Cost-Conscious Comparison of Supervised Learning Algorithms over Multiple Data Sets

We propose Multi2Test for ordering multiple learning algorithms on multiple data sets from “best ” to “worst. ” Our goodness measure uses a prior cost term additional to generalization error. Our simulations show that Multi2Test generates orderings using pairwise tests on error and different types of cost. 2 1

A model for predicting drying time period of wool yarn bobbins using computational intelligence techniques

In this study, a predictive model has been developed using computational intelligence techniques for the prediction of drying time in the wool yarn bobbin drying process. The bobbin drying process is influenced by various drying parameters, 19 of which were used as input variables in the dataset. These parameters affect the drying time of yarn bobbins, which is considered as the target variable. The dataset, which consists of these input and target variables, was collected from an experimental yarn bobbin drying system. Firstly, the most effective input variables on the target variable, named as the best feature subset of the dataset, were investigated by using a filter-based feature selection method. As a result, the most important five parameters were obtained as the best feature subset. Afterwards, the most successful method that can predict the drying time of wool yarn bobbins with the highest accuracy was explored amongst the 16 computational intelligence methods for the best feature subset. Finally, the best performance has been found by the REP tree method, which achieved minimum error and time taken to build the model.TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [108M274]This work was supported by TUBITAK (grant number 108M274)

A review on fuel cell electric vehicle powertrain modeling and simulation

In parallel to the developments in the fuel cell and hydrogen production, storage, and delivery technologies, fuel cell electric vehicles (FCEV) have started to take more attention; and several commercial products and prototypes have been already released. Modeling and simulation are important techniques to choose the design and operating parameters of the powertrain components of these vehicles and thus enhance their performance. This paper discusses, compares, and synthesizes different methods used for modeling vehicle dynamics and main powertrain components (fuel cell system, hydrogen storage unit, battery, transmission, electric motor, energy management system) as well as the drive cycle and life cycle simulations for FCEV. Rule-based and metaheuristic energy management methods for FCEV are discussed. Recent papers on the modeling and simulation of FCEV are categorized according to the powertrain components and methods used, and briefly discussed. The findings of this paper guide the researchers to select the optimum FCEV powertrain component types and their technical properties through modeling and simulation methods.</p

Automatic T1 bladder tumor detection by using wavelet analysis in cystoscopy images

Correct classification of cystoscopy images depends on the interpreter's experience. Bladder cancer is a common lesion that can only be confirmed by biopsying the tissue, therefore, the automatic identification of tumors plays a significant role in early stage diagnosis and its accuracy. To our best knowledge, the use of white light cystoscopy images for bladder tumor diagnosis has not been reported so far. In this paper, a texture analysis based approach is proposed for bladder tumor diagnosis presuming that tumors change in tissue texture. As is well accepted by the scientific community, texture information is more present in the medium to high frequency range which can be selected by using a discrete wavelet transform '(DWT). Tumor enhancement can be improved by using automatic segmentation, since a mixing with normal tissue is avoided under ideal conditions. The segmentation module proposed in this paper takes advantage of the wavelet decomposition tree to discard poor texture information in such a way that both steps of the proposed algorithm segmentation and classification share the same focus on texture. Multilayer perceptron and a support vector machine with a stratified ten-fold cross-validation procedure were used for classification purposes by using the hue-saturation-value '(HSV), red-green-blue, and CIELab color spaces. Performances of 91% in sensitivity and 92.9% in specificity were obtained regarding HSV color by using both preprocessing and classification steps based on the DWT. The proposed method can achieve good performance on identifying bladder tumor frames. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis.This work is supported by FCT under Project No. UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020-Programa Operacional Competitividade e Internacionalizacao (POCI) under Project No. POCI-01-0145-FEDER-006941.info:eu-repo/semantics/publishedVersio

Characterization of the inflammatory‐metabolic

Accumulating evidence points to the existence of an inflammatory-metabolic phenotype of heart failure with a preserved ejection fraction (HFpEF), which is characterized by biomarkers of inflammation, an expanded epicardial adipose tissue mass, microvascular endothelial dysfunction, normal-to-mildly increased left ventricular volumes and systolic blood pressures, and possibly, altered activity of adipocyte-associated inflammatory mediators. A broad range of adipogenic metabolic and systemic inflammatory disorders - e.g. obesity, diabetes and metabolic syndrome as well as rheumatoid arthritis and psoriasis - can cause this phenotype, independent of the presence of large vessel coronary artery disease. Interestingly, when compared with men, women are both at greater risk of and may suffer greater cardiac consequences from these systemic inflammatory and metabolic disorders. Women show disproportionate increases in left ventricular filling pressures following increases in central blood volume and have greater arterial stiffness than men. Additionally, they are particularly predisposed to epicardial and intramyocardial fat expansion and imbalances in adipocyte-associated proinflammatory mediators. The hormonal interrelationships seen in inflammatory-metabolic phenotype may explain why mineralocorticoid receptor antagonists and neprilysin inhibitors may be more effective in women than in men with HFpEF. Recognition of the inflammatory-metabolic phenotype may improve an understanding of the pathogenesis of HFpEF and enhance the ability to design clinical trials of interventions in this heterogeneous syndrome

Glossokinetic potential based tongue-machine interface for 1-D extraction

The tongue is an aesthetically useful organ located in the oral cavity. It can move in complex ways with very little fatigue. Many studies on assistive technologies operated by tongue are called tongue-human computer interface or tongue-machine interface (TMI) for paralyzed individuals. However, many of them are obtrusive systems consisting of hardware such as sensors and magnetic tracer placed in the mouth and on the tongue. Hence these approaches could be annoying, aesthetically unappealing and unhygienic. In this study, we aimed to develop a natural and reliable tongue-machine interface using solely glossokinetic potentials via investigation of the success of machine learning algorithms for 1-D tongue-based control or communication on assistive technologies. Glossokinetic potential responses are generated by touching the buccal walls with the tip of the tongue. In this study, eight male and two female naive healthy subjects, aged 22-34 years, participated. Linear discriminant analysis, support vector machine, and the k-nearest neighbor were used as machine learning algorithms. Then the greatest success rate was achieved an accuracy of 99% for the best participant in support vector machine. This study may serve disabled people to control assistive devices in natural, unobtrusive, speedy and reliable manner. Moreover, it is expected that GKP-based TMI could be alternative control and communication channel for traditional electroencephalography (EEG)-based brain-computer interfaces which have significant inadequacies arisen from the EEG signals