background: municipal solid waste (msw) is the natural result of human activities. msw generation modeling is of prime im­portance in designing and programming municipal solid waste management system. this study tests the short-term pre­diction of waste generation by artificial neural network (ann) and principal component-regression analysis. methods: two forecasting techniques are presented in...

this paper presents a combination of data envelopment analysis (dea) and principal component analysis (pca) to reduce the dimensionality of data set. dea is known as effective tool for assessment and benchmarking. the weak point of dea, it is that the number of efficient dmus relies on the number of variables (inputs and outputs). for solving this, first, we do principal component analysis (pca...

q-mode hierarchical cluster (hca) and principal component analysis (pca) were simultaneously applied to groundwater hydrochemical data from the three times in 2004: june, september, and december, along the ain azel aquifer, algeria, to extract principal factors corresponding to the different sources of variation in the hydrochemistry, with the objective of defining the main controls on the h...

functional magnetic resonance imaging (fmri) is a safe and non-invasive way to assess brain functions by using signal changes associated with brain activity. the technique has become a ubiquitous tool in basic, clinical and cognitive neuroscience. this method can measure little metabolism changes that occur in active part of the brain. we process the fmri data to be able to find the parts of br...

Background Various clinical (disease activity, seropositive RA etc.) and metabolic risk factors (Dkk1 have been associated with erosive rheumatoid arthritis (RA). However, such might be intertwined, multicollinearity reduce our ability to discern the individual contribution score. Principal component analysis (PCA) is statistical technique for reducing dataset’s dimension principal regression (...

In this project you will explore the use of Principle Component Analysis (PCA) and Probabilistic PCA (PPCA). PPCA is closely-related to factor analysis, which is described in chapter 14 of your text. Our application is face recognition, following on the work of Moghadden and Pentland. A minimum version of this project would involve reading chapter 14 and conducting a face recognition experiment...