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Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity.

, , , , , and . Neural Comput. Appl., 33 (8): 3299-3310 (2021)

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Deep Learning Framework for Categorical Emotional States Assessment Using Electrodermal Activity Signals., , and . ICIMTH, volume 305 of Studies in Health Technology and Informatics, page 40-43. IOS Press, (2023)Differential Gene Expression Data Analysis of ASD Using Random Forest., , , and . MIE, volume 302 of Studies in Health Technology and Informatics, page 1047-1051. IOS Press, (2023)Multi-Class Seizure Type Classification Using Features Extracted from the EEG., , , , , and . ICIMTH, volume 305 of Studies in Health Technology and Informatics, page 68-71. IOS Press, (2023)Automated Diagnosis of Autism Spectrum Disorder Condition Using Shape Based Features Extracted from Brainstem., , , , , and . MIE, volume 294 of Studies in Health Technology and Informatics, page 53-57. IOS Press, (2022)Low-Power Hardware Accelerator for Detrending Measured Biopotential Data., , , , and . IEEE Trans. Instrum. Meas., (2021)Time-Sliced Architecture for Efficient Accelerator to Detrend High-Definition Electroencephalograms., , and . IEEE Trans. Instrum. Meas., (2022)Segmentation and analysis of brain subcortical regions using regularized multiphase level set in autistic MR images., , and . Int. J. Imaging Syst. Technol., 24 (3): 256-262 (2014)Optimization of Pre-Ictal Interval Time Period for Epileptic Seizure Prediction Using Temporal and Frequency Features., , , , , and . MIE, volume 302 of Studies in Health Technology and Informatics, page 232-236. IOS Press, (2023)Performance of machine learning classification models of autism using resting-state fMRI is contingent on sample heterogeneity., , , , , and . Neural Comput. Appl., 33 (8): 3299-3310 (2021)Study on the effect of extreme learning machine and its variants in differentiating Alzheimer conditions from selective regions of brain MR images., , , and . Expert Syst. Appl., (2022)