한동훈 딸 IEEE 논문

Abstract:

Depression is much more than just tiredness or unpleasantness for a few days. Some individuals believe that depression is a minor ailment rather than a serious medical disease. However, depression is not a weakness that can be “snapped out of” by “getting yourself together.” Depression is a disease which can be recovered by taking proper treatment and support. Depression symptom may be easily detected when a man or woman goes into depression. For the purpose of medication and assistance purpose, prediction of prognosis of the depression is important. In this research paper, five Machine Learning algorithms such as Decision Tree Classifier (DTC), Random Forest Classifier (RFC), Multi-layer Perceptron Classifier (MLP), Support Vector Machine (SVM), and AdaBoost Classifier are used to apply to for prediction of depression prognosis. As a result, it is found that SVM machine learning algorithm performs the best. It has an accuracy rate of 85 percent. Also indicated is the age at which men and women are most likely to become depressed. Support Vector Machine classifiers also have low FP (False Positive) and FN (False Negative) rates. Some visualization is applied to generate a view of depression rate in different types of people. This study also used principal component analysis to Figure out the selective data for analysis algorithms.

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우선 이건 고1이 할 수준이 아니에요.  파이선 ML 패키지나 텐서플로 이해애햐하고, linear algebra 선형대수학 이해해야하고, 미적분 이해해야 하고, 그리고 나서야 decision tree나 random forest, SVM을 이해하고 코드 최적화를 할 수 있어요. 그럴려면 최소 대학 3학년 이상은 되어야 겨우 과제를 할 수준이 되고, 저렇게 IEEE 논문까지 쓸려면 아무리 허접하다해도 석사과정 수준은 되어야해요. 

이건 특권층 스카이캐슬이 안드로메다급이라는 얘기.  남의딸은 일기장까지 압색하면서 자기딸은 소중해서 고소고소고소.  미쳤네.