fuzzy inference system
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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
- 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
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.
Objective: The objective of this work is to develop efficient classification systems using intelligent computing techniques for classification of normal and abnormal EEG signals. Methods: In this work, EEG recordings were carried out on volunteers (N=170). The features for classification of clinical EEG signals were extracted using wavelet transform and the feature selection was carried out using Principal Component Analysis. Intelligent techniques like Back Propagation Network (BPN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization Neural network (PSONN) and Radial Basis function Neural network (RBFNN) were trained for diagnosing seizures. Further, the performance of the developed classifiers was compared. Results: Results demonstrate that RBFNN classifies normal and abnormal EEG signals better than the other methods. It appears that the RBFNN is able to detect Generalized Tonic-Clonic Seizure (GTCS) more efficiently than the Complex Partial Seizures (CPS). Positive predictive value was better in PSONN and ANFIS than BPN method. Conclusions: It appears that the combination of Wavelet transform method and PCA derived features along with RBFNN classifier is efficient for automated EEG signal classification.
Background: Breast cancer is one of the leading cancers in woman worldwide both in developed and developing nations as per the records from World Health Organization. Many studies have shown that mammography is very effective tool for the breast cancer diagnosis. Mass segmentation plays an important step for the cancer detection. Objective: The objective of the proposed method is to segment the mass and to classify the mass with high accuracy. Methods: The segmentation includes two main steps. First, a rough initial segmentation through iterative thresholding, and second, an active contour based segmentation. The relevant statistical features are extracted and the classification is done by using Adaptive Neuro Fuzzy Inference System (ANFIS). Results: The proposed mass detection scheme achieves sensitivity of 87.5% and specificity of 100% for a set of twenty two images. The overall segmentation accuracy obtained is 91.30%. Conclusions: This work appears to be of high clinical significance since the mass detection plays an important role in diagnosis of breast cancer.
... Digital Signal Processing -- 173 -- 5.1 Discrete-Time Signal And Systems -- 5.1.1 Analog signal processing ... ... -- Structure of decision-support / expert systems Knowledge representations Inference mechanisms User ... ... Fuzzy Sets And Systems 330 -- 7.5.1 Introduction 330 -- 7.5.2 Fuzzy sets 335 -- 7.5.3 Fuzzy relations ... ... rule base and fuzzy inference engine 350 -- 7.5.7 Fuzzifiers and defuzzifiers 353 -- 7.5.8 The fuzzy ... ... and neuro-fuzzy systems 434 -- References 439 -- 9. ...
xiii, 449 stran : ilustrace, tabulky ; 24 cm
- MeSH
- lékařská informatika MeSH
- Publikační typ
- učebnice MeSH
- Konspekt
- Lékařské vědy. Lékařství
- NLK Obory
- lékařská informatika
In this paper, we present the results of the research concerning extraction of informative gene expression profiles from high-dimensional array of gene expressions considering the state of patients' health using clustering method, ML-based binary classifiers and fuzzy inference system. Applying of the proposed stepwise procedure can allow us to extract the most informative genes taking into account both the subtypes of disease or state of the patient's health for further reconstruction of gene regulatory networks based on the allocated genes and following simulation of the reconstructed models. We used the publicly available gene expressions data as the experimental ones which were obtained using DNA microarray experiments and contained two types of patients' gene expression profiles-the patients with lung cancer tumor and healthy patients. The stepwise procedure of the data processing assumes the following steps-in the beginning, we reduce the number of genes by removing non-informative genes in terms of statistical criteria and Shannon entropy; then, we perform the stepwise hierarchical clustering of gene expression profiles at hierarchical levels from 1 to 10 using the SOTA (Self-Organizing Tree Algorithm) clustering algorithm with correlation distance metric. The quality of the obtained clustering was evaluated using the complex clustering quality criterion which is considered both the gene expression profiles distribution relative to center of the clusters where these gene expression profiles are allocated and the centers of the clusters distribution. The result of this stage execution was a selection of the optimal cluster at each of the hierarchical levels which corresponded to the minimum value of the quality criterion. At the next step, we have implemented a classification procedure of the examined objects using four well known binary classifiers-logistic regression, support-vector machine, decision trees and random forest classifier. The effectiveness of the appropriate technique was evaluated based on the use of ROC (Receiver Operating Characteristic) analysis using criteria, included as the components, the errors of both the first and the second kinds. The final decision concerning the extraction of the most informative subset of gene expression profiles was taken based on the use of the fuzzy inference system, the inputs of which are the results of the appropriate single classifiers operation and the output is the final solution concerning state of the patient's health. To our mind, the implementation of the proposed stepwise procedure of the informative gene expression profiles extraction create the conditions for the increasing effectiveness of the further procedure of gene regulatory networks reconstruction and the following simulation of the reconstructed models considering the subtypes of the disease and/or state of the patient's health.
- Publikační typ
- časopisecké články MeSH
