Výsledky vyhledávání - "Dunlap, Kaitlyn"

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Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder

Autor: Huynh, Marie, Kline, Aaron, Surabhi, Saimourya, Dunlap, Kaitlyn, Mutlu, Onur Cezmi, Honarmand, Mohammadmahdi, Azizian, Parnian, Washington, Peter, Wall, Dennis P.

Early detection of autism, a neurodevelopmental disorder marked by social communication challenges, is crucial for timely intervention. Recent advancements have utilized naturalistic home videos captured via the mobile application GuessWhat. Through

Externí odkaz: http://arxiv.org/abs/2408.13255

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An Exploration of Active Learning for Affective Digital Phenotyping

Autor: Washington, Peter, Mutlu, Cezmi, Kline, Aaron, Hou, Cathy, Dunlap, Kaitlyn, Kent, Jack, Husic, Arman, Stockham, Nate, Chrisman, Brianna, Paskov, Kelley, Jung, Jae-Yoon, Wall, Dennis P.

Some of the most severe bottlenecks preventing widespread development of machine learning models for human behavior include a dearth of labeled training data and difficulty of acquiring high quality labels. Active learning is a paradigm for using alg

Externí odkaz: http://arxiv.org/abs/2204.01915

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Classifying Autism from Crowdsourced Semi-Structured Speech Recordings: A Machine Learning Approach

Autor: Chi, Nathan A., Washington, Peter, Kline, Aaron, Husic, Arman, Hou, Cathy, He, Chloe, Dunlap, Kaitlyn, Wall, Dennis

Autism spectrum disorder (ASD) is a neurodevelopmental disorder which results in altered behavior, social development, and communication patterns. In past years, autism prevalence has tripled, with 1 in 54 children now affected. Given that traditiona

Externí odkaz: http://arxiv.org/abs/2201.00927

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Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study

Autor: Washington, Peter, Kalantarian, Haik, Kent, John, Husic, Arman, Kline, Aaron, Leblanc, Emilie, Hou, Cathy, Mutlu, Onur Cezmi, Dunlap, Kaitlyn, Penev, Yordan, Varma, Maya, Stockham, Nate Tyler, Chrisman, Brianna, Paskov, Kelley, Sun, Min Woo, Jung, Jae-Yoon, Voss, Catalin, Haber, Nick, Wall, Dennis Paul

Publikováno v: JMIR pediatrics and parenting 5.2 (2022): e26760

Background: Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult

Externí odkaz: http://arxiv.org/abs/2012.08678

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