Aircraft approaching is the most dangerous phase in every complete flight. To solve the pressure of air traffic controllers and the landings delayed problems caused by the huge air traffic flow in Terminal Control Area (TCA), an automatic Air Traffic Control (ATC) instructions system is initially designed in this paper. It applies the fuzzy theory to make instant and appropriate decisions which can be transmitted via Controller-Pilot Datalink Communications (CPDLC). By means of the designed system, the decision-making time can be saved and the human factors can be reduced to avoid the flight accidents and further delays in aircraft approaching.

The number of people suffering from diabetes in Taiwan has been increasing in recent years, according to data from the Health Promotion Administration, the prevalence rate of diabetes in Taiwan has reached 5%. In 2019, there were approximately 1.1 million type 1 diabetes patients under the age of 20 in the world, indicating that diabetes is also threatening the health of children and adolescents. Moreover, the vast majority of about 463 million diabetic patients globally between the ages of 20 and 79 suffer from type 2 diabetes. One can see that diabetes is an important public health problem and one of the four major noncommunicable diseases that leaders of all countries should take priority action to address. Type 2 diabetes causes many complications, including cardiovascular disease, impaired vision, amputation, kidney disease, etc. and increases the cost of social medical care. This study takes data from the Data Database of the Health Promotion Administration as the parent population, fuzzy theory and neural network to build predictive models with Matlab tools. The predictive results can be used as a reference for medical personnel in any diagnosis.

In recent years, diabetes has become one of the most common human diseases in the world, and is even the main cause of high mortality and economic losses, while timely diagnosis and prediction provide patients with appropriate methods for prevention and treatment. By using a logistic regression model, we tried to predict type 2 diabetes. The statistical analysis was conducted with SPSS for descriptive analysis of data, a chi-square test, and logistic regression analysis to predict the risk factor of diabetes. As the result, five main predictive factors were identified: waist circumference, family history, hypertension, cardiovascular disease, and age. The overall prediction rate of the logistic regression model for predicting diabetes was 80%. The research results help prevent the occurrence of diabetes or facilitate early treatment, reduce misdiagnosis and avoid wasting health care resources.

According to the Global Cancer Statistics 2020 published in the official journal of the American Cancer Society (ACS), colorectal cancer ranked 4th in incidence and 2nd in mortality, and the 2018 Cancer Registry Report of Taiwan Health Promotion Administration showed that colorectal cancer ranked 2nd in incidence and 3rd in mortality. With the rapid evolution of the times, the lifestyles of the people have shifted from what they used to be. In addition to uncontrollable factors such as family genetic disorders, diet, and bad habits, life stress may lead to an unhealthy body mass index (BMI), which, together with aging, increases the incidence of colorectal cancer. In this study, the convolutional neural network was used to assess the risk of tumor in the colon by colonoscopy. The endoscopic images of the colon, which were classified into three categories of healthy (normal), benign tumor, and malignant tumor, were adopted as training data. When this method is combined with the patient’s physical data, the risk cancer can be calculated by the fuzzy algorithm. Based on the result of this study, the accuracy of the tumor profile by colonoscopy, that is, 81.6%, is more precise than that of colorectal cancer tumor analysis studies in the recent literature. The proposed method will help physicians in the diagnosis of colorectal cancer and treatment decisions.