This paper presents a novel computer-aided diagnosis system for melanoma. The novelty lies in the optimised selection and integration of features derived from textural, borderbased and geometrical properties of the melanoma lesion. The texture features are derived from using wavelet-decomposition, the border features are derived from constructing a boundaryseries model of the lesion border and analysing it in spatial and frequency domains, and the geometry features are derived from shape indexes. The optimised selection of features is achieved by using the Gain-Ratio method, which is shown to be computationally efficient for melanoma diagnosis application. Classification is done through the use of four classifiers; namely, Support Vector Machine, Random Forest, Logistic Model Tree and Hidden Naive Bayes. The proposed diagnostic system is applied on a set of 289 dermoscopy images (114 malignant, 175 benign) partitioned into train, validation and test image sets. The system achieves and accuracy of 91.26% and AUC value of 0.937, when 23 features are used. Other important findings include (i) the clear advantage gained in complementing texture with border and geometry features, compared to using texture information only, and (ii) higher contribution of texture features than border-based features in the optimised feature set.

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A scheme suitable for the detection and identification of faults in power systems is presented. Two notable contributions are made: a remodelling of faulty components of power systems that is applicable to both normal and faulty conditions, and a fault detection scheme for power systems. The faults are modelled as unknown inputs, decoupled from the state and output measurements through coordinate transformations, and then estimated through the use of observer theory. The proposed scheme is applied to a power system consisting of a synchronous generator, an exciter, a turbine and speed-governing system, and a network of lines and loads. The case where faults occur on the transmission network is considered. It is shown that the proposed fault detection procedure allows for the real-time identification of the occurrence of the faults and determines their exact locations. Results of detailed simulation studies involving disturbances and faults occurring in linear and nonlinear models of the power system are presented.