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.
- MeSH
- Algorithms MeSH
- Expert Systems * MeSH
- Fuzzy Logic MeSH
- Humans MeSH
- Neural Networks, Computer MeSH
- Neurological Rehabilitation * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
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.
- MeSH
- Aggression MeSH
- Safety MeSH
- Accidents, Traffic * MeSH
- Adult MeSH
- Fuzzy Logic MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Attitude MeSH
- Surveys and Questionnaires MeSH
- Models, Psychological * MeSH
- Regression Analysis MeSH
- Automobile Driving psychology MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article 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.
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
- Biodiversity MeSH
- Volunteers MeSH
- Ecosystem MeSH
- Expert Systems MeSH
- Fuzzy Logic MeSH
- Mobile Applications * MeSH
- Altitude MeSH
- Seasons MeSH
- Odonata * anatomy & histology classification MeSH
- Animals MeSH
- Check Tag
- Male MeSH
- Female MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Czech Republic MeSH
Důležitým faktorem sportovního výkonu v tenisu je optimální kondiční úroveň sportovce. Diagnostika její úrovně je v praxi často realizována pomocí motorických testů či testových baterií, zjištěné výsledky jsou důležitým východiskem pro kontrolu, regulaci a plánování tréninkového procesu. K hodnocení výsledků testů jsou nejčastěji využívány testové normy sestavené na principu klasického pravděpodobnostního (diskrétního) přístupu. V posledním období se také v některých sportovních výzkumech objevují snahy o využití tzv. fuzzy přístupu, založeného na teorii fuzzy logiky, kterou vytvořil L. A. Zadeh. Cílem studie je prezentace principů vyhodnocení testových výsledků pomocí fuzzy přístupu a komparace s výsledky získaných pomocí klasického diskrétního přístupu. Prezentace obou přístupů hodnocení je dokumentována na výsledcích testování souboru českých tenistů ve věku 13-14 let (n=211, výška 170±8,9 cm, hmotnost 57,2±9,2 kg), kteří se zúčastnili pravidelného testování Českého tenisového svazu v letech 2000-2015 pomocí testové baterie TENDIAG1. Pro ukázku analýzy dat pomocí fuzzy přístupu byl využit software FuzzME. Míra shody hodnocení výsledků testů fuzzy a pravděpodobnostním přístupem byla věcně i statisticky významná (r=0,94). Posouzení věcné významnosti diferencí středních hodnot výsledků získaných oběma přístupy pomocí Cohenova d prokázalo malý, věcně nevýznamný, rozdíl (d=0,36). Přesto je zřejmé, že fuzzy hodnocení poskytuje možnost významné diferenciace dílčích výsledků jednotlivých tenistů. Zejména u výsledků hráčů, pohybujících se na hranicích hodnotících kategorií, umožňuje fuzzy přístup jemnější a přesnější rozlišení úrovně kondičních předpokladů.
An important factor in sports performance in tennis is the optimal fitness level of the athlete. Diagnosis of its level is often done in practice by motor tests or tested batteries, the results found are an important starting point for control, regulation and planning of training. To test the test results, test standards based on classical probability (discrete) approach are most frequently used. Recently, some sporting research has also made attempts to use a so-called fuzzy approach, based on the theory of fuzzy logic created by L. A. Zadeh. The aim of the study is to present the principles of evaluation of test re-sults using fuzzy approaches and to compare the results obtained using a classical discrete approach. Presentation of the two approaches of the evaluation is documented on the results of testing of sets of Czech tennis players aged 13–14 (n = 211, height 170 ± 8.9 cm, weight 57.2 ± 9.2 kg) who participated in regular testing of Czech tennis from 2000 to 2015 using the TENDIAG1 test battery. FuzzME software was used to demonstrate data analysis using fuzzy access. The degree of fuzzy and probability access was both materially and statistically significant (r = 0.94). The assessment of the factual significance of differences in the mean values of the results obtained by both approaches using Cohen’s d showed a small, factually insignificant difference (d = 0.36). However, it is clear that fuzzy evaluation provides a significant differentiation of individual players’ partial results. Especially in the results of players moving on the boundaries of rating categories, fuzzy access allows a more gentle and more precise resolution of the level of the conditions.
- MeSH
- Diagnostic Techniques and Procedures MeSH
- Fuzzy Logic MeSH
- Kinesiology, Applied MeSH
- Humans MeSH
- Sports MeSH
- Tennis MeSH
- Check Tag
- Humans MeSH
- Publication type
- Evaluation Study 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.
- MeSH
- Algorithms MeSH
- Fuzzy Logic * MeSH
- Data Compression MeSH
- Humans MeSH
- Mammography methods MeSH
- Pattern Recognition, Automated methods MeSH
- Decision Support Systems, Clinical organization & administration MeSH
- Information Storage and Retrieval methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
We present many solutions to predict 1-year the post-operative survival expectancy in thoracic lung cancer surgery base on artificial intelligence. We implement multi-layer architecture of SUB- Adaptive neuro fuzzy inference system (MLA-ANFIS) approach with various combinations of multiple input features, neural networks, regression and ELM (extreme learning machine) based on the used thoracic surgery data set with sixteen input features. Our results contribute to the ELM (wave kernel) based on 16 features is more accurate than different proposed methods for predict the post-operative survival expectancy in thoracic lung cancer surgery purpose.
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.
- MeSH
- Fuzzy Logic * MeSH
- Humans MeSH
- Linear Models * MeSH
- Recycling * statistics & numerical data MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
First published 131 stran : ilustrace, 1 mapa ; 30 cm
- MeSH
- Travel MeSH
- Transportation MeSH
- Environmental Policy MeSH
- Environmental Health MeSH
- Fuzzy Logic MeSH
- Marketing MeSH
- Regional Health Planning MeSH
- Recreation MeSH
- Restaurants MeSH
- Social Capital MeSH
- Case-Control Studies MeSH
- Conservation of Natural Resources MeSH
- Medical Tourism MeSH
- Publication type
- Collected Work MeSH
- Geographicals
- Czech Republic MeSH
- Austria MeSH
- Slovenia MeSH
- Conspectus
- Hospodářská a výrobní odvětví
- NML Fields
- politologie, politika, zdravotní politika
- zájmy a záliby
Complex decision making tasks of different natures, e.g. economics, safety engineering, ecology and biology, are based on vague, sparse, partially inconsistent and subjective knowledge. Moreover, decision making economists / engineers are usually not willing to invest too much time into study of complex formal theories. They require such decisions which can be (re)checked by human like common sense reasoning. One important problem related to realistic decision making tasks are incomplete data sets required by the chosen decision making algorithm. This paper presents a relatively simple algorithm how some missing III (input information items) can be generated using mainly decision tree topologies and integrated into incomplete data sets. The algorithm is based on an easy to understand heuristics, e.g. a longer decision tree sub-path is less probable. This heuristic can solve decision problems under total ignorance, i.e. the decision tree topology is the only information available. But in a practice, isolated information items e.g. some vaguely known probabilities (e.g. fuzzy probabilities) are usually available. It means that a realistic problem is analysed under partial ignorance. The proposed algorithm reconciles topology related heuristics and additional fuzzy sets using fuzzy linear programming. The case study, represented by a tree with six lotteries and one fuzzy probability, is presented in details.
- MeSH
- Algorithms MeSH
- Fuzzy Logic * MeSH
- Heuristics physiology MeSH
- Humans MeSH
- Decision Support Techniques MeSH
- Probability MeSH
- Decision Trees * MeSH
- Decision Making physiology MeSH
- Models, Theoretical MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
