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Water quality detection based on a data mining process on the California estuary.

, , , , and . Int. J. Bus. Intell. Data Min., 12 (4): 406-424 (2017)

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Water quality detection based on a data mining process on the California estuary., , , , and . Int. J. Bus. Intell. Data Min., 12 (4): 406-424 (2017)Expert system for coffee rust detection based on supervised learning and graph pattern matching., , , and . Int. J. Metadata Semant. Ontologies, 12 (1): 19-27 (2017)Discovering weather periods and crop properties favorable for coffee rust incidence from feature selection approaches., , , , and . Comput. Electron. Agric., (2020)Volcanic Anomalies Detection Through Recursive Density Estimation., , , , and . MICAI (1), volume 11288 of Lecture Notes in Computer Science, page 299-314. Springer, (2018)Decision Support System for Coffee Rust Control Based on Expert Knowledge and Value-Added Services., , and . ICCSA (2), volume 10405 of Lecture Notes in Computer Science, page 70-83. Springer, (2017)Data Processing for a Water Quality Detection System on Colombian Rio Piedras Basin., , , , and . ICCSA (4), volume 9789 of Lecture Notes in Computer Science, page 665-683. Springer, (2016)Feature selection for classification tasks: Expert knowledge or traditional methods?, , , and . J. Intell. Fuzzy Syst., 34 (5): 2825-2835 (2018)FramePests: A Comprehensive Framework for Crop Pests Modeling and Forecasting., , , and . IEEE Access, (2021)Graph Patterns as Representation of Rules Extracted from Decision Trees for Coffee Rust Detection., , , and . MTSR, volume 544 of Communications in Computer and Information Science, page 405-414. Springer, (2015)