objectives: semantic feature analysis was designed to improve lexical retrieval of aphasic patients via activation of semantic networks of the words. in this approach, the anomic patients are cured with semantic information to assist oral naming. the purpose of this study was to examine the effects of semantic feature analysis treatment on anomia of two nonfluent aphasic patients. methods: a si...

In order to improve classification accuracy and lower future computation data collecting costs, feature selection is the process of choosing most crucial features from a group attributes removing less or redundant ones. To narrow down that need be analyzed, variety procedures have been detailed in published publications. Chi-Square (CS), IG, Relief, GR, Symmetrical Uncertainty (SU), MI are six ...

Software development involves several interrelated factors that influence efforts and productivity. Improving the estimation techniques available to project managers will facilitate more effective time budget control in software development. Effort Estimation or cost/effort can help a company overcome difficulties experienced estimating efforts. This study aims compare Machine Learning method o...

Drainage channels are essential components of englacial and subglacial hydrologic systems. Here we use the M integral, a path-independent integral of the equations of continuum mechanics for a class of media, to unify descriptions of creep closure under a variety of stress states surrounding drainage channels. The advantage of this approach is that the M integral around the hydrologic channels ...

in this paper, a novel neuro-fuzzy based method combined with a feature selection technique is proposed for online dynamic voltage stability status prediction of power system. this technique uses synchronized phasors measured by phasor measurement units (pmus) in a wide-area measurement system. in order to minimize the number of neuro-fuzzy inputs, training time and complication of neuro-fuzzy ...

when the number of training samples is limited, feature reduction plays an important role in classification of hyperspectral images. in this paper, we propose a supervised feature extraction method based on discriminant analysis (da) which uses the first principal component (pc1) to weight the scatter matrices. the proposed method, called da-pc1, copes with the small sample size problem and has...