Ilievska, Natasha
Preferred name
Ilievska, Natasha
Official Name
Ilievska, Natasha
Main Affiliation
Email
natasa.ilievska@finki.ukim.mk
3 results
Now showing 1 - 3 of 3
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, A METHOD FOR CALCULATING THE PROBABILITY OF RUIN OF AN INSURANCE COMPANY(2019-01-01); ; Miteski, StefanInsurance companies frequently ask their actuaries to do calculations of the probability of the company being ruined. These calculations are based on complex data analysis of the last insurance period, which requires building mathematical models and using the data as an input parameter in these models. For this purpose, an algorithm, which presents a simplified mathematical model and provides approximate results of the outcomes (later used for managerial decisions), was prepared. Modeling of different outcomes based on various different inputs shows that the probability of the company to become ruined is inversely proportional to the written premium. The algorithm developed in this paper is illustrated with tables. The model is presented to the reader in a way that the reader can reproduce the calculations and build a custom data model. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Contribution to the Quasigroup Based Error-Detecting Code(IEEE, 2023-07-19)In the last years we have developed few error-detecting codes based on quasigroups. One of them is the code which is a subject of this paper. The previous analyses of the code shows that the code has very high probability of detecting transmission errors. We have previously identified so-called best class of quasigroups of order 4, with the highest probability of detecting errors when the coding process is performed with a quasigroups of order 4. But, there is one more class of quasigroups of order 4, second-best class, whose quasigroups give approximately equal probability of undetected errors as the quasigroups from the best one. Therefore, we proceeded with the examination of the code when it uses quasigroups from this second-best class of quasigroups of order 4. In this paper we will analytically obtain the second important parameter of every error-detecting code, i.e. the number of errors that the code surely detects when for coding it uses a quasigroup from this second-best class of quasigroups of order 4. At the end we will conclude whether the quasigroups from these top two classes have overall equal ability to detect errors with this code. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Simulating the error-detecting capability of the error-detecting code(IEEE, 2018-05)Using simulations, we analyze an error-detecting code from the aspect of the number of errors that the code surely detects. In order to conclude whether and how the order of the quasigroup used for coding affects the number of errors that the code surely detects, we use quasigroups of different orders for coding. Also, we code input blocks of different lengths in order to conclude whether the number of errors that the code surely detects depends on the length of the input block, i.e., the length of the code word.