首页|New Machine Learning Study Findings Have Been Published by Researchers at Univer sity of Vigo [Machine Learning Models to Classify Shiitake Mu shrooms (* * Lentinula edodes* * ) According to Their Geographical Origin Labeli ng]
New Machine Learning Study Findings Have Been Published by Researchers at Univer sity of Vigo [Machine Learning Models to Classify Shiitake Mu shrooms (* * Lentinula edodes* * ) According to Their Geographical Origin Labeli ng]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting out of Ourense, Spain, b y NewsRx editors, research stated, “The shiitake mushroom has gained popularity in the last decade, ranking second in the world for mushrooms consumed, providin g consumers with a wide variety of nutritional and healthy benefits.” The news correspondents obtained a quote from the research from University of Vi go: “It is often not clear the origin of these mushrooms, so it becomes of great importance to the consumers. In this research, different machine learning algor ithms were developed to determine the geographical origin of shiitake mushrooms (* * Lentinula edodes* * ) consumed in Korea, based on experimental data reporte d in the literature (d13C, d15N, d18O, d34S, and origin). Regarding the origin of shiitake in three categories (Korean, Ch inese, and mushrooms from Chinese inoculated sawdust blocks), the random forest model presents the highest accuracy value (0.940) and the highest kappa value (0 .908) for the validation phase. To determine the origin of shiitake mushrooms in two categories (Korean and Chinese, including mushrooms from Chinese inoculated sawdust blocks in the latter ones), the support vector machine model is chosen as the best model due to the high accuracy (0.988) and kappa (0.975) values for the validation phase. Finally, to determine the origin in two categories (Korean and Chinese, but this time including the mushrooms from Chinese inoculated sawd ust blocks in the Korean ones), the best model is the random forest due to its h igher accuracy value (0.952) in the validation phase (kappa value of 0.869). The accuracy values in the testing phase for the best selected models are acceptabl e (between 0.839 and 0.964); therefore, the predictive capacity of the models co uld be acceptable for their use in real applications.”
University of VigoOurenseSpainEuro peCyborgsEmerging TechnologiesMachine Learning
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