cisplatin is a common chemotherapeutic agent that used for treatment of many solid cancers. rapid identification of chemotherapy resistance is very important and may lead to effective treatment plan. spectroscopy techniques, such as infrared spectroscopy, which are sensitive to biochemical composition of samples, have shown potentials to discriminate tissues. developing in fourier transform inf...

the agriculture sector has been affected by severe drought in recent years, making development of a drought warning system for agriculture crucial. such a system can be a useful tool for policy makers and investors. this research develops a model for agricultural drought risk assessment using statistical and intelligent methods. kermanshah province, a major rain-fed region of iran, was selected...

Visible/Near-infrared reflectance spectroscopy (Vis/NIRS) was applied to variety discrimination of juicy peach. A total of 75 samples were investigated for Vis/NIRS using a field spectroradiometer. Chemometrics was used to build the relationship between the absorbance spectra and varieties. Principle component analysis (PCA) was executed to reduce numerous wavebands into 8 principle components ...

Abstract We study an inhomogeneous sparse random graph, $${\mathcal G }_N$$ G N , on $$[N]=\{1,\dots ,N\}$$ [ ] = { 1 , ⋯ }...

This paper proposes a universal model that governs the general theory of software engineering and complies with three engineering principles, namely, repeatable, economic, and safety principle. The main idea is to create core components that serve as the basic building blocks to build working software components. Each working software component is made up of core component strings by algorithmi...

This review article describes the principle and clinical applications of spectral analysis. Spectral analysis provides a spectrum of the kinetic components which are involved in the regional uptake and partitioning of tracer from the blood to the tissue. This technique allows the tissue impulse response function to be derived with minimal modeling assumptions. Spectral analysis makes no a prior...

We consider the online version of the well known Principal Component Analysis (PCA) problem. In standard PCA, the input to the problem is a set of ddimensional vectors X = [x1, . . . ,xn] and a target dimension k < d; the output is a set of k-dimensional vectors Y = [y1, . . . ,yn] that minimize the reconstruction error: minΦ ∑ i ‖xi − Φyi‖2. Here, Φ ∈ Rd×k is restricted to being isometric. The...

Extreme Components Analysis (XCA) is a statistical method based on a single eigenvalue decomposition to recover the optimal combination of principal and minor components in the data. Unfortunately, minor components are notoriously sensitive to overfitting when the number of data items is small relative to the number of attributes. We present a Bayesian extension of XCA by introducing a conjugat...

Probabilistic principal component analysis (PPCA) seeks a low dimensional representation of a data set in the presence of independent spherical Gaussian noise. The maximum likelihood solution for the model is an eigenvalue problem on the sample covariance matrix. In this paper we consider the situation where the data variance is already partially explained by other factors, for example sparse c...

With Independent Component Analysis (ICA) the objective is to separate multidimensional data into independent components. A well known problem in ICA is that since both the independent components and the separation matrix have to be estimated, neither the ordering nor the amplitudes of the components can be determined. One suggested method for solving these ambiguities in ICA is to measure the ...