• ETRI Journal
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• v.44 no.4
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• The Journal of the Korea Contents Association
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• v.22 no.9
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• pp.520-530
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• 2022

The purpose of this study is to examine the status and perceptions, needs of speech language pathologists(SLPs) for the intervention of non-verbal autism spectrum disorders(ASD) through a survey. Among SLPs registered in the Korean Association of Speech-Language Pathologists (KSLP), 116 SLPs participated in this survey. The result is as follows. First, 96.6% of SLP reported that they had been referred for non-verbal ASD, and it was found that SELSI was the most used evaluation tool, and communication ability and social interaction were the most frequently used intervention goals. Second, 86.2% of the SLP said that speech therapy had difficulties, and the reason for the burden was the lack of speech therapy methods. Also, the level of knowledge of speech therapy for non-verbal ASD was low in the treatment area, and the level of confidence responded positively only in communication ability and social interaction. Third, education that was considered necessary within the curriculum was education on treatment methods, and it was found that the improvement points of education other than regular courses were increased education such as expert courses and workshops and activation of supervision. From the results of this study, it is expected that the related curriculum will be expanded and improved in the future.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.447-456
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• 2021

This study analyzed 28 domestic and foreign studies related Prompts for Restructuring Oral Muscular Phonetic Targets treatment techniques from 1984 to 2020 to prepare basic data for the development of PROMPT intervention programs and examination tools. According to the analysis, continuous research has been conducted since 1984 when the prompt study was first started, and the method of research was 16 intervention studies, with the highest number of speech disorders, and the target age being 3 to 5 years old, the most frequently conducted for infancy. The treatment was the most frequent in the 16th sessions, and the activities were based on the Motor Speech Hierarchy(MSH), except for the subjects of the non-verbal autism spectrum disorder. According to the analysis of the dependent variables, 'speech production' was the most common, followed by 'speech motor control', 'articulation', and 'speech intelligibility' were highest. Combined with all these studies, it suggests that PROMPT, which are directly useful for exercise spoken word production, are effectively being used outside the country and that it is necessary to develop a PROMPT program that can be applied domestically, in Korea.