• 한국정보통신학회논문지
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• 제20권12호
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• pp.2348-2354
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• 2016

최근 농업분야에서 IoT(Internet of Things)기술을 통해 다양한 생체 및 환경 정보를 DB(data base)로 구축할 수 있게 되면서 빅 데이터를 이용한 기계학습 분석이 증가하고 있다. 기계학습 분석을 통해 농업의 생산량과 가축의 질병 등을 예측할 수 있게 되어 농업경영에서 효율적인 의사결정을 돕는다. 본 논문에서는 스마트 돈사의 다양한 환경데이터와 몸무게데이터를 이용하여 환경정보와 일당증체의 연관성 모델을 도출하고 그 정확도를 분석하였다. 이를 위해 기계학습의 M5P tree기법을 적용하였다. 분석을 통해 일당증체량이 풍속에 큰 영향을 받는 것을 확인하였다.

• 한국인공지능학회지
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• 제10권1호
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• pp.9-14
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• 2022

The demand for public bicycles operated by the Seoul Metropolitan Government is increasing every year. The size of the Seoul public bicycle project, which first started with about 5,600 units, increased to 3,7500 units as of September 2021, and the number of members is also increasing every year. However, as the size of the project grows, excessive budget spending and deficit problems are emerging for public bicycle projects, and new bicycles, rental office costs, and bicycle maintenance costs are blamed for the deficit. In this paper, the Azure Machine Learning Studio program and the Boosted Decision Tree Regression technique are used to predict the number of public bicycle rental over environmental factors and time. Predicted results it was confirmed that the demand for public bicycles was high in the season except for winter, and the demand for public bicycles was the highest at 6 p.m. In addition, in this paper compare four additional regression algorithms in addition to the Boosted Decision Tree Regression algorithm to measure algorithm performance. The results showed high accuracy in the order of the First Boosted Decision Tree Regression Algorithm (0.878802), second Decision Forest Regression (0.838232), third Poison Regression (0.62699), and fourth Linear Regression (0.618773). Based on these predictions, it is expected that more public bicycles will be placed at rental stations near public transportation to meet the growing demand for commuting hours and that more bicycles will be placed in rental stations in summer than winter and the life of bicycles can be extended in winter.

• 대한한의진단학회지
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• 제16권2호
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• pp.59-70
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• 2012

Objectives : Pulse diagnosis by Expert is the only way to classify 8 Constitutions so the study to supplement classifying method by the questionnaire has developed and modified and ECM-32 System has designed in 2010. But analyzing with Decision tree had many nodes and 32 important questions omitted while processing the data. So this study was to classify the 8 constitution patients into 2 groups first and analyze its characters in consecutive order. Methods : The participants of this study were 1027 patients who classified into one of the 8 constitutions according to pulse diagnosis and answered 251 questionnaires in 2010. They were divided into sympathetic nerve acceleration constitution and parasympathetic nerve acceleration constitution and analyzed with decision tree. Results : The reponses of the questionnaire were analyzed with 4 methods of 5 scales interval method from 0 to 5, Na, Low(1,2), Medium(3), High(4,5), average value, Y/N dichotomy. Average Value had no significance. 1. From the 5 scale interval method 6 questionnaires with 7 nodes (F5e, B1d, F7f, F2a, F1b, C4L) were significant. The accuracy was 92.5%. 2. From L, M, H method 7 questionnaires with 7 nodes(F5e, B1d, F7f, F1a, B1c, C4L, P3d) were significant. The accuracy was 92.5%. 3. From Y/N dichotomy 9 questionnaires with 9 nodes( F5e, B1d, F7f, F1a, B1c, C4L, B1b, P1i, B2a) were significant. The accuracy was 93.18%. Conclusions : Based on this study, Yes or No dichotomy method was most significant and categorized among the 4 methods. Unlike previous studies which used interval scale method only, Y/N dichotomy method was more statistically significant with the questionnaire to supplement the method of pulse diagnosis. For further study by analyzing decision tree method in consecutive order, the patients can be divided into 8 Constitutions with higher significance with less questionnaires.

• 한방비만학회지
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• 제19권1호
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• pp.31-41
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• 2019

Objectives: The purpose of this study was to investigate the effects of gender, age, body weight, muscle mass, fat mass, body mass index (BMI), metabolism, and compliance with medication on weight loss in obese adults. Methods: We reviewed the medical records of 178 patients who were visited to the Korean Oriental Clinic for 3~6 month and had obesity treatment using Gamitaeumjowee-tang from April 2017 to May 2017. We conducted a paired T-test, correlation coefficient and decision tree to analyze factors influencing obesity treatment. Results: The results of correlation analysis showed that initial weight (kg), initial fat mass (kg), BMI ($kg/m^2$), compliance with medication (%), Original Harris-Benedict Equation, Revised Harris-Benedict Equation and The Mifflin St Jeor Equation was significantly correlated to weight loss (kg) (P<0.001). As a result of constructing the decision tree model, it showed that over 5% weight loss of their initial weight (n=154) was related with initial BMI ($kg/m^2$), compliance with medication (%) and initial muscle mass (kg). In case of over 5 kg weight loss of their initial weight (n=131), it was related with initial BMI ($kg/m^2$), compliance with medication (%) and final BMI ($kg/m^2$). Conclusions: This study suggests that weight loss may be affected by initial factors and that initial factors can be used for obesity treatment.

• Asian Pacific Journal of Cancer Prevention
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• 제13권5호
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• pp.1911-1915
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• 2012

Aim: New technologies for the early detection of pancreatic cancer (PC) are urgently needed. The aim of the present study was to screen for the potential protein biomarkers in serum using proteomic fingerprint technology. Methods: Magnetic beads combined with surface-enhanced laser desorption/ionization (SELDI) TOF MS were used to profile and compare the protein spectra of serum samples from 85 patients with pancreatic cancer, 50 patients with acute-on-chronic pancreatitis and 98 healthy blood donors. Proteomic patterns associated with pancreatic cancer were identified with Biomarker Patterns Software. Results: A total of 37 differential m/z peaks were identified that were related to PC (P < 0.01). A tree model of biomarkers was constructed with the software based on the three biomarkers (7762 Da, 8560 Da, 11654 Da), this showing excellent separation between pancreatic cancer and non-cancer., with a sensitivity of 93.3% and a specificity of 95.6%. Blind test data showed a sensitivity of 88% and a specificity of 91.4%. Conclusions: The results suggested that serum biomarkers for pancreatic cancer can be detected using SELDI-TOF-MS combined with magnetic beads. Application of combined biomarkers may provide a powerful and reliable diagnostic method for pancreatic cancer with a high sensitivity and specificity.