56,630 research outputs found
ANALISIS RESIKO KANKER PAYUDARA (BREAST CANCER) MENGGUNAKAN FUZZY INFERENCE SYSTEM (FIS) MODEL MAMDANI
Breast cancer is a type of malignant cancer, in which cells form in the breast tissue, and is the most common type of cancer - apart from skin cancer - and is ranked second (after lung cancer) the type of cancer that causes death. Every year thousands of people die from cancer due to limited medical resources and the inability of society to use existing information sources effectively. The most efficient way and one of the means of protection against breast cancer is early diagnosis. In this study, a system to analyze the risk of breast cancer was developed using the Mamdani model of Fuzzy Inference System (FIS). By using 6 input variables, the developed Mamdani FIS is able to produce an accuracy of 85% with 20 data used.  Keywords: cancer, breast cancer, fuzzy inference system,,fuzzy logic, Mamdani model
Telling Cause from Effect using MDL-based Local and Global Regression
We consider the fundamental problem of inferring the causal direction between
two univariate numeric random variables and from observational data.
The two-variable case is especially difficult to solve since it is not possible
to use standard conditional independence tests between the variables.
To tackle this problem, we follow an information theoretic approach based on
Kolmogorov complexity and use the Minimum Description Length (MDL) principle to
provide a practical solution. In particular, we propose a compression scheme to
encode local and global functional relations using MDL-based regression. We
infer causes in case it is shorter to describe as a function of
than the inverse direction. In addition, we introduce Slope, an efficient
linear-time algorithm that through thorough empirical evaluation on both
synthetic and real world data we show outperforms the state of the art by a
wide margin.Comment: 10 pages, To appear in ICDM1
Two New Estimators of Entropy for Testing Normality
We present two new estimators for estimating the entropy of absolutely
continuous random variables. Some properties of them are considered,
specifically consistency of the first is proved. The introduced estimators are
compared with the existing entropy estimators. Also, we propose two new tests
for normality based on the introduced entropy estimators and compare their
powers with the powers of other tests for normality. The results show that the
proposed estimators and test statistics perform very well in estimating entropy
and testing normality. A real example is presented and analyzed.Comment: 28 page
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