• International Journal of Computer Science & Network Security
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• v.24 no.8
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• pp.184-190
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• 2024

The research involved children and parents of primary school-aged children with cognitive development disorder, as well as scientific and pedagogical workers who organized the psychological and pedagogical principles for organizing distance learning. The purpose of the research lies in establishing how effectively children, parents and their teachers cope with online distance learning during the pandemic, as well as investigating the extent to which such educational technology affects the emotional and behavioural state of the child. The research methodology is based on complexity. In the course of the research, the method of pedagogical experiment was used; observation and questionnaire methods were also introduced; the descriptive method, analysis and synthesis were used to review the theoretical material. The hypothesis lies in the fact that distance online education increases academic difficulties, changes the behavioural and emotional picture of a child with cognitive development disorder; consequently, the behaviour and emotional background will be limited by certain parameters, and this requires the active involvement of parents and teachers in the distance work process. The results of the research have revealed that distance education causes a number of restrictions for children with cognitive development disorder, namely: concentration of attention has decreased, anxiety has increased, and sleep has worsened. Behavioural changes predicted increased restlessness and aggression. Parents and teachers have had methodological, academic and everyday difficulties; all participants in the educational process have been more limited in the conditions of online distance learning. difficulties and improving the behaviour and emotional states of all participants in the educational process.

• 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.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.12 no.1
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• pp.103-114
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• 2001

Tourette's disorder is a disease which manifests one or more motor tics and vocal tics for more than a year. Chronic motor tic or vocal tic disorders are characterized by only one kind of tics for more than a year. We intended to investigate the clinical characteristics of the patients with chronic motor tic disorders or Tourette's disorders who had admitted from May 1, 1998 to May 1, 1999 to Seoul National University Hospital Child and Adolescent Psychiatry ward. In addition, we compared the clinical characteristics of the patients in order to elucidate the relationship between the two disorders. The patients with learning disabilities were selected as controls. There was no statistically significant difference between the onsets of the patients with chronic motor tic disorders(n=13, $7.3{\pm}2.5$ years), and Tourette's disorder(n=39, $7.2{\pm}2.2$ years), but with learning disability($4.2{\pm}1.9$ years). Also, the patients with chronic motor tic disorder and Tourette's disorder showed similar age at admission($11.7{\pm}2.7$ versus $11.5{\pm}2.6$ years), duration of admission($5.7{\pm}5.4$ versus $11.0{\pm}8.7$ weeks), mothers' ages at child birth($27.3{\pm}2.9$ versus $28.3{\pm}6.7$ years old),and fathers' age at child birth($32.2{\pm}3.2$ versus $33.3{\pm}5.2$ years old). We observed that those who had learning disabilities were alike in those aspects, except for age at visit to clinic($9.8{\pm}3.2$ years old). Family history of psychiatric illnesses(24.1% versus 46.2%), recognized precipitating factors(11.1% versus 35.7%) and response to pharmacological treatments(77.8% versus 76.9%) of the patients with chronic motor tic disorders and Tourette's disorders were observed and no differences were found. Comorbid patterns of diseases were noted. Intrafamilial conflicts were more common in the patients with learning disabilities than those with chronic tic disorders or Tourette's disorders. Precipitating factors were observed more frequent in chronic tic disorder and Tourette's disorder than learning disability. Neurocognitive profiles were investigated, and verbal IQs of the patients with chronic motor tic disorder, Tourette's disorder and learning disability were $92.3{\pm}10.7$, $94.7{\pm}14.9$, $94.3{\pm}13.8$, performance IQs $93.0{\pm}20.5$, $97.5{\pm}13.0$, $95.0{\pm}16.9$ and full-scale IQs $91.9{\pm}20.1$, $95.8{\pm}14.5$, $93.9{\pm}15.1$, respectively, which were found to be not significantly different. No difference was found in structural neurological abnormalities and EEG profiles. The patients with learning disabilities showed more common Bender-Gestalt test abnormalities. In conclusion, we have not found any affirmative clues for the division of chronic motor tic disorder and Tourette's disorder in clinical perspective.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.133-140
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• 2010

We improve the performance of cardiac disorder classification by adding new temporal features extracted from continuous heart sound signals. We add three kinds of novel temporal features to a conventional feature based on mel-frequency cepstral coefficients (MFCC): Heart sound envelope, murmur probabilities, and murmur amplitude variation. In cardiac disorder classification and detection experiments, we evaluate the contribution of the proposed features to classification accuracy and select proper temporal features using the sequential feature selection method. The selected features are shown to improve classification accuracy significantly and consistently for neural network-based pattern classifiers such as multi-layer perceptron (MLP), support vector machine (SVM), and extreme learning machine (ELM).

• Journal of Oriental Neuropsychiatry
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• v.20 no.4
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• pp.197-209
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• 2009

Objectives : Attention Deficit Hyperactivity Disorder(ADHD) is a psychiatric disorder, characterized by the primary symptoms of inattention and/or impulsivity and hyperactivity. the aim of this study is to investigate the clinical efficiency of Various Oriental Medical Therapy combinated with Learning therapy in the treatment of ADHD. Methods : This study is a clinical report of 1 ADHD child treated with Various Oriental Medical Therapy(Herb-med, acupunture, etc.) in Combination with Learning therapy. CAR(Conner's abbreviated Rating Scale), K-CBCL, ADS(ADHD Diagnostic System) were compared between before and after treatment. Results : The results show the Various Oriental Medical Therapy in Combination with Learning therapy is efficient in the treatment of ADHD. Conclusions : Various Oriental Medical therapy including herb-med, pharmacopuncture, electroacupuncture is efficient in improvement of ADHD child. also In case of combination with Learning therapy, it is more effective in child's school life and school work. therefore, it is practical useful in the treatment of ADHD.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.55-60
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• 2022

Specific Language Impairment is one of the serious disorders that interferes with spontaneous communication skills in children. Children suffering from this disorder may have reading, speaking, or listening impairments, and such type of disorders are also termed Autism Speech Disorder (ASD) in medical terminology. The aim of the article is to define specific language impairment in children and the problems it can cause. The different methods adopted by speech pathologists to diagnose language impairment. Finally implementing machine learning models to automate the process and help speech pathologists and pediatricians/ in diagnosing the specific language impairment.

• Journal of Computing Science and Engineering
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• v.10 no.4
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• pp.111-117
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• 2016

An accurate approach for diagnosis of attention deficit hyperactivity disorder (ADHD) is presented in this paper. The presented technique efficiently classifies three subtypes of ADHD (ADHD-C, ADHD-H, ADHD-I) and typically developing control (TDC) by using only structural magnetic resonance imaging (MRI). The research examines structural MRI of the hippocampus from the ADHD-200 database. Each available MRI has been processed by a region-of-interest (ROI) to build a set of features for further analysis. The presented ADHD diagnostic approach unifies feature selection and classification techniques. The feature selection technique based on the proposed binary-coded genetic algorithm searches for an optimal subset of features extracted from the hippocampus. The classification technique uses a chosen optimal subset of features for accurate classification of three subtypes of ADHD and TDC. In this study, the famous Extreme Learning Machine is used as a classification technique. Experimental results clearly indicate that the presented BCGA-ELM (binary-coded genetic algorithm coupled with Extreme Learning Machine) efficiently classifies TDC and three subtypes of ADHD and outperforms existing techniques.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.22 no.1
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• pp.16-24
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• 2011

Objectives：This study examined the prevalence of psychiatric problems in children with scholastic difficulties who had been referred for mental health services from the Office of Education in Ulsan Metropolitan City. Methods：Child psychiatrists evaluated the referred children using the DSM-IV. Evaluation tools included the Wechsler Intelligence Scale for Children-III, the Children's Depression Inventory, the Korean form of the State-trait anxiety Inventory for children, the ADHD rating. Results：Seventy-six children consisting of 64 boys (84.2%) and 12 girls (15.8%) participated in the study. The average age was 10.3 (SD=0.93) years old. Approximately 74% of the children referred for scholastic difficulties were diagnosed with mental retardation. The Axis I diagnosis among these children were ADHD (86.8%), depression (21.1%), learning disorder (9.2%), communication disorder (4.8%), pervasive developmental disorder (3.6%), internet addiction (1.3%), and mood disorder (1.3%). Their overall measure according to the Child Depression Inventory was 22.7 (SD=16.8), that for the State-Trait Anxiety Inventory for Children was 33.3 (SD=7.9)/32.4 (SD=9.5), and that for the ADHD rating scale was 18.9 (SD=10.9). Conclusion：These results suggest that many children with scholastic difficulties have both complex psychiatric and educational problems.

• Korean Journal of Adult Nursing
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• v.13 no.3
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• pp.385-396
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• 2001

The purpose of this study is to present an actual example for procedures for developing a PBL package based on philosophical backgrounds derived from Problem-based learning. To perform a systemic study on the operations of an intergrated curricula under multidisciplines, a research team made up of several professors with different academic backgrounds was formed. Among the four situations for the patients with perception-adjustment disorder, especially a procedure for the development of PBL package which can be used in the emergency room situation has been proposed. The little(2000)'s PBL package model has been applied for this study. Tha package includes course objectives, learning objectives, concept map, situation scenario, tutor guide, and evaluation method. It is believed that learning objectives achievement procedures designed as a part of a problem-based learning package development procedures for the nursing of patients with perception-adjustment can be achieved at the same level as the learning objectives for the science of nursing founded by the Korean Nurses Association.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.529-530
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• 2021

본 논문에서는 MindWave와 AR 헤드셋 기기를 연동하여 자폐 스펙트럼 장애 아동이 불안감을 느낄 때 발산되는 뇌파 신호를 실시간으로 감지한다. 또한 실시간 객체 검출을 위한 YOLOv5 알고리즘을 통해 시각적 정보를 수집하여 해당 아동이 불안감을 느끼는 원인을 파악하고 이에 맞는 해결책을 AR 형태로 제시하며 자폐 스펙트럼 장애 아동이 불안감을 느끼면 보호자에게 알림을 전송하는 앱을 구현한다. 이를 통해 자폐 스펙트럼 장애 아동의 뇌파 안정과 정서 회복을 돕고 실생활에서 발생할 수 있는 돌발 상황을 방지할 수 있는 시스템을 제안한다.