Nursing staff that work with patients with frontal lobe dementia (FLD) experience challenges that may lead to physical and psychiatric distress. The aim of this study was to capture the feelings, experiences, and reflections of the health staff regarding interactions with and caring for patients with FLD and to highlight what it means for health staff to care for patients with FLD through their...

OBJECTIVES Metabolically healthy obese (MHO) phenotype describes an obese state with a favorable metabolic profile. However, the prognosis of this subpopulation remains controversial. We aimed to examine whether MHO phenotype is associated with progression of atherosclerotic activity, reflected as the changes in coronary artery calcification (CAC) over time. If so, we sought to determine the ro...

This paper presents a new method for combining Fisher’s Linear Discriminant (FLD) and Multi Layer Perceptron (MLP) for face detection. The input patterns are first clustered into 10 face and 10 non-face clusters using K-means algorithm. Then, the FLD coefficients are calculated to obtain the optimal projection of face and non-face images. For each 19×19 pixel window, only 19 FLD coefficients ar...

Forming Limit Diagrams are useful tools for evaluation of formability in the sheet metals. In this paper the effects of yield criteria on predictions of the right and left-hand sides of forming limit diagrams (FLDs) are investigated. In prediction of FLD, Hosford 1979, “Karafillis-Boyce” (K-B) and BBC2000 anisotropy yield functions have been applied. Whereas the prediction of FLD is based on th...

The conventional principal component analysis (PCA) and Fisher linear discriminant analysis (FLD) are both based on vectors. Rather, in this paper, a novel PCA technique directly based on original image matrices is developed for image feature extraction. Experimental results on ORL face database show that the proposed IMPCA are more powerful and e:cient than conventional PCA and FLD. ? 2002 Pat...

Principal Component Analysis (PCA) has emerged as a more efficient approach for extracting features for many pattern classification problems. It has been the standard approach to reduce the high-dimensional original pattern vector space into low-dimensional feature vector space, that removes some of the noisy directions. PCA is an unsupervised technique which does not include label information ...

This research features a new method for automatic face recognition robust to variations in lighting, facial expression and eyewear. The new algorithm named SKKUfaces (Sungkyunkwan University faces) employs PCA (Principal Component Analysis) and FLD (Fisher’s Linear Discriminant) in series similarly to Fisherfaces. The fundamental difference is that SKKUfaces effectively eliminates, in the reduc...

In this paper an ensemble feature extraction algorithm is proposed based on Adaboost.M2 for multiclass classification problem. The proposed algorithm makes no assumption about the distribution of the data and primarily performs by directly selecting the discriminant features with the minimum training error, which can overcome the main drawbacks of the traditional methods, such as Principle Comp...