine learning: Dealing with imbalanced training data (8 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
His primary areas of study are Geochemistry, Mineral resource classification, Prospectivity mapping, Mineral exploration and Algorithm.
His research brings together the fields of Multi-source and Geochemistry.
His research integrates issues of Carbonate rock, Window and Multifractal system in his study of Mineral resource classification.
His studies deal with areas such as Sedimentary rock, Mineral deposit, Metamorphic rock and Precambrian as well as Prospectivity mapping.
He interconnects Wall rock, Hydrothermal circulation, Computer simulation and Petrology in the investigation of issues within Mineral exploration.
The concepts of his Algorithm study are interwoven with issues in Exploratory data analysis, Multivariate statistics and Fractal analysis.
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