首页|Data on Machine Learning Reported by Andrea Valsecchi and Colleagues (Informatio n fusion for infant age estimation from deciduous teeth using machine learning)
Data on Machine Learning Reported by Andrea Valsecchi and Colleagues (Informatio n fusion for infant age estimation from deciduous teeth using machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating from Ponferrada, Spain, by NewsRx correspondents, research stated, "Over the past few years, seve ral methods have been proposed to improve the accuracy of age estimation in infa nts with a focus on dental development as a reliable marker. However, traditiona l approaches have limitations in efficiently combining information from differen t teeth and features." Financial support for this research came from Universidad de Granada. Our news editors obtained a quote from the research, "In order to address these challenges, this article presents a study on age estimation in infants with Mach ine Learning (ML) techniques, using deciduous teeth. The involved dataset compri ses 114 infant skeletons from the Granada osteological collection of identified infants, aged between 5 months of gestation and 3 years of age. The samples cons ist of features such as the maximum length and mineralization and alveolar stage s of teeth. For the purpose of designing a method capable of combining all the i nformation available from each individual, a Multilayer Perceptron model is prop osed, one of the most popular artificial neural networks. This model has been va lidated using the leave-one-out experimental validation protocol. Through differ ent groups of experiments, the study examines the informativeness of the aforeme ntioned features, individually and in combination. The results indicate that the fusion of different variables allows for more accurate age estimates (RMSE = 66 days) than when variables are analyzed separately (RMSE = 101 days). Additional ly, the study demonstrates the benefits of involving multiple teeth, which signi ficantly reduces the RMSE compared to a single tooth." According to the news editors, the research concluded: "This article underlines the clear advantages of ML-based methods, emphasizing their potential to improve the accuracy and robustness when estimating the age of infants."
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