Normalized entropy is a strong predictor of patient survival rate for lung and renal cell cancers in CT images. In this work, we present a semi-automatic algorithm to compute the normalized entropy of lung metastasis from renal cell cancer via spatio-textural tumour classification. The proposed algorithm only requires a single click from the user to calculate the normalized entropy for tumours,...

A new algorithm, the binormalized data-reusing least mean squares (LMS) algorithm is presented. The new algorithm has been found to converge faster than other LMS-like algorithms, such as the Normalized LMS algorithm and several data-reusing LMS algorithms, when the input signal is highly correlated. The computational complexity of this new algorithm is only slightly higher than a recently prop...

Many applications of clustering require the use of normalized data, such as text or mass spectra mining. The spherical K-means algorithm [6], an adaptation of the traditional K-means algorithm, is highly useful for data of this kind because it produces normalized cluster centers. The K-medians clustering algorithm is also an important clustering tool because of its wellknown resistance to outli...

Many applications of clustering require the use of normalized data, such as text or mass spectra mining. The spherical k-means algorithm [6], an adaptation of the traditional k-means algorithm, is highly useful for data of this kind because it produces normalized cluster centers. The k-medians clustering algorithm is also an important clustering tool because of its wellknown resistance to outli...

In this paper, an approach is proposed for accurate locating of single phase faults in transmission lines using voltage signals measured at one-end. In this method, harmonic components of the voltage signals are extracted through Discrete Fourier Transform (DFT) and are normalized by a transformation. The proposed fault locator, which is designed based on Random Forests (RF) algorithm, is train...