This manuscript contains several new spaces as the generalizations of fuzzy triple controlled metric space, fuzzy controlled hexagonal metric space, fuzzy pentagonal controlled metric space and intuitionistic fuzzy double controlled metric space. We prove the Banach fixed point theorem in the context of intuitionistic fuzzy pentagonal controlled metric space, which generalizes the previous ones in the existing literature. Further, we provide several non-trivial examples to support the main results. The capacity of intuitionistic fuzzy pentagonal controlled metric spaces to model hesitation, capture dual information, handle imperfect information, and provide a more nuanced representation of uncertainty makes them important in dynamic market equilibrium. In the context of changing market dynamics, these aspects contribute to a more realistic and flexible modelling approach. We present an application to dynamic market equilibrium and solve a boundary value problem for a satellite web coupling.
- MeSH
- algoritmy MeSH
- fuzzy logika * MeSH
- internet MeSH
- lidé MeSH
- teoretické modely MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
This article focuses on the development of algorithms for a smart neurorehabilitation system, whose core is made up of artificial neural networks. The authors of the article have proposed a completely unique transfer of ACE-R results to the CHC model. This unique approach allows for the saturation of the CHC model domains according to modified ACE-R factor analysis. The outputs of the proposed algorithm thus enable the automatic creation of a personalized and optimized neurorehabilitation plan for individual patients to train their cognitive functions. A set of tasks in 6 levels of difficulty (level 1 to level 6) was designed for each of the nine CHC model domains. For each patient, the results of the ACE-R screening helped deter-mine the specific CHC domains to be rehabilitated, as well as the initial gaming level for rehabilitation in each domain. The proposed artificial neural network algorithm was adapted to real data from 703 patients. Experimental outputs were compared to the outputs of the initially designed fuzzy expert system, which was trained on the same real data, and all outputs from both systems were statistically evaluated against expert conclusions that were available. It is evident from the conducted experimental study that the smart neurorehabilitation system using artificial neural networks achieved significantly better results than the neurorehabilitation system whose core is a fuzzy expert system. Both algorithms are implemented into a comprehensive neurorehabilitation portal (Eddie), which was supported by a research project from the Technology Agency of the Czech Republic.
- Klíčová slova
- ACE-R, CHC, Fuzzy expert system model, Neural network model, Neurocognitive rehabilitation,
- MeSH
- algoritmy MeSH
- expertní systémy * MeSH
- fuzzy logika MeSH
- lidé MeSH
- neuronové sítě MeSH
- neurorehabilitace * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The Internet of Things (IoT) is a universal network to supervise the physical world through sensors installed on different devices. The network can improve many areas, including healthcare because IoT technology has the potential to reduce pressure caused by aging and chronic diseases on healthcare systems. For this reason, researchers attempt to solve the challenges of this technology in healthcare. In this paper, a fuzzy logic-based secure hierarchical routing scheme using the firefly algorithm (FSRF) is presented for IoT-based healthcare systems. FSRF comprises three main frameworks: fuzzy trust framework, firefly algorithm-based clustering framework, and inter-cluster routing framework. A fuzzy logic-based trust framework is responsible for evaluating the trust of IoT devices on the network. This framework identifies and prevents routing attacks like black hole, flooding, wormhole, sinkhole, and selective forwarding. Moreover, FSRF supports a clustering framework based on the firefly algorithm. It presents a fitness function that evaluates the chance of IoT devices to be cluster head nodes. The design of this function is based on trust level, residual energy, hop count, communication radius, and centrality. Also, FSRF involves an on-demand routing framework to decide on reliable and energy-efficient paths that can send the data to the destination faster. Finally, FSRF is compared to the energy-efficient multi-level secure routing protocol (EEMSR) and the enhanced balanced energy-efficient network-integrated super heterogeneous (E-BEENISH) routing method based on network lifetime, energy stored in IoT devices, and packet delivery rate (PDR). These results prove that FSRF improves network longevity by 10.34% and 56.35% and the energy stored in the nodes by 10.79% and 28.51% compared to EEMSR and E-BEENISH, respectively. However, FSRF is weaker than EEMSR in terms of security. Furthermore, PDR in this method has dropped slightly (almost 1.4%) compared to that in EEMSR.
Underwater visible light communication (UVLC) has recently come to light as a viable wireless carrier for signal transmission in risky, uncharted, and delicate aquatic environments like seas. Despite the potential of UVLC as a green, clean, and safe alternative to conventional communication methods, it is challenged by significant signal attenuation and turbulent channel conditions compared to long-distance terrestrial communication. To address linear and nonlinear impairments in UVLC systems, this paper presents an adaptive fuzzy logic deep-learning equalizer (AFL-DLE) for 64 Quadrature Amplitude Modulation-Component minimal Amplitude Phase shift (QAM-CAP)-modulated UVLC systems. The proposed AFL-DLE is dependent on complex-valued neural networks and constellation partitioning schemes and utilizes the Enhanced Chaotic Sparrow Search Optimization Algorithm (ECSSOA) to improve overall system performance. Experimental outcomes demonstrate that the suggested equalizer achieves significant reductions in bit error rate (55%), distortion rate (45%), computational complexity (48%), and computation cost (75%) while maintaining a high transmission rate (99%). This approach enables the development of high-speed UVLC systems capable of processing data online, thereby advancing state-of-the-art underwater communication.
- Klíčová slova
- adaptive fuzzy logic, deep learning, deep-learning equalizer, equalization, sparrow search optimization, underwater visible light communication,
- MeSH
- algoritmy MeSH
- deep learning * MeSH
- fuzzy logika MeSH
- komunikace MeSH
- světlo MeSH
- Publikační typ
- časopisecké články MeSH
Although there are several articles that have carried out a systematic literature review of the analytical hierarchy process (AHP), many of them work with a limited number of analyzed documents. This article presents a computer-aided systematic literature review of articles related to AHP. The objectives are: (i) to identify AHP usage and research impact in different subject areas; (ii) to identify trends in the popularity of the AHP from the first introduction of the method in 1980 to the present; (iii) to identify the most common topics related to AHP and topic development over time. We process 35,430 documents related to AHP, published between 1980 and 2021, retrieved from the Scopus database. We provide detailed statistics about research interest, research impact in particular subject areas over the analyzed time period. We use Latent Dirichlet Allocation (LDA) using Gibbs sampling to perform topic modeling based on the corpus of abstracts. We identify nine topics related to AHP: Ecology & Ecosystems; Multi-criteria decision-making; Production and performance management; Sustainable development; Computer network, optimization and algorithms; Service quality; Fuzzy logic; Systematic evaluation; Risk assessment. We also present the individual topics trends over time and point out the possible future direction of AHP.
This research proposes an assessment and decision support model to use when a driver should be examined about their propensity for traffic accidents, based on an estimation of the driver's psychological traits. The proposed model was tested on a sample of 305 drivers. Each participant completed four psychological tests: the Barratt Impulsiveness Scale (BIS-11), the Aggressive Driving Behaviour Questionnaire (ADBQ), the Manchester Driver Attitude Questionnaire (DAQ) and the Questionnaire for Self-assessment of Driving Ability. In addition, participants completed an extensive demographic and driving survey. Various fuzzy inference systems were tested and each was defined using the well-known Wang-Mendel method for rule-base definition based on empirical data. For this purpose, a programming code was designed and utilized. Based on the obtained results, it was determined which combination of the considered psychological tests provides the best prediction of a driver's propensity for traffic accidents. The best of the considered fuzzy inference systems might be used as a decision support tool in various situations, such as in recruitment procedures for professional drivers. The validity of the proposed fuzzy approach was confirmed as its implementation provided better results than from statistics, in this case multiple regression analysis.
- Klíčová slova
- Traffic accidents, driving behaviour, fuzzy rules based on data, fuzzy systems, multiple regression, road safety,
- MeSH
- agrese MeSH
- bezpečnost MeSH
- dopravní nehody * MeSH
- dospělí MeSH
- fuzzy logika MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- postoj MeSH
- průzkumy a dotazníky MeSH
- psychologické modely * MeSH
- regresní analýza MeSH
- řízení motorových vozidel psychologie MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
The purpose of this paper is to develop a fuzzy model of the risk assessment for environmental start-up projects in the air transport sector at the stage of business expansion. The model developed for the following software will be a useful tool for the risk decision support system of investment funds in financing environmental start-up projects at the stage of market conquest. Developing a quantitative risk assessment for environmental start-up projects for the air transport sector will increase the resilience of making risk decisions about their financing by the investors. In this paper, a set of 21 criteria for assessing the risk of launching environmental start-up projects in the air transport sector were formulated for the first time by presenting inputs in the form of a linguistic risk assessment and the number of credible expert considerations. The fuzzy risk assessment model, based on expert knowledge, uses linguistic variables, reveals the uncertainty of the input data, and displays a risk assessment with linguistic interpretation. The result of the paper is a fuzzy model that is embedded in a generalized algorithm and tested in an example risk assessment of environmental start-up projects in the air transport sector.
- Klíčová slova
- air transport sector, decision support systems, decision-maker (DM), environmental start-up project, expert evaluation, expert systems, financial investments, fuzzy model, risk assessment,
- MeSH
- fuzzy logika * MeSH
- hodnocení rizik * MeSH
- investice MeSH
- letectví * MeSH
- rozhodování MeSH
- software MeSH
- životní prostředí * MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Citizen science and data collected from various volunteers have an interesting potential in aiding the understanding of many biological and ecological processes. We describe a mobile application that allows the public to map and report occurrences of the odonata species (dragonflies and damselflies) found in the Czech Republic. The application also helps in species classification based on observation details such as date, GPS coordinates, and the altitude, biotope, suborder, and colour. Dragonfly Hunter CZ is a free Android application built on the open-source framework NativeScript using the JavaScript programming language which is now fully available on Google Play. The server side is powered by Apache Server with PHP and MariaDB SQL database. A mobile application is a fast and accurate way to obtain data pertaining to the odonata species, which can be used after expert verification for ecological studies and conservation basis like Red Lists and policy instruments. We expect it to be effective in encouraging Citizen Science and in promoting the proactive reporting of odonates. It can also be extended to the reporting and monitoring of other plant and animal species.
- MeSH
- biodiverzita MeSH
- dobrovolní pracovníci MeSH
- ekosystém MeSH
- expertní systémy MeSH
- fuzzy logika MeSH
- mobilní aplikace * MeSH
- nadmořská výška MeSH
- roční období MeSH
- vážky * anatomie a histologie klasifikace MeSH
- zvířata MeSH
- Check Tag
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Geografické názvy
- Česká republika MeSH
The aim of the article is to present a novel method for fuzzy medical image retrieval (FMIR) using vector quantization (VQ) with fuzzy signatures in conjunction with fuzzy S-trees. In past times, a task of similar pictures searching was not based on searching for similar content (e.g. shapes, colour) of the pictures but on the picture name. There exist some methods for the same purpose, but there is still some space for development of more efficient methods. The proposed image retrieval system is used for finding similar images, in our case in the medical area - in mammography, in addition to the creation of the list of similar images - cases. The created list is used for assessing the nature of the finding - whether the medical finding is malignant or benign. The suggested method is compared to the method using Normalized Compression Distance (NCD) instead of fuzzy signatures and fuzzy S-tree. The method with NCD is useful for the creation of the list of similar cases for malignancy assessment, but it is not able to capture the area of interest in the image. The proposed method is going to be added to the complex decision support system to help to determine appropriate healthcare according to the experiences of similar, previous cases.
- Klíčová slova
- Fuzzy S-tree, Image classification, Image comparison, Medical image, NCD, TF-IDF, Vector quantization,
- MeSH
- algoritmy MeSH
- fuzzy logika * MeSH
- komprese dat MeSH
- lidé MeSH
- mamografie metody MeSH
- rozpoznávání automatizované metody MeSH
- systémy pro podporu klinického rozhodování organizace a řízení MeSH
- ukládání a vyhledávání informací metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique.
- Klíčová slova
- Empirical test, Fuzzy logic, Prediction, Recycling behaviour,
- MeSH
- fuzzy logika * MeSH
- lidé MeSH
- lineární modely * MeSH
- recyklace * statistika a číselné údaje MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
