Patryk Żywica

• Ph.D., D.Sc.
• Professor (Associate) at Adam Mickiewicz University in Poznań

The main topic of this paper is the notion of relative cardinality for interval-valued fuzzy sets – its definition, properties and computation. First we define relative cardinality for interval-valued fuzzy sets following the concept of uncertainty modelling given by Mendel's Wavy-Slice Representation Theorem. We expand on previous approaches by co...

This paper presents an approach to making accurate and high-quality decisions under incomplete information. Our comprehensive approach includes interval modeling of incomplete data, uncertaintification of classical models and aggregation of incomplete results. We conducted a thorough evaluation of our approach using medical data for ovarian tumor d...

We state a problem concerning how to make an effective and proper decision in the presence of data incompleteness. As an example we consider a medical diagnostic system where the problem of missing data is commonly encountered. We propose and evaluate an approach that makes it possible to reduce the influence of missing data on the final result and...

In this paper we propose a new method for classifying uncertain data, modeled as interval-valued fuzzy sets. We develop the notion of an interval-valued prototype-based fuzzy classifier, with the idea of preserving full information including the uncertainty factor about data during the classification process. To this end, the classifier was based o...

In this paper we present OvaExpert, an intelligent system for ovarian tumor diagnosis. We give an overview of its features and main design assumptions. As a theoretical framework the system uses fuzzy set theory and other soft computing techniques. This makes it possible to handle uncertainty and incompleteness of the data which is an unique featur...

This paper presents an innovative approach to financial market modelling by integrating fuzzy discount factors into the decision-making process, thereby reflecting the complexities of human behaviour. Traditional financial models often fail to account for market dynamics’ psychological factors. The proposed method utilizes fuzzy logic to encapsulat...

In this paper, we present a novel method of communicating knowledge between units in a swarm, applying artificial empathy and imprecise cognition to improve cooperation and adaptation. This research focuses on building a swarming agent’s individual knowledge base, relying on knowledge imprecisely shared by another unit and one’s own experience. The...

This paper introduces a novel artificial intelligence model that integrates artificial empathy into the decision-making processes of collaborative agent systems. The existing models of collaborative behaviors, especially in swarm applications, lack the aspect of empathy, known to improve cooperation in human teams. Emphasizing both cognitive and em...

This paper presents a novel framework for integrating artificial empathy into robot swarms to improve communication and cooperation. The proposed model uses fuzzy state vectors to represent the knowledge and environment of individual agents, accommodating uncertainties in the real world. By utilizing similarity measures, the model compares states,...

This paper introduces an innovative framework for the integration of artificial empathy into robot swarms to enhance communication and cooperation. The proposed model relies on fuzzy state vectors to represent the knowledge and environment of individual agents, accommodating uncertainties in the real world. Utilizing similarity measures, the model...

Europe has unique natural values but also has the highest level of ecosystem fragmentation. Evaluating the effectiveness of protected area management is becoming an increasingly common practice. Our main goal was to assess the dynamics of LULC changes in the buffer zones of 159 national parks in 11 selected European countries on the basis of the CO...

The epistemic uncertainty stems from the lack of knowledge and it can be reduced when the knowledge increases. Such interpretation works well with data represented as a set of possible states and therefore, multivalued similarity measures. Unfortunately, set-valued extensions of similarity measures are not computationally feasible even when the dat...

Criticality is considered as a fundamental category of production planning, maintenance process planning and management. The criticality assessment of machines and devices can be a structured set of activities allowing to identify failures which have the greatest potential impact on the company's business goals. It can be also used to define mainte...

During the last five decades, manufacturing has radically changed not only due to the technology development but also because of the new market and environmental requirements. Nowadays, companies are focused not only on cost-reduction and effectiveness or realized processes but also on reducing the negative impact on natural and social environment....

Objectives — The study’s main aim was to evaluate the relationship between theperformance of predictive models for differential diagnoses of ovarian tumors and levels of diagnostic confidence in subjective assessment (SA) with ultrasound. The second aim was to identify the parameters that differentiate between malignant and benign tumors among tumo...

In response to the growing sustainability concerns, manufacturing companies have to formulate measures to assess sustainable manufacturing performance, aiming at integration of sustainability aspects. Although various models and methods to assess the sustainability of production processes, and point the role of maintenance have been developed in re...

In the paper we describe a computer system that store and process uncertain data in such a way as to be able to obtain information essential to make an effective diagnosis while also indicating the uncertainty level of that diagnosis. We consider the problem of incompleteness and imprecision of medical data and discuss some issues connected with su...

This paper presents an approach to applying stochastic orderings to evaluate classification algorithms for low quality data. It discusses some known stochastic orderings along with practical notes about their application to classifier evaluation. Finally, a new approach based on fuzzy cost function is presented. The new method allows comparing any...

Supplementary material

In this paper we present OvaExpert, an intelligent system for ovarian tumor diagnosis. We give an overview of its features and main design assumptions. As a theoretical framework the system uses fuzzy set theory and other soft computing techniques. This makes it possible to handle uncertainty and incompleteness of the data, which is a unique featur...

In the paper we present OvaExpert - a unique tool for supporting gynecologists in the diagnosis of ovarian tumor, combining classical diagnostic scales with modern methods of machine learning and soft computing. A distinguishing feature of the system is its comprehensiveness, which makes it usable at any stage of a diagnostic process. We gather all...

The main topic of this paper is the notion of relative cardinality for interval-valued fuzzy sets - its definition, properties and computation. First we define relative cardinality for interval-valued fuzzy sets following the concept of uncertainty modelling given by Mendel's Wavy-Slice Representation Theorem. We expand on previous approaches by co...

Objectives: The external, two-center validation of the IOTA ADNEX model for differential diagnosis of adnexal tumors. Methods: A total of 204 patients with adnexal masses (134 benign and 70 malignant) treated at the Division of Gynecologic Surgery, Poznan University of Medical Sciences, Poland (Center I), and 123 patients (89 benign and 34 maligna...

In the paper we present experimental results on the problem of an effective decision making on incomplete data. In order to investigate this problem we examined a variety of interval aggregation methods. Exemplary results are based on a medical diagnosis support system. Our research shows that an application of the aggregation in this problem leads...

This article describes the fundamentals of an intelligent decision support system for the diagnosis of ovarian tumors. The system is designed to support diagnosis by less experienced gynecologists, and to gather data for continuous improvement of the quality of diagnosis. The theoretical basis for the construction of the system is the IF-sets frame...

Objectives The aim of this study was to externally validate the diagnostic performance of the International Ovarian Tumor Analysis logistic regression models (LR1 and LR2, 2005) and other popular prognostic models including the Timmerman logistic regression model (1999), the Alcazar model (2003), the risk of malignancy index (RMI, 1990), and the ri...

In this paper we consider applications of bipolarity in modelling problems encountered in ovarian tumor diagnosis. We focus on imprecision of data obtained by a gynaecologist during examinations. We also present a wide range of predictive diagnostic models and propose a new idea for their improvement.

An efficient and general method for calculating an exact value of relative cardinality of Atanassov's intuitionistic fuzzy sets (IFSs) is still an open problem. In this paper we make a step towards the solution of this problem by proposing an algorithm for computing relative cardinality of IFSs based on algebraic t-norm.