Fuzzy-based non-bankruptcy trend forecast

Many companies face an economic downturn due to the Covid-19 pandemic outbreak, which makes their future uncertain. The practical aim of the paper is to establish a procedure for an effective prediction of a business tendency to bankrupt in the short-term period. The tool is a three-stage fuzzy model formulated in the theoreticalmethodological part and applied on the real data of an examined company. The model input parameters are objective and subjective measured data between 2008-2020 of a nature affecting the output. The output is an interval of subjectively expected values determining the non-Bankruptcy trend (non-B) of a company. The paper shows advantages of the interval fuzzy approach for bankruptcy prediction and identifies the measure of business safety.

A fuzzy approach for determining the cognitive spatial location of an object in geographical information system

The work is devoted to the problem of interpretation of fuzzy semantics of cognitive descriptions of spatial relations in natural language and their visualization in a geographic information system (GIS). The solution to the problem of determining the fuzzy spatial location of an object based on vague descriptions of the observer in natural language is considered. The task is relevant in critical situations when there is no way to report the exact coordinates of the observed object, except by describing its location relative to the observer itself. Such a situation may be the result of a crime, terrorist act or natural disaster. An observer who finds itself at the scene transmits a text message, which is a description of the location of the object or place (for example, the crime scene, the location of dangerous objects, the crash site). The semantics of the spatial location of the object can be further extracted from the text message. The proposed fuzzy approach is based on the formalization of the observer's phrases, with which it can describe spatial relations, in the form of a set of linguistic variables that determine the direction and distance to the object. Examples of membership functions for linguistic variables are given. The spatial knowledge base is built on the basis of the phrases of observers and their corresponding fuzzy regions. Algorithms for constructing cognitive regions in GIS have been developed. Methods of their superposition to obtain the final fuzzy location of the object are proposed. An example of the implementation of a fuzzy model for identifying cognitive regions based on vague descriptions of several observers, performed using developed Python scripts integrated into ArcGIS 10.5, is considered.

A unified deep neuro-fuzzy approach for COVID-19 twitter sentiment classification

Covid-19 braces serious mental health crisis across the world. Since a vast majority of the population exploit social media platforms such as twitter to exchange information, rapid collecting anf analyzing social media data to understand personal well-being and subsequently adopting adequate measures could avoid severe socio-economic damage. Sentiment analysis on twitter data is very useful to understand and identify the mental health issues. In this research, we proposed a unified deep neuro-fuzzy approach for Covid-19 twitter sentiment classification. Fuzzy logic has been a very powerful tool for twitter data analysis where approximate semantic and syntactic analysis is more relevant because correcting spelling and grammar in tweets are merely obnoxious. We conducted the experiment on three challenging COVID-19 twitter sentiment datasets. Experimental results demonstrate that fuzzy Sugeno integral based ensembled classifers succeed over individual base classifiers.