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Vascular tissue characterization is of great importance concerning the possibility of an Acute Cardiac Syndrome (ACS). Gray-scale intravascular ultrasound (IVUS) is a powerful tomographic modality providing a thorough visualization of coronary arteries. Among the existing methods, virtual histology (VH) is the most popular and clinically available technique for plaque component analysis. IVUS-VH though suffers from a poor longitudinal resolution due to the electrocardiogram (ECG)-gated frame acquisition. In order to surmount this demerit, a new image-based methodology for plaque assessment is suggested in this paper that differentiates tissue components into four classes: calcium, necrotic core, fibrous and fibro-lipid. The available IVUS frames characterized by VH are used for plaque labeling. In addition, a rich set of five textural feature families are extracted from IVUS images and computed at different scales. To increase the discrimination capabilities between the classes, all features are combined into an integrated feature vector. The classification of plaque types is accomplished using a genetic fuzzy rule-based classification system (GFRBCS) that is able to handle effectively highly-dimensional feature spaces. The incorporated feature selection mechanism along with the rule extraction algorithm allows the creation of compact fuzzy rule bases with enhanced classification accuracy. The high interpretability properties of our classifier assist physicians to gain a deeper insight regarding the impact of features involved in the evaluation of atherosclerotic plaques. An extensive experimental analysis is carried out, where various tests are performed in order to highlight the advantages of the proposed scheme against existing methods of the literature, including other GRBCSs and the support vector machine (SVM) classifier. In particular, the attained results show that the applied classifier exhibits better generalization capabilities than the competing methods, i.e., higher accuracy in characterizing unseen IVUS images. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a recurrent fuzzy-neural filter that performs the task of separation of lung sounds, obtained from patients with pulmonary pathology. The filter is a pipelined Takagi–Sugeno–Kang recurrent fuzzy network, consisting of a number of modules interconnected in a cascaded form. The participating modules are implemented through recurrent fuzzy neural networks with internal dynamics. The structure of the modules is evolved sequentially from input–output data. Extensive experimental results, regarding the lung sound category of crackles, are given, and a performance comparison with a series of other fuzzy and neural filters is conducted, underlining the separation capabilities of the proposed filter. [Copyright &y& Elsevier]

Abstract To meet the growing demands from public and private stakeholders for early yield estimates, a high-resolution (2 km × 2 km) rice yield forecasting system based on the integration of the WARM model and remote sensing (RS) technologies was developed. RS was used to identify rice-cropped area and to derive spatially distributed sowing dates, and for the dynamic assimilation of RS-derived leaf area index (LAI) data within the crop model. The system—tested for the main European rice production districts in Italy, Greece, and Spain—performed satisfactorily; >66% of the inter-annual yield variability was explained in six out of eight combinations of ecotype × district, with a maximum of 89% of the variability explained for the 'Tropical Japonica' cultivars in the Vercelli district (Italy). In seven out of eight cases, the assimilation of RS-derived LAI improved the forecasting capability, with minor differences due to the assimilation technology used (updating or recalibration). In particular, RS data reduced uncertainty by capturing factors that were not properly reproduced by the simulation model (given the uncertainty due to large-area simulations). The system, which is an extension of the one used for rice within the EC-JRC-MARS forecasting system, was used pre-operationally in 2015 and 2016 to provide early yield estimates to private companies and institutional stakeholders within the EU-FP7 ERMES project. Highlights • We present a high-resolution rice forecasting system integrating WARM model and RS • The system extends the MARS one and was tested in Italy, Greece and Spain • Variance explained ranged from 66% to 89% in 6 out of 8 combinations ecotype×district • The assimilation of RS LAI increased the forecasting capability in 7 out of 8 cases [ABSTRACT FROM AUTHOR]