CLASSIFICATION AND ANALYSIS OF BLOOD PRESSURE CHARACTERISTICS USING FUZZY LOGIC APPROACH

The idea of Fuzzy Expert System (FES) used in this research work is proposed to assist the medical experts to make right diagnosis for patients that are suffering from hypertension. The only sure way to monitor high blood pressure is through regular checkups. Majority of the researchers that have worked in this field only focused on using fuzzy expert system for classification of hypertension data, while few of them dealt with data analysis. This research work further checked for the efficacy of medication on the patients and the exact time the effect began to have impact on the patients using secondary data collected from questionnaire. It was gathered from the sampled respondents that the antihypertensive medication (Dieuretic) has been reliable in the treatment of hypertension.

Fuzzy Expert System for Severity Measurement of COVID-19 Suspect

The pandemic of COVID-19 disease is spread over the world. The symptoms of COVID-19 disease can vary from mild to severe illness. Also, these symptoms are complex and uncertain in nature. The severity score is useful to treat the suspect that highly depends on symptoms. To handle with this problem, the current study makes use of the Fuzzy Expert System which is one in every of the foremost suitable methods in modelling systems with high uncertainty and complexity. In this study, the fuzzy-based expert system is designed to measure the severity of COVID-19 disease in suspect. Keywords: Fuzzy Logic, Expert System, COVID-19 .

FUZZY-LOGICAL EXPERT SYSTEM FOR ASSESSING THE FINANCIAL SECURITY OF ENTERPRISES

The purpose of this study is to build a fuzzy expert system for assessing the financial component of the economic security of telecommunications enterprises. The methodological basis of the research is founded on scientific works of domestic and foreign scientists and leading experts in the field of financial analysis and modeling of economic processes, as well as statistical and financial reporting data that are publicly available. To construct an integral indicator of the financial security of an enterprise, a fuzzy conclusion is used. Three financial indicators are used as input variables. The first indicator X1 is the Current Ratio (CR). The second indicator X2 is Equity Ratio (ER). The third indicator is Return on Assets (ROA). The output variable is defined as an indicator of the financial security of an enterprise Y123 (FS). Both the input variables and the output variable are converted to fuzziness by constructing membership functions. The type and parameters of the affiliation function are justified, and the bell-shaped affiliation function is chosen to describe the uncertainty of values that fall under the normal distribution. The quantity of fuzzy sets at every input is considered as z=3 and the quantity of input variables is considered as ω=3. To achieve completeness of the model, the quantity of logic rules is considered as r=33=9. To calculate a degree of market concentration, Mamdani fuzzy conclusion is applied. Defuzzification is engaged to calculate the value of the output variable Y123(FS) for an indicator that determines the degree of financial security of an enterprise and, as a result, the degree of its economic security. To assess the level of the financial security indicator of an enterprise, a fuzzy expert system is constructed. The fuzzy expert system allows you to use various indicators thanks to the fuzzy logic methodology, which takes into account the fuzziness of input variables and output variables as much as possible. For the three telecommunications companies whose core business is wireline communication, ratios are calculated based on financial reports. Financial coefficients are used to determine the integral indicator of financial security of enterprises. This indicator can be characterized by both numerical values and linguistic terms.