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Plantar lesions induced by biomechanical dysfunction pose a considerable socioeconomic health care challenge, and failure to detect lesions early can have significant effects on patient prognoses. Most of the previous works on plantar lesion identification employed the analysis of biomechanical microenvironment variables like pressure and thermal fields.(More)
Breast masses due to benign disease and malignant tumors related to breast cancer differ in terms of shape, edge-sharpness, and texture characteristics. In this study, we evaluate a set of 22 features including 5 shape factors, 3 edge-sharpness measures, and 14 texture features computed from 111 regions in mammograms, with 46 regions related to malignant(More)
We propose methods to perform a certain nonlinear transformation of features based on a kernel matrix, before the classification step, aiming to improve the discriminating power of the comparatively weak edge-sharpness and texture features of breast masses in mammograms, and seek better incorporation of features representing different radiological(More)
Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be(More)
We propose the strict 2-surface proximal (S2SP) classifier, by seeking two cross proximal planes to fit the distribution of the given samples in a corresponding feature space. The method is applied to screen knee-joint vibration or vibroarthrographic (VAG) signals based on statistical parameters derived from signals and selected by the genetic algorithm. A(More)
Pedobarographic images reflect the dynamic interaction between the plantar foot and supporting surfaces during gait and postural activities. Since intra-foot and inter-subject contact geometry are grossly similar, images may be spatially registered and directly compared. Previously arbitrary subjects have been selected as registration templates, but this(More)
We propose two variations of the support vector data description (SVDD) with negative samples (NSVDD) that learn a closed spherically shaped boundary around a set of samples in the target class by involving different forms of slack vectors, including the two-norm NSVDD and nu-NSVDD. We extend the NSVDDs to solve the multiclass classification problems based(More)
Route planning for fully electric vehicles (FEVs) must take energy efficiency into account due to limited battery capacity and time-consuming recharging. In addition, the planning algorithm should allow for negative energy costs in the road network due to regenerative braking, which is a unique feature of FEVs. In this paper, we propose a framework for(More)
Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be(More)
Performance of support vector machines (SVM) is sensitive to the setting of kernel and regularization parameters. Hence, parameter selection becomes an important challenge that the SVM users need to face. In this paper, it is shown that the multiple parameter tuning for a 2-norm SVM (L<sub>2</sub>-SVM) classifier could be viewed as an identification problem(More)