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Repository of data on epidemic situations on sea vessels

Bodus‐Olkowska I., Garczyńska I., Lisaj A., Mąka M., Dramski M., Chronopoulos A., Gregoric T., Koivisto H., de Melo Rodriguez G., Ziarati R., Filipiak K.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2024

Vol. 18 No. 1

133--137

This article is an introduction to the DESSEV (DEcision Support System regarding the risk of Epidemic threats on a sea-going Vessel) project implemented as part of the Erasmus+ partnership under the leadership of the Maritime University of Szczecin. This project consists of three elements: a data repository, a rule base and a decision support system. Due to the schedule of works in the project, only the first of them was presented. The next ones will be described in subsequent publications at various international conferences. The idea of the project naturally resulted from the COVID-19 pandemic, but other infectious outbreaks may also occur on sea vessels, posing a threat to the ship's crew and passengers (passenger transport). The existing legal regulations, available knowledge or training do not sufficiently address the problem of the epidemic on the state. Therefore, it was decided to introduce a novelty - a decision support system, which is designed to facilitate taking the right steps in the event of an infectious disease on a seagoing vessel.

Klasyfikatory z oceną wiarygodności decyzji i ich zastosowanie do wyboru strategii leczenia

Stawska Z., Jóźwik A., Grabowski M., Filipiak K., Rudowski R., Opolski G.

Automatyka / Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie

2004

T. 8, z. 3

331-338

W artykule została zaproponowana modyfikacja schematu podejmowania decyzji opartego na regule k najbliższych sąsiadów, polegająca na wprowadzeniu gradacji błędu. Stosowane do tej pory metody rozpoznawania obrazów pozwalają na ocenę jakości klasyfikacji jako funkcji prawdopodobieństwa mylnej decyzji. Prawdopodobieństwo to szacowane jest eksperymentalnie jako średni błąd klasyfikacji obiektów o znanej przynależności. Wprowadzenie gradacji błędów umożliwia oszacowanie prawdopodobieństwa mylnej decyzji w zależności od cech klasyfikowanego obiektu. Przedstawione podejście zostało zastosowane do analizy rzeczywistych danych dotyczących problemu rozpoznawania pacjentów z ostrym zespołem wieńcowym.

A modification of the k nearest neighbor rule, which enables the classification confidence, is proposed. The quality of the standard classifiers is measured by the probability of misclassification estimated experimentally by a use of objects with known class membership. The error rate is computed as the percentage of misclassified objects. An error rate gradation enables the evaluation of the misclassification probability as a function of the object feature values. The presented approach was applied to an analysis of a real data related to the patients with acute coronary syndromes.

Distance based classifiers and their use to analysis of data concerned acute coronary syndromes

Jóźwik A., Stawska Z., Grabowski M., Filipiak K., Rudowski R., Opolski G.

Image Processing & Communications

2003

Vol. 9, no 3-4

45-52

The paper presents effectiveness of classifiers based on distance function in application to real problem concerned acute coronary syndromes. The types of decision rules: the standard k-NN rule; its fuzzy version and the multistage decision rule that uses the class overlap idea are considered. In the case of the fuzzy k-NN rule the fuzzyness is applied only for decreasing a misclassification rate. The multistage classifier is taken into account because of its very desired property, which consist in possibility of determination whether a case being classified is difficult or easy for recognition. The more difficult is the case to be classified the more stages are required. This property enables an error rate gradation. In each stage the proposed classifier can make up one of the three following decisions: indicate a class number, reply :"I do not know" or qualifythe object to the next stage. A number of stages depend on the classified object. The analyzed data concern to the two-class decision problem that consist in prediction whether the patient will survive the period of one month or not.