GENERALIZED FUZZY DIVERGENCE MEASURE, PATTERN RECOGNITION AND INEQUALITIES

Many fuzzy information and divergence measures developed by various researchers and authors. Here, authors proposed new fuzzy divergence measure using the properties of convex function and fuzzy concept. The applications of novel fuzzy divergence measures in pattern recognition with case study, are discussed. Obtained various novel fuzzy information inequalities on fuzzy divergence measures. The new relations among new and existing fuzzy divergence measure by new f-divergence, Jensen inequalities, properties of convex functions and inequalities have studied. Finally, verified these results and proposed fuzzy divergence measures by numerical example.

COVID-19 Vaccinations: Summary Guidance for Cancer Patients in 28 Languages: Breaking Barriers to Cancer Patient Information

Background: Covid-19 vaccination has started in the majority of the countries at the global level. Cancer patients are at high risk for infection, serious illness, and death from COVID-19 and need vaccination guidance and support. Guidance availability in the English language only is a major limit for recommendations’ delivery and their application in the world’s population and generates information inequalities across the different populations. Methods: Most of the available COVID-19 vaccination guidance for cancer patients was screened and scrutinized by the European Cancer Patients Coalition (ECPC) and an international oncology panel of 52 physicians from 33 countries.Results: A summary guidance was developed and provided in 28 languages in order to reach more than 70 percent of the global population. Conclusion: Language barrier and e-guidance availability in the native language are the most important barriers when communicating with patients. E-guidance availability in various native languages should be considered a major priority by international medical and health organizations that are communicating with patients at the global level.

Bag Query Containment and Information Theory

The query containment problem is a fundamental algorithmic problem in data management. While this problem is well understood under set semantics, it is by far less understood under bag semantics. In particular, it is a long-standing open question whether or not the conjunctive query containment problem under bag semantics is decidable. We unveil tight connections between information theory and the conjunctive query containment under bag semantics. These connections are established using information inequalities, which are considered to be the laws of information theory. Our first main result asserts that deciding the validity of a generalization of information inequalities is many-one equivalent to the restricted case of conjunctive query containment in which the containing query is acyclic; thus, either both these problems are decidable or both are undecidable. Our second main result identifies a new decidable case of the conjunctive query containment problem under bag semantics. Specifically, we give an exponential-time algorithm for conjunctive query containment under bag semantics, provided the containing query is chordal and admits a simple junction tree.