• ETRI Journal
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• 제44권4호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2012-2027
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• 2019

Nutritional disorders are one of the most common diseases of crops and they often result in significant loss of agricultural output. Moreover, the imbalance of nutrition element not only affects plant phenotype but also threaten to the health of consumers when the concentrations above the certain threshold. A number of disease identification systems have been proposed in recent years. Either the time consuming or accuracy is difficult to meet current production management requirements. Moreover, most of the systems are hard to be extended, only detect a few kinds of common diseases with great difference. In view of the limitation of current approaches, this paper studies the effects of different trace elements on crops and establishes identification system. Specifically, we analysis and acquire eleven types of tomato nutritional disorders images. After that, we explore training and prediction effects and significances of super resolution of identification model. Then, we use pre-trained enhanced deep super-resolution network (EDSR) model to pre-processing dataset. Finally, we design and implement of diagnosis system based on deep learning. And the final results show that the average accuracy is 81.11% and the predicted time less than 0.01 second. Compared to existing methods, our solution achieves a high accuracy with much less consuming time. At the same time, the diagnosis system has good performance in expansibility and portability.

• Clinical and Experimental Pediatrics
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• 제51권9호
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• pp.911-921
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• 2008

Learning disabilities (LD) are heterogeneous group of disorders with evidences of genetic or familial trait, intrinsic to the individual and presume to be due to central nervous dysfunction. Learning disabilities and attention deficit hyperactivity disorder (ADHD) are the two of the most common disorders in the population of school-age children. Typically academic achievements in children with learning disabilities are significantly lower than expected by their normal or above normal range of IQ. Although academic and cognitive deficits are hallmarks of children with LD, those children are also at risk for a broad range of behavioral and emotional problems. Almost all cases meet criteria for at least one additional diagnosis such as ADHD, developmental coordination disorder, depression, anxiety, obsessive compulsive disorder, tic disorder, among which ADHD is particularly predominant. Because of the response to the therapeutic intervention program is promising and positive when applied early, it is critical to recognize patients as early as possible. Pediatricians often are the first to hear from parents worried about a childs academic progress. It is not the responsibility of pediatrician to make a diagnosis, referring children for a diagnostic evaluation of LD is a reasonable first step. Pediatricians can make early referral of suspicious children by asking some serial short questions about basic and processing skills. With a basic knowledge about the clinical characteristics, diagnostic and therapeutic procedures of LD, pediatricians also can provide primary counseling and education for parents at their outpatient clinical settings.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.728-735
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• 2022

Roofers get exposed to increased risk of knee musculoskeletal disorders (MSDs) at different phases of a sloped shingle installation task. As different phases are associated with different risk levels, this study explored the application of machine learning for automated classification of seven phases in a shingle installation task using knee kinematics and roof slope information. An optical motion capture system was used to collect knee kinematics data from nine subjects who mimicked shingle installation on a slope-adjustable wooden platform. Four features were used in building a phase classification model. They were three knee joint rotation angles (i.e., flexion, abduction-adduction, and internal-external rotation) of the subjects, and the roof slope at which they operated. Three ensemble machine learning algorithms (i.e., random forests, decision trees, and k-nearest neighbors) were used for training and prediction. The simulations indicate that the k-nearest neighbor classifier provided the best performance, with an overall accuracy of 92.62%, demonstrating the considerable potential of machine learning methods in detecting shingle installation phases from workers knee joint rotation and roof slope information. This knowledge, with further investigation, may facilitate knee MSD risk identification among roofers and intervention development.

• 한국정보시스템학회지:정보시스템연구
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• 제31권3호
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• pp.47-65
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• 2022

Purpose With the recent development of Big Data and Artificial Intelligence technology, self-driving technology has developed into three stages (partial self-driving) or four stages (conditional self-driving), it is expected to bring a new paradigm to transportation in the city. Although many researchers are researching related technologies, there is no research on self-driving for disabled persons. In this study, the basic research was conducted based on the assumption that the shared self-driving car used by the disabled person is similar to the special transportation currently driving. Design In this study, data analysis and machine learning techniques were utilized to analyze the mobility patterns of disabled persons by type and to search for leading factors affecting the traffic volume of special transportation. Findings The study found that external physical disorders and developmental disorders often visit general welfare centers, internal organ disorders often visit general hospitals, and the elderly and mental disorders have various destinations. In addition, machine learning analysis showed that the main transportation routes for the disabled person use arterial roads and auxiliary arterial roads and that the ratio of building usage-related variables affecting the use of special transportation for a disabled person is high. In addition, the distance to the subway and bus stops was also mentioned as a meaningful variable. Based on these analysis results, it is expected that the necessary infrastructure for shared self-driving cars for disability person traffic will be used as meaningful research data in the future.

• 대한치매학회지
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• 제17권3호
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• pp.83-89
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• 2018

Machine learning is where a machine (i.e., computer) determines for itself how input data is processed and predicts outcomes when provided with new data. An artificial neural network is a machine learning algorithm based on the concept of a human neuron. The purpose of this review is to explain the fundamental concepts of artificial neural networks.

• Psychiatry investigation
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• 제15권12호
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• pp.1144-1153
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• 2018

Objective One of the areas of social cognition is Theory of Mind (ToM) is defined as the capacity to interpret, infer and explain mental states underlying the behavior of others. When social cognition studies on neurodevelopmental disorders are examined, it can be seen that this skill has not been studied sufficiently in children with Specific Learning Disorder (SLD). Methods In this study, social cognition skills in children diagnosed with attention deficit hyperactivity disorder (ADHD), SLD or Autism Spectrum Disorder (ASD) evaluated before puberty and compared with controls. To evaluate the ToM skills, the first and second-order false belief tasks, the Hinting Task, the Faux Pas Test and the Reading the Mind in the Eyes Task were used. Results We found that children with neurodevelopmental disorders as ADHD, ASD, and SLD had ToM deficits independent of intelligence and language development. There was a significant correlation between social cognition deficits and problems experienced in many areas such as social communication and interaction, attention, behavior, and learning. Conclusion Social cognition is an important area of impairment in SLD and there is a strong relationship between clinical symptoms and impaired functionality.

• 음성과학
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• 제10권4호
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• pp.63-71
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• 2003

This study aims to experiment on learning French intonation, based on the visualization of melody, which was employed in the early sixties to reeducate those with communication disorders. The visualization of melody in this paper, however, was used to the foreign language learning and produced successful results in many ways, especially in learning foreign intonation. In this paper, we used the PitchWorks to visualize some French intonation samples and experiment on learning intonation based on the bitmap picture projected on a screen. The students could see the melody curve while listening to the sentences. We could observe great achievement on the part of the students in learning intonations, as verified by the result of this experiment. The students were much more motivated in learning and showed greater improvement in recognizing intonation contour than just learning by hearing. But lack of animation in the bitmap file could make the experiment nothing but a boring pattern practices. It would be better if we can use a sound analyser, as like for instance a PitchWorks, which is designed to analyse the pitch, since the students can actually see their own fluctuating intonation visualized on the screen.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• 제12권1호
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• pp.103-114
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• 2001

뚜렛씨 장애는 근육틱과 음성틱이 만성적으로 지속되는 질환이다. 만성 틱장애는 근육틱 혹은 음성틱중 하나만 지속적으로 나타나는 질환이다. 본 연구에서는 1998년 4월 1일부터 1999년 4월 1일까지 서울대학교병원 소아정신과 병동에 입원한 만성 틱 장애 아동과 뚜렛씨 장애 아동의 임상적 특징을 조사하고 두 질환 사이의 관계를 비교하고자 시행되었다. 이들의 특성을 확인하기 위해 대조군으로 학습장애 환자를 선정하였다. 조사 결과는 다음과 같다. 첫째, 만성 틱장애(n=13)와 뚜렛씨 장애 환자(n=29)의 평균 발병연령은 각각 $7.3{\pm}2.5$, $7.2{\pm}2.2$세, 입원시 연령은 평균 $11.7{\pm}2.7$, $11.5{\pm}2.6$세, 입원기간은 $5.7{\pm}5.4$, $11.0{\pm}8.7$주였고 두 군 사이에 의미 있는 차이는 없었다. 학습장애의 경우 발병연령($4.2{\pm}1.9$세)이 두 장애보다 빠르고 의료기관을 찾는 시기($9.8{\pm}3.2$세)도 빨랐다. 출생 계절은 틱장애 환자들에서 6월에서 9월 사이가 가장 적었지만 의미 있는 차이는 없었다. 남녀의 성비율은 각각 10：3, 26：3, 11：5였고 의미 있는 차이는 보이지 않았다. 환자가 출생할 때의 아버지와 어머니 연령은 세 군 모두 차이가 없었다. 둘째, 정신과적 가족력이 있는 경우도 세 군 사이에 차이가 없었고 각각 24.1%, 46.2%, 56.3%였다. 발병전 유발 요인이 확인된 경우는 만성틱장애와 뚜렛씨 장애에서 11.1%와 35.7%로서 의미 있는 차이를 보이지는 않았지만 학습장애(56.3%)에 비해서는 적었다. 셋째, 만성 틱장애와 뚜렛씨 장애, 그리고 학습장애 환자의 지능지수는 각각 언어성 지능 $92.3{\pm}10.7$, $94.7{\pm}14.9$, $94.3{\pm}13.8$이었고, 동작성 지능은 $93.0{\pm}20.5$, $97.5{\pm}13.0$, $95.0{\pm}16.9$이었으며, 전체 지능은 $91.9{\pm}20.1$, $95.8{\pm}14.5$, $93.9{\pm}15.1$로서 세 군 사이에 의미 있는 차이는 없었다. 기질적 뇌장애 소견은 CT/MRI 등에서 0%, 27.3%, 6.3%, 뇌파 이상은 8.3%, 17.2%, 12.5%에서 나타났고 차이는 발견하지 못하였다. 넷째, 항도파민 약물에 대한 반응은 만성 틱장애와 뚜렛씨 장애 환자에서 각각 84.6%, 77.0%가 부분관해를 보였고 완전 관해된 경우는 한 명도 없었으며 두 군 사이에 차이가 없었다. 다섯째, 공동 유병현황을 조사한 결과 주의력결핍·과잉운동장애가 학습장애에서 의미 있게 많은 것을 제외하고는 세 군 사이에 통계적으로 의미 있는 차이를 보이지 않았다. 조사 결과 입원한 환자의 경우 만성 틱장애와 뚜렛씨 장애를 가진 환자들은 임상적으로 학습장애를 가진 환자와 많은 부분에서 차이를 보였으나 만성 틱장애와 뚜렛씨 장애를 구분해야 하는 근거를 찾지 못하였다.

• Journal of Ginseng Research
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• 제31권3호
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• pp.117-126
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• 2007

Ginseng, the root of Panax species, a well-known herbal medicine has been used as a traditional medicine for thousands of years and is now a popular and worldwide used natural medicine. Ginseng has been used primarily as a tonic to invigorate weak bodies to help the restoration of homeostasis in a wide range of pathological conditions such as cardiovascular diseases, cancer, immune deficiency and hepatotoxicity. Although conclusive clinical data in humans is still missing, recent research results have suggested that some of the active ingredients ginseng exert beneficial effects on central nervous system (CNS) disorders and neurodegenerative diseases, suggesting it could be used in treatment of psychotic disorders. Data from neural cell cultures and animal studies contribute to the understanding of these mechanisms that involve inhibitory effects on stress-induced corticosterone level increasing and modulating of neurontransmitters, reducing $Ca^{2+}$ over-influx, scavenging of free radicals and counteracting excitotoxicity. In this review, we focused on recently reported medicinal effects of ginseng and summarized the possibility of its applications on psychotic disorders.