Induced aggregation operators in decision making with the Dempster-Shafer belief structure

We study the induced aggregation operators. The analysis begins with a revision of some basic concepts such as the induced ordered weighted averaging (IOWA) operator and the induced ordered weighted geometric (IOWG) operator. We then analyze the problem of decision making with Dempster-Shafer theory of evidence. We suggest the use of induced aggregation operators in decision making with Dempster-Shafer theory. We focus on the aggregation step and examine some of its main properties, including the distinction between descending and ascending orders and different families of induced operators. Finally, we present an illustrative example in which the results obtained using different types of aggregation operators can be seen.aggregation operators, dempster-shafer belief structure, uncertainty, iowa operator, decision making

MATHEMATICAL THEORY OF EVIDENCE TO DENGUE FEVER DETECTION

This paper presents Dempster-Shafer Theory for dengue fever detection. Sustainable elimination of dengue fever as a public-health problem is feasible and requires continuous efforts and innovative approaches. In this research, we used Dempster-Shafer theory for detecting dengue fever diseasesand displaying the result of detection process. The Dempster-Shafer theory is a mathematical theory of evidence.Dengue fever diseases have the same symptoms withbabesiosis, lyme, malaria, and west nile. We describe six symptoms as major symptoms which include fever, red urine, skin rash, paralysis, headache, and arthritis. Dempster-Shafer theory to quantify the degree of belief, our approach uses Dempster-Shafer theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result

A unified representation of conditioning rules for convex capacities

This paper proposes a unified representation, called the G-updating rule, which includes three conditioning rules as special cases, the naïve Bayes rule, the Dempster-Shafer rule (Shafer(1976)), and the generalized Bayes' updating rule introduced by Dempster(1967) or Fagin and Halpern(1991). It is shown that the G-updating rule constitutes a three-step conditioning, where one of the three rules is applied in each step.

SISTEM DIAGNOSA PENYAKIT PARU-PARU DENGAN MENGGUNAKAN APLIKASI FORWARD CHAINING

Sistem pakar adalah sistem komputer yang berisi seperangkat aturan untuk memecahkan masalah seperti seorang pakar. Paru-paru merupakan salah satu organ pernapasan yang rentan. Tujuan dari penelitian ini adalah untuk mengimplementasikan metode pohon keputusan dan dempster shafer pada diagnosis penyakit paru dan mengukur akurasi sistem. Gejala dicari dengan menggunakan pohon keputusan forward chaining dan diagnosisnya dihitung menggunakan metode dempster shafer. Metode Dempster Shafer menghitung kemungkinan suatu penyakit paru berdasarkan nilai densitas probabilitas yang dimiliki oleh setiap gejala. Penelitian ini menggunakan 65 data yang diperoleh dari rekam medis Puskesmas Tegowanu Kabupaten Grobogan. Gejala umum dan jenis penyakit digunakan sebagai variabel. Berdasarkan hasil penelitian dapat disimpulkan bahwa hasil diagnosis menggunakan metode dempster shafer memiliki akurasi 83,08%