The COVID-19 pandemic triggered a sudden shift toward remote and hybrid work, placing IT technologies at the forefront of organizational practices and revealing a spectrum of emotional responses among employees. While conventional wisdom suggests that high salaries in the IT industry safeguard well-being, this study challenges the notion that “money can't buy happiness” by demonstrating that technostress, social isolation, and “Zoom fatigue” persist regardless of income level. Drawing on a longitudinal dataset collected at three one-year intervals, the research employs fuzzy semantics to translate qualitative survey data into quantitative descriptors, basket analysis to identify consistent behavioral patterns, and a Minimal Spanning Tree-Based Isolation Forest enhanced by Takagi–Sugeno fuzzy rules to detect anomalies. The findings indicate a nuanced interplay of negative and positive emotions, with fear, anxiety, and fatigue frequently coexisting alongside pride, energy, and satisfaction. Some anomalies in responses reveal issues such as random guessing or minimal IT usage, underscoring the importance of filtering out low-quality data. Crucially, the emotional outcomes are influenced not only by pandemic-related disruption but also by psychosocial and organizational factors – such as role complexity, skill requirements, and managerial support – providing a holistic view of how and why IT-based work can foster both strain and fulfillment. These insights hold practical implications for employers, suggesting that comprehensive well-being strategies, rather than monetary incentives alone, are pivotal in promoting a healthier, more resilient IT workforce.

Anomaly detection is one of the most important problems of modern data science due to the threat to the security of information systems as well as their users. This applies in particular to logistic data, which is used to predict costs, times, and organization of travel routes. Data anomalies may endanger the welfare and safety of transport users, goods, handling companies, and consumers. Moreover, they contribute to the overexploitation of the natural environment. Therefore, it is extremely important to find methods that are responsible for their effective detection. The desired approach may be the Choquet integral and its extensions, which in various applications have proven that with their help it is possible to efficiently increase the quality of the classification measured, for example, with the help of the accuracy. Due to the fact that the Choquet integral is resistant to data fluctuations and takes into account the quality (significance) of the information source, it appears to be an effective proposition for the final determination of what data, or more precisely, which records can be considered anomalous. The innovative approach to analyze transport data has not been used before. This article considers four publicly available databases covering different fields of application of transport systems. In a series of comprehensive numerical experiments, the Choquet integral-based approach has proven high efficiency for each of them. Moreover, we made a comparative analysis of the solutions before applying the Choquet integral and the results after its application.

Clinical practice confirms that speech can support the diagnosis of several mental disorders. For example, reduced speech activity, changes in specific voice features, and pause-related measures were found to be sensitive markers of depressive symptoms. Considering the possibility of continuous speech data collection via a smartphone app, voice analysis has great potential for monitoring mental states. Nevertheless, there is still a need to select the most effective validation approaches for solving the task of predicting the mental state. Those validation approaches shall consider that the data collected from sensors and the response variables considered in this BD application problem are subject to various sources of uncertainty. The aim of the study is to perform an experimental evaluation of the accuracy of top-performing crisp and fuzzy methods, such as Naive Bayes Network, SOTA algorithm, Fuzzy Rule, Probabilistic Neural Network, Decision Tree, Gradient Boosted Tree, Random Forest, Tree Ensemble, and an ensemble approach that combines them. Various training and testing scenarios are considered for each of these methods, consisting of a given percentage of all observations. Additionally, the results from multiple methods are aggregated using the dominant function. Thus, the most frequent rating is taken and a metric based on fuzzy numbers is also considered for comparative purposes. The preliminary results of numerical experiments are promising. The sensitive point is the vicinity of the threshold of transition to a disease state. It should be noted that due to minor differences inherent in such cases, it seems intuitive to use fuzzy numbers to determine the patient’s assessment. Experiments confirmed also that the ranking of methods depends on the choice of the training set and evaluation metric.

Anomaly detection in data sets is one of the most important challenges for modern analysts and data administrators. It is usually based on algorithms that use raw data. In this study, we analyze the possibilities of improving the well-known Isolation Forest algorithm based on binary search trees for data preprocessing using the grouping of both attributes first, and then records within attribute groups. Attribute clustering is based on hierarchical grouping, while record grouping uses K-Means and Fuzzy C-Means. To describe the relationships between records, data membership functions are also used, built on the basis of record distances from centroids. This approach gives a new look at the possibilities of the Isolation Forest method and leads to a significant improvement in the results for selected public databases.

Startups are defined in the literature as newly established enterprises that are looking for a chance for further dynamic development. In the Polish economy in recent years, we have witnessed a significant increase in the number of startups, which allows us to use the potential of Polish entrepreneurship for economic development. Startups are a key factor in many innovative economies. Young companies contribute to the dynamic growth of the economy. More boldly than mature entities, they reach for innovative technological solutions, producing digital products and services under conditions of increased risk. Startups can develop based on proven methodologies, have many opportunities to raise capital and use many sources of financing, but not all of them take advantage of the opportunities available on the market.In the proposed innovative approach, we focus on data from entrepreneurs themselves. The results of surveys conducted among a large group of enterprises, including startups supported by startup platforms, allowed for the development of fuzzy classification models. The use of fuzzy sets to determine the degree of development of a startup based on the degrees of membership to individual classifiers allowed for the achievement of a very high efficiency of the model. Moreover, the application of the classic forms of the membership function poses no problems with the correct interpretation of the obtained results by people who are not experts in the use of fuzzy sets. The obtained results indicate the enormous potential of interdisciplinary research and the promotion of the idea of using fuzzy sets in economics and management sciences.

The problem of detecting outliers in data is a widely discussed issue. The sources of outliers vary and can come from system errors, or human mistakes. Due to the constantly increasing number of data for analysis, an effective tool for detecting outliers should be proposed. Therefore, in this study we present a method based on the use of the fuzzy three-sigma rule to detect outliers. The novelty of the described method is the use of the properties of fuzzy sets to replace the properties of the analyzed data with common statistical semantics. Due to the untypical approach consisting in an independent analysis of each dimension of the data set, a universal method was obtained, which operates regardless of the specificity of the analyzed data. Moreover, the appropriate aggregation of the membership degrees to the descriptors describing the type and strength of deviation from the norm makes it possible to look at the analyzed data from various angles. The high performance of the proposed novel approach was confirmed in numerical experiments.

Saaty’s Analytic Hierarchy Process (AHP) is a widely used and often considered tool in decision-making theory. This method allows to determine the classification of a set of multiple responses based on pairwise comparison of two answers. One of the main shortcomings of this method, in its original version, is the need to use a fixed set of numerical or linguistic descriptors. This requirement brings a lot of problems, especially for people who have never encountered this method before.In the proposed approach, the concept of using GUI tools to determine the preferences of respondents is developed. In this way, it allows conducting surveys among people who have no experience with AHP. In contrast to previous methods, in the proposed novel approach all possible combinations of responses are considered. The combinations of respondents’ answers are obtained in the process of projecting the choices indicated by the slider onto the classical descriptor space. The application of such a method fully takes into account the preferences of the respondents. The results of numerical experiments conducted among students confirm that this approach is very intuitive, and does not cause any difficulties for the respondents.

Genetics is a highly relevant field of science. During the time of COVID-19 pandemic, it has gained additional importance. In this paper, a novel approach to genetic research using fuzzy sets is presented. Such a synergy of two so far rarely interacting scientific disciplines opens new avenues of research. The proposed approach shows only a sample of the possibilities offered by interdisciplinary research. In this study, a new approach using fuzzy set-based techniques to analyze the phenomena of homozygosity of microsatellite markers is presented. The analyses carried out using one of the most intuitive types of membership functions allowed us to achieve results that shed new light on the examined data. Moreover, the analysis of the distributions of individual markers using fuzzy sets allowed for a more in-depth study of the problem under consideration.

Anomaly (outlier) detection is one of the most important problems of modern data analysis. The sources of anomalies are varying. They can be the results of database users’ mistakes, operational errors, or just missing values. The problem is very important because of the fast growth of large datasets. Therefore, in this article, we present detailed results of work on the concept of granular computing-based approach to anomaly detection, classification, and gradation. The aim of the study is to introduce an innovative solution that allows the use of information granules to identify and classify anomalies. The novelty of the proposed solution consists in the use of fuzzy semantics implied by the statistical properties of the data considered. Moreover, instead of the classic approach to detecting anomalies in the data, it is proposed to determine the degree of anomaly for the data transformed to the new resulting state space. Thanks to the use of an innovative approach using the universal descriptor space, it is possible to determine the degree of anomaly, and by using various aggregation methods one can also specify its type.

In this study, we propose an approach based on the advanced fuzzy techniques such as Fuzzy C-Means, Fuzzy Robust Gamma Rank Correlation and Fuzzy Cognitive Maps to predict the acceptance attitudes towards Internet health information. To improve the Fuzzy Cognitive Maps efficiency we introduce the setting the values of initial matrix with the use of fuzzy methods and to divide the concepts based on the clustering methods. This allows us to use maps as a tool for prediction the acceptance attitudes of the young people in the area of heath information management. Moreover, this work sheds the light on the novel application of both Fuzzy C-Means and Fuzzy Robust Gamma Rank Correlation as tools for settings the initial values of connections between concepts for Fuzzy Cognitive Maps.

Capturing anomalies in data is one of the most important problems in modern data analysis. In recent years, scientists have developed many interesting approaches. One of the leading is the Isolation Forest method, which is based on searching a forest of binary trees. This method is extremely effective, especially in the case of relatively small databases. Despite of that, a lot of work has been done for years to improve it. For instance, variants based on rotation or fuzzy sets were developed. In this paper, we propose a very effective method of building search trees based on grouping data using the K-Medoids method. The results of the conducted experiments suggest a significant improvement in the quality of the method in relation to the original Isolation Forest.

The selection of an appropriate method of data analysis is a key problem for researchers from various fields of applications. They consider different methods of data classification, often based on the thematic scope of the data at their disposal. However, various data characteristics, such as data set size, data type and quality, gaps, outliers and other anomalies, can make proper selection significantly difficult. Therefore, in this study we propose a method based on a very universal classifier designed on the basis of calculations using information granules. The main objective of the work is to present and comprehensively verify the effectiveness of the classifier. As an example of application, we propose complicated yet currently important data coming from widely understood ecological research. Detailed numerical experiments indicate the high efficiency of the proposed method and the possibility of easy application to data appearing in other fields. In addition, various types of aggregation functions of the classification results are considered in order to obtain the most reliable results for the discussed problems,

With the advent of research into Granular Computing, in particular information granules, the way of thinking about data has changed gradually. Researchers and practitioners do not consider only their specific properties, but also try to look at the data in a more general way, closer to the way people think. This kind of knowledge representation is expressed particularly in approaches based on linguistic modeling or fuzzy techniques such as fuzzy clustering, but also newer approaches related to the explanation of how artificial intelligence works on these data (so-called explainable artificial intelligence). Therefore, especially important from the point of view of the methodology of data research is an attempt to understand their potential as information granules. Such a kind of approach to data presentation and analysis may introduce considerations of a higher, more general level of abstraction, while at the same time reliably describing the network of relationships between the data and the observed information granules. In this study, we tackle this topic with particular emphasis on the problem of choosing a predictive model. In a series of numerical experiments based on both artificially generated data, ecological data on changes in bird arrival dates in the context of climate change, and COVID-19 infections data we demonstrate the effectiveness of the proposed approach built with a novel application of information potential granules.

Classification of objects in empirical data, especially in biological sciences, is a very complex process and has been a big challenge for researchers who do not specialize in data analysis. Therefore, in this study, we present a comprehensive summary of selected classifiers operating on both exact and fuzzy numbers. The results of performance of specific classifiers are compared on the example of a unique set of empirical data on changes in the behavior of animals in response to environmental factors. This is one of the key challenges in ecological research and it is strictly related to ecosystem changes caused by climate change. Nowadays, changes in behavior are a very popular topic of research because as a result of the COVID-19 pandemic and lower activity of people (lockdown effect). Therefore, various unusual reactions of wild animals were found around the world. A detailed compilation of research results, shortcomings, and strengths of various classification methods may be a compendium of knowledge for biologists and other practitioners as well as researchers working with empirical data.

In this study, we propose an approach based on the advanced fuzzy techniques such as Fuzzy C-Means and Fuzzy Cognitive Maps to cluster the birds species, based on the information of first arrival date, into more coherent and uniform groups. The birds are very suitable subject for modelling the climate changes. Very popular indicator to forecast bird migration dynamic is the first arrival date. In many reported studies, this indicator is shown as very useful. However, there is still a lack of precise methods grouping the birds into the classes in satisfying manner producing detailed information about species and the relations between them. As evidenced in the experimental series section, the proposed approach enables the researchers and practitioners working with that important area of ecology to observe subtle dependencies between various bird species. Moreover, this work sheds the light on the novel application of both Fuzzy C-Means and Fuzzy Cognitive Maps as the efficient tools to analyse the ecological data collected in changing climatic environment.

One of the main challenges is the analysis of large data sets, in particular those containing various types of data, such as time, place, image, and those assuming categorical values. This type of data may contain numerous outliers. Despite the continuous development of data analysis, many methods can be effectively improved, in particular through the use of efficient solutions based on fuzzy set technologies. In this paper, we analyze the improvement of a well-known method, i.e. Isolation Forest, for which we introduce an innovative modification, referred to as the Fuzzy Set-Based Isolation Forest.

In this study, we present the results of surveys conducted in a group of employees and students of IT faculties presenting the answers to the most important, in our opinion, issues related to software engineering (SE), IT project management, and programming paradigms. The above topics are chosen because of their high relevance to the professional community. The participants taking part in the experiments quantified their input through the process of pairwise comparisons (a so-called Analytic Hierarchy Process, AHP) using an innovative highly interactive approach based on a graphic communication means. The generic AHP method was augmented by the optimization mechanisms delivered by the Particle Swarm Optimization (PSO) in order to deliver the highest possible consistency of responses of the participants. Moreover, we demonstrate a method based on Fuzzy C-Means (FCM) filtering highly inconsistent and unreal experts' assessments. In a series of experiments, we demonstrate the accuracy and stability of the AHP method based on graphical environment. We discuss two variants of aggregation of experts' opinions according to their level of experience in the field of interest. Finally, we show the efficiency of the FCM as the method of preselection of experts' evaluations.

Anomaly (outlier) detection is one of the most important problems of modern data analysis. Anomalies can be the results of database users' mistakes, operational errors or just missing values. The problem is important because of fast growth of the large data sets. Therefore, we present the initial results of work on a Granular Computing approach to data imputation and missing data analysis. Our proposal brings intuitive and interpretable solutions. Finally, in a series of experiments, we demonstrate its effectiveness for a large dataset in the area of transport.

One of the main challenges faced by people who use data from empirical research in their work is missing data. In many scientific disciplines and industries there are references to time series. The suitability of several methods to imputation of the missing data in the study of mutual links between the analysed time series have been presented and tested in this work. In this paper, known methods of supplementing data in time series were enriched by the use of fuzzy sets and their processing was tested on unique data from experimental research and a transport company database. Fuzzy linguistic descriptors-based methods of missing data imputation in databases containing time series are discussed. The proposed method has a high efficiency, which have been proven in a series of experiments with both artificial and real datasets. The proposed methodologies have been tested on theoretical example and empirical data sets from various fields: (1) ecological data on changes in bird arrival dates in the context of climate change and (2) data describing the transport of containers between ports on the Mediterranean. Moreover, an important novelty of this work is, in particular, an application of fuzzy techniques to the correction of the datasets containing bird migration descriptions.

The task of anomaly detection in data is one of the main challenges in data science because of the wide plethora of applications and despite a spectrum of available methods. Unfortunately, many of anomaly detection schemes are still imperfect i.e., they are not effective enough or act in a non-intuitive way or they are focused on a specific type of data. In this study, the classical method of Isolation Forest is thoroughly analyzed and augmented by bringing an innovative approach. This is k-Means-Based Isolation Forest that allows to build a search tree based on many branches in contrast to the only two considered in the original method. k-Means clustering is used to predict the number of divisions on each decision tree node. As supported through experimental studies, the proposed method works effectively for data coming from various application areas including intermodal transport and geographical, spatio-temporal data. In addition, it enables a user to intuitively determine the anomaly score for an individual record of the analyzed dataset. The advantage of the proposed method is that it is able to fit the data at the step of decision tree building. Moreover, it returns more intuitively appealing anomaly score values.

In this study, we develop a process of estimation of importance of features considered in face recognition by making use of the analytic hierarchy process (AHP). The AHP method of pairwise comparisons realized at three levels of hierarchy becomes crucial to realize a comprehensive weighting of cues so that sound estimates of weights associated with the individual features of faces can be formed. We demonstrate how to carry out an efficient process of face description by using a collection of linguistic descriptors of the features and their groups. Numerical dependencies between the features are quantified with the help of experienced criminology and psychology experts. Finally, we present an entropy-based method of evaluation of the relevance of the estimation process completed by the individuals. The intuitively appealing results of experiments are presented and analyzed in detail.

In this study, we present a new approach to the face retrieval and face classification problem, which exploits available expert's knowledge and introduces a novel way of describing facial features. These features are described by manually assigned weights corresponding to membership grades with respect to the linguistic descriptors such as short, medium, or long. In the series of experiments, we also use weights produced by the Analytic Hierarchy Process aimed at producing saliences of facial cues. We identify a group of the most essential facial features.

In this study, we present a new approach to the problem of face classification, which relies on the linguistic description of the facial features. In this method, face descriptors are represented through the Analytic Hierarchy Process (AHP) and formalized as information granules. Moreover, neural networks are used to construct efficient classifiers. Furthermore, with usage of AHP we realize a transition from the linguistic description of the facial features to the vectors of numbers that are used by a neural network in the process of matching faces. The results of experiments demonstrate the potential applicability of our proposal to the forensic investigations. Finally, discussed are important aspects of constructing neural networks regarded as a vehicle to perform classification process.