• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7599-7606
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• 2015

The objective of this study was to examine the correlations between smart phone addictions (SPA) and learning disorder (LD), attention deficit hyperactivity disorder (ADHD), and depression of post secondary level students, who were believed to have decent degree of self-commands. The correlation between the degree of smart phone addiction and learning disorder was 46 (p<0.001) and the correlation between the degree of smart phone addiction and ADHD was 48 (p<0.001). Meanwhile, the correlation between learning disorder and ADHD was 64 (p<0.001). From the multiple comparison of learning disorders, bothe the learning disorder and the ADHD of a group with lower degree of smart phone addiction showed mean differences that were more statistically significant than those of a group with higher degree of smart phone addiction. The depression of a group with lower degree of smart phone addition was also more statistically significant than that of a group with higher degree of smart phone addiction.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.371-372
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• 2020

최근 인공지능의 발달로 인해 AI스피커에 대한 연구가 활발히 이루어지고 있다. 조음장애는 구강 안에서 말소리를 제대로 만들지 못해서 제대로 된 언어를 구사하지 못하는 장애를 말한다. 조음장애인들이 AI스피커를 사용하면 발음을 제대로 인식하지 못하기 때문에 사용의 어려움이 있다. 본 논문에서는 경증 조음장애인들이 AI스피커를 이용할 수 있도록 머신러닝 기반 언어교정기의 개발내용에 관하여 기술한다. 이는 언어로 명령 줄 수 있는 여러 시스템에 활용될 수 있을 것으로 기대한다.

• Journal of Integrative Natural Science
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• v.17 no.1
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• pp.21-30
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• 2024

Several anti-inflammatory small molecules have been found in the process of the inflammatory response, and these small molecules have been used to treat some inflammatory and autoimmune diseases. Numerous tools for predicting anti-inflammatory peptides (AIPs) have emerged in recent years. However, conducting experimental validations in the lab is both resource-intensive and time-consuming. Current therapies for inflammatory and autoimmune disorders often involve nonspecific anti-inflammatory drugs and immunosuppressants, often with potential side effects. AIPs have been used in treating inflammatory illnesses like Alzheimer's disease and can limit the expression of inflammatory promoters. Recent advances in adverse incident predictions (AIPs) have been made, but it is crucial to acknowledge limitations and imperfections in existing methodologies.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.113-123
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• 2020

Obstructive sleep apnea (OSA) among sleep disorders is one of relatively common diseases. Patients can be checked for the disease through sleep polysomnography. However, as far as he diagnosis of OSA using polysomnography (PSG) is concerned, many practical problems such as an increasing number of patients, expensive testing cost, discomfort during examination, and the limited number of people for testing have been pointed out. Accordingly, for the purpose of substituting PSG researchers have been actively conducting studies on OSA diagnosis based on machine learning using bio signals. In this regard, we review a rich body of existing OSA diagnosis studies applying machine learning techniques based on bio-signal data. As a result, this paper presents a novel taxonomy of the reviewed studies and provides their comprehensive comparative analysis results. Also, we reveal various limitations of the studies using the bio signals and suggest several improvements about utilization of the used machine learning methods. Finally, this paper presents future research topics related to the application of machine learning techniques using bio signals.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.67-80
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• 2023

Rice is an important food crop for most of the population in Nowadays, psychologists consider social media an important tool to examine mental disorders. Among these disorders, depression is one of the most common yet least cured disease Since abundant of writers having extensive followers express their feelings on social media and depression is significantly increasing, thus, exploring the literary text shared on social media may provide multidimensional features of depressive behaviors: (1) Background: Several studies observed that depressive data contains certain language styles and self-expressing pronouns, but current study provides the evidence that posts appearing with self-expressing pronouns and depressive language styles contain high emotional temperatures. Therefore, the main objective of this study is to examine the literary cyber writers' posts for discovering the symptomatic signs of depression. For this purpose, our research emphases on extracting the data from writers' public social media pages, blogs, and communities; (3) Results: To examine the emotional temperatures and sentences usage between depressive and not depressive groups, we employed the SentiStrength algorithm as a psycholinguistic method, TF-IDF and N-Gram for ranked phrases extraction, and Latent Dirichlet Allocation for topic modelling of the extracted phrases. The results unearth the strong connection between depression and negative emotional temperatures in writer's posts. Moreover, we used Naïve Bayes, Support Vector Machines, Random Forest, and Decision Tree algorithms to validate the classification of depressive and not depressive in terms of sentences, phrases and topics. The results reveal that comparing with others, Support Vectors Machines algorithm validates the classification while attaining highest 79% f-score; (4) Conclusions: Experimental results show that the proposed system outperformed for detection of depression trends in literary cyber writers using sentiment analysis.

• Steel and Composite Structures
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• v.29 no.6
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.91-94
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• 2023

According to most experts and health workers, a living creature's body heat is little understood and crucial in the identification of disorders. Doctors in ancient medicine used wet mud or slurry clay to heal patients. When either of these progressed throughout the body, the area that dried up first was called the infected part. Today, thermal cameras that generate images with electromagnetic frequencies can be used to accomplish this. Thermography can detect swelling and clot areas that predict cancer without the need for harmful radiation and irritational touch. It has a significant benefit in medical testing because it can be utilized before any observable symptoms appear. In this work, machine learning (ML) is defined as statistical approaches that enable software systems to learn from data without having to be explicitly coded. By taking note of these heat scans of breasts and pinpointing suspected places where a doctor needs to conduct additional investigation, ML can assist in this endeavor. Thermal imaging is a more cost-effective alternative to other approaches that require specialized equipment, allowing machines to deliver a more convenient and effective approach to doctors.

• Journal of Society of Preventive Korean Medicine
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• v.14 no.3
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• pp.51-61
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• 2010

This study aimed to evaluate musculoskeletal workload associated with the work of hair designers, to identify the factors associated with work-related stress, depression and musculoskeletal symptoms in Hair Designers, and to check the painful areas based on patients who complained of musculoskeletal symptoms. The data were collected from 279 hair designers in Daegu metropolitan city from February 1 to August 31 of 2009. A summary of the results was as follows : According to work-related stress in study subjects, the degree of stress load was relatively higher in association with the working demand, the relational conflicts and the organizational culture, but the degree of stress was found to be relatively lower in association with the physical environment, work-related autonomy, an insufficient compensation and an occupational instability. People engaged for beauty business have gotten lots of stress because of the endless needs from customers, the pressure of the learning new skills and the uncomfortable working environment. These are able to cause the musculoskeletal disorder. Under this circumstance, small fries do not have any prevention managements for improving the musculoskeletal diseases and they are not afforded to have regular checkup. When teaching the people related with beauty business, it is necessary to provide accurate information like correct carriage to reduce musculoskeletal disorder stress.

• Journal of the Semiconductor & Display Technology
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• v.21 no.2
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• pp.117-122
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• 2022

This paper proposes a method for evaluating the work of manufacturing workers using MediaPipe as a risk factor for musculoskeletal diseases. Recently, musculoskeletal disorders (MSDs) caused by repeated working attitudes in industrial sites have emerged as one of the biggest problems in the industrial health field while increasing public interest. The Korea Occupational Safety and Health Agency presents tools such as NIOSH Lifting Equations (NIOSH), OWAS (Ovako Working-posture Analysis System), Rapid Upper Limb Assessment (RULA), and Rapid Entertainment Assessment (REBA) as ways to quantitatively calculate the risk of musculoskeletal diseases that can occur due to workers' repeated working attitudes. To compensate for these shortcomings, the system proposed in this study obtains the position of the joint by estimating the posture of the worker using the posture estimation learning model of MediaPipe. The position of the joint is calculated using inverse kinetics to obtain an angle and substitute it into the REBA equation to calculate the load level of the working posture. The calculated result was compared to the expert's image-based REBA evaluation result, and if there was a result with a large error, feedback was conducted with the expert again.

• Journal of Interdisciplinary Genomics
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• v.4 no.2
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• pp.24-30
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• 2022

Klinefelter's syndrome (KS) is a syndrome with extra X chromosome(s), in XY individuals, characterized by gynecomastia, small testes, and infertility. Additional X chromosomes can be present as variable karyotypic forms, including mosaicism (47,XXY/46,XY). The reported prevalence of KS ranges from one in 500 to one in 1,000 live males, but is probably underestimated. The classic phenotype is small, firm testes and infertility resulting from seminiferous tubule dysgenesis and androgen deficiency. The spectrum of KS includes tall stature with relatively long legs and arm span, decreased body hair, learning disabilities, behavioral problems, poor motor skills, and other important medical issues, such as metabolic syndrome, diabetes, autoimmune diseases, cardiovascular disease, certain neoplasia. The increased risk of certain medical problems in KS can be attributed to a direct effect of the extra X chromosome, the combined action of multiple genomic and epigenetic factors, or the hormonal imbalances. Typically, chromosome analysis is not ordered for adult patients with general medical conditions, except for suspected cases of hematologic and lymphoid disorders. Even though it was found during work-up for certain disorders in adult patient, most physicians do not suspect KS or consider its impact. Therefore, understanding the pathophysiology and variable manifestation in KS is necessary, and discussions with multidisciplinary teams will help to diagnose and treat males with KS.