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Kim, Johanna Inhyang; Bang, Sungkyu; Yang, Jin-Ju; Kwon, Heejin; Jang, Soomin; Roh, Sungwon; Kim, Seok Hyeon; Kim, Mi Jung; Lee, Hyun Ju; Lee, Jong-Min; Kim, Bung-Nyun – Journal of Autism and Developmental Disorders, 2023
Multimodal imaging studies targeting preschoolers and low-functioning autism spectrum disorder (ASD) patients are scarce. We applied machine learning classifiers to parameters from T1-weighted MRI and DTI data of 58 children with ASD (age 3-6 years) and 48 typically developing controls (TDC). Classification performance reached an accuracy,…
Descriptors: Preschool Children, Autism Spectrum Disorders, Control Groups, Classification
Casey J. Metoyer; Katherine Sullivan; Lee J. Winchester; Mark T. Richardson; Michael R. Esco; Michael V. Fedewa – Measurement in Physical Education and Exercise Science, 2025
Relative adiposity (%Fat) was measured using a smartphone-based application in a convenience sample of adults aged 20-52 years (n = 32, 68.7% female, 84.3% White/Caucasian, 26.7 ± 3.5 kg/m2) across different body positions (Anterior versus Posterior) on consecutive days (Day 1 versus Day 2). A reference photo was obtained from the posterior view…
Descriptors: Adults, Body Composition, Handheld Devices, Computer Assisted Instruction
Stenliden, Linnea; Nissen, Jörgen – Education and Information Technologies, 2022
In a world 'flooded' with data, students in school need adequate tools as Visual Analytics (VA), that easily process mass data, give support in drawing advanced conclusions and help to make informed predictions in relation to societal circumstances. Methods for how the students' insights may be reformulated and presented in 'appropriate' modes are…
Descriptors: Elementary School Students, Visual Aids, Data Analysis, Visualization

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