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, organization of travel routes, etc. 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 study 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.

Correct classification remains a challenge for researchers and practitioners developing algorithms. Even a minor enhancement in classification quality, for instance, can significantly boost the effectiveness of detecting conditions or anomalies in safety data. One solution to this challenge involves aggregating classification results. This process can be executed effectively as long as the aggregation function is appropriately chosen. One of the most efficient aggregation operators is the Choquet integral. Furthermore, there exist numerous generalizations and extensions of the Choquet integral in the existing literature. In this study, we conduct a comprehensive analysis and evaluation of a novel approach for deriving an aggregate classification. The aggregation process applied to various classifiers is based on enhancements to the Choquet integral. These novel expressions draw inspiration from Newton-Cotes quadratures and other well-known formulae from numerical analysis. In contrast to previous approaches that exploit the generalization of the Choquet integral, our approach requires the utilization of two or three adjacent values associated with the membership of a specific element in different classes. This enables the use of more efficient enhancements in terms of accuracy measurement. Specifically, the t-norm following the integral symbol can be effectively replaced by mathematical expressions used in executing numerical integration formulae. This leads to more precise results and aligns with the concept of numerical integration. Furthermore, in a series of experiments, we thoroughly assess the performance of the proposed approach in terms of classification accuracy. We analyze the strengths and weaknesses of the new approach and establish the experimental settings that can be applied to similar tasks. In the series of experiments, we have demonstrated that the proposed Quadrature-Inspired Generalized Choquet Integral (QIGCI) can either outperform previous enhancements of the Choquet integral or at least achieve a similar level of accuracy measurement. However, we also highlight scenarios where previous approaches can still be a suitable choice. The number of QIGCI-based aggregation models that outperform others is convincing, indicating that this approach is worthy of consideration.

Artificial intelligence (AI) has become an important tool for recognizing changes in the ocular fundus, but most existing studies are conducted in peacetime clinical environments with advanced diagnostic equipment and stable infrastructure. In contrast, wartime conditions impose severe...

Sustainable transport is the concept of efficient, low-cost transportation that minimizes the harmful impact of vehicles on the environment. One of the main ideas is to control exhaust emissions and promote means of transport that use renewable energy. Although the car is still the most popular, in the city it should not be the basis of our transportation. Examples of sustainable transportation include walking, bicycling, or riding a scooter. An increasing number of cities are opting for bike or scooter sharing systems. There is a need to develop a good management system for city rental vehicle networks to encourage and facilitate the use of sustainable means of transport by residents. In this study, the initial assumptions of a city bike network management system are presented. With the skillful use of fuzzy techniques, a method for predicting the travel speed and the availability of a destination station has been developed. In a series of numerical experiments, the high performance of the proposed approach is confirmed.