Fuzzy logical conclusions and conclusions in expert systems of medical diagnostics

The main problems in making a correct diagnosis are: subjectivity and insufficient qualifications of the doctor, difficulties in correctly assessing the patient’s complaints, signs and symptoms of the disease observed in the patient, as well as individual manifestations of the symptoms of the disease. In publications on the use of expert systems for medical diagnostics using fuzzy logic, the main attention was paid to the medical features of the problem. In this work, for the first time, general methodological aspects of building such systems, creating databases, representing by fuzzy sets of real numbers, digital scales, linguistic and Boolean data of symptom values are formulated. The types of membership functions that are advisable to use to represent the symptoms of diseases are proposed. In fuzzy-logical conclusions, not only the values of the characteristic functions of the logical terms of individual symptoms, but also complex arithmetic functions of their values are used.

Fuzzy Logical Algebra and Study of the Effectiveness of Medications for COVID-19

A fuzzy logical algebra has diverse applications in various domains such as engineering, economics, environment, medicine, and so on. However, the existing techniques in algebra do not apply to delta-algebra. Therefore, the purpose of this paper was to investigate new types of cubic soft algebras and study their applications, the representation of cubic soft sets with δ-algebras, and new types of cubic soft algebras, such as cubic soft δ-subalgebra based on the parameter λ (λ-CSδ-SA) and cubic soft δ-subalgebra (CSδ-SA) over η. This study explains why the P-union is not really a soft cubic δ-subalgebra of two soft cubic δ-subalgebras. We also reveal that any R/P-cubic soft subsets of (CSδ-SA) is not necessarily (CSδ-SA). Furthermore, we present the required conditions to prove that the R-union of two members is (CSδ-SA) if each one of them is (CSδ-SA). To illustrate our assumptions, the proposed (CSδ-SA) is applied to study the effectiveness of medications for COVID-19 using the python program.

Implement multi-step-ahead forecasting with multi-point association fuzzy logical relationship for time series

In the existing high-order fuzzy logical relationship (FLR) based forecasting model, each FLR is used to describe the association between multiple premise observations and a consequent observation. Therefore, these FLRs concentrate on the one-step-ahead forecasting. In real applications, there exist another kind of association: the association between multiple premise observations and multiple consequent observations. For such association, the existing FLRs can’t express and ignored. To depict it, the high-order multi-point association FLR is raised in this study. The antecedent and consequent of a high-order multi-point association FLR are consisted of multiple observations. Thus, the proposed FLR reflects the influence of multiple premise observations on the multiple consequent observations, and can be applied for multi-step-ahead forecasting with no cumulative errors. On the basis of high-order multi-point association FLR, the high-order multi-point trend association FLR is constructed, it describes the trend association in time series. By using these two new kinds of FLRs, a fuzzy time series based multi-step-ahead forecasting model is established. In this model, the multi-point (trend) association FLRs effective in capturing the associations of time series and improving forecasting accuracy. The benefits of the proposed FLRs and the superior performance of the established forecasting model are demonstrated through the experimental analysis.

PREDIKSI JUMLAH PESERTA BPJS PENERIMA BANTUAN IURAN (PBI) APBN MENGGUNAKAN METODE FUZZY TIME SERIES CHENG

BPJS merupakan salah satu badan Penjaminan Kesehatan yang ada di Indonesia. Jenis BPJS terdiri dari BPJS mandiri, BPJS PPU khusus untuk pekerja diperusahaan, dan BPJS PBI khusus masyarakat yang tidak mampu yang iurannya dibayarkan oleh pemerintah yang diditetapkan dalam APBN. Dari ketiga kategori tersebut jumlah kepesertaan BPJS PBI meningkat dari tahun ke tahunnya. Penelitian ini bertujuan untuk memprediksi jumlah peserta BPJS PBI pada tahun 2019 hingga tahun 2021 dengan menggunakan metode Fuzzy Time series Cheng. Fuzzy Time Series Cheng mempunyai cara yang sedikit berbeda dalam penentuan interval, menggunakan Fuzzy Logical Relationship (FLR) dengan memasukkan semua hubungan dan memberikan bobot berdasarkan pada urutan dan perulangan FLR yang sama. Perhitungan akurasi prediksi pada model ini menggunakan MAPE. Hasil dari penelitian ini diperoleh kenaikan peserta BPJS PBI APBN pada tahun 2019 sampai dengan 2021 sebesar 52.071 peserta dengan hasil MAPE 0,97% dan ketepatan hasil prediksi diperoleh sebesar 99,03%.

FUZZY LOGIC APPARATUS AS A TOOL FOR ASSESSING THE EFFECTIVENESS OF DIGITALIZATION OF A COMPANY

In this article, the author gives an example of using the apparatus of fuzzy logic in assessing the effectiveness of digitalization of an enterprise, specifies indicators that can be used as criteria for building a system for assessing the effectiveness of digitalization, as well as provides an overview of existing developments. Within the framework of the article, the study outlines the directions of modification of classical estimation methods that are possible using the apparatus of fuzzy logic. The paper substantiates the choice of a universal fuzzy logic tool as a mathematical apparatus for forming an assessment: a system of fuzzy logical conclusions – standard five-level [0; 1]-classifiers. The possibility of fuzzy classification of properties, as well as qualimetry based on the aggregation of hierarchies of factors, will make it possible to assess the level of efficiency of enterprise digitalization by the degree of success.

A TIME SERIES FORECASTING MODEL BASED ON LINGUISTIC FORECASTING RULES

The fuzzy time series (FTS) forecasting models have been being studied intensively over the past few years. Most of the researches focus on improving the effectiveness of the FTS forecasting models using time-invariant fuzzy logical relationship groups proposed by Chen et al. In contrast to Chen’s model, a fuzzy set can be repeated in the right-hand side of the fuzzy logical relationship groups of Yu’s model. N. C. Dieu enhanced Yu’s forecasting model by using the time-variant fuzzy logical relationship groups instead of the time-invariant ones. The forecasting models mentioned above partition the historical data into subintervals and assign the fuzzy sets to them by the human expert’s experience. N. D. Hieu et al. proposed a linguistic time series by utilizing the hedge algebras quantification to converse the numerical time series data to the linguistic time series. Similar to the FTS forecasting model, the obtained linguistic time series can define the linguistic, logical relationships which are used to establish the linguistic, logical relationship groups and form a linguistic forecasting model. In this paper, we propose a linguistic time series forecasting model based on the linguistic forecasting rules induced from the linguistic, logical relationships instead of the linguistic, logical relationship groups proposed by N. D. Hieu. The experimental studies using the historical data of the enrollments of University of Alabama observed from 1971 to 1992 and the daily average temperature data observed from June 1996 to September 1996 in Taipei show the outperformance of the proposed forecasting models over the counterpart ones.