Technology aided platforms provide reliable tools in almost every field these days. These tools being supported by computational power are significant for applications that need sensitive and precise data analysis. One such important application in the medical field is Automatic Lesion Detection System (ALDS) for skin cancer classification. Computer aided diagnosis helps physicians and dermatologists to obtain a "second opinion" for proper analysis and treatment of skin cancer. Precise segmentation of the cancerous mole along with surrounding area is essential for proper analysis and diagnosis. This paper is focused towards the development of improved ALDS framework based on probabilistic approach that initially utilizes active contours and watershed merged mask for segmenting out the mole and later SVM and Neural Classifier are applied for the classification of the segmented mole. After lesion segmentation, the selected features are classified to ascertain that whether the case under consideration is melanoma or non-melanoma. The approach is tested for varying datasets and comparative analysis is performed that reflects the effectiveness of the proposed system.

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Most of the brain's cognitive functions rely on the coordinated interactions of neuronal sources that are distributed within and across specialized brain areas. It is important to quantify these temporal interactions directly from neuroimaging data such as the electroencephalogram (EEG). A variety of measures have been proposed to quantify the neural interactions including linear correlation measures and nonlinear information theoretic measures. An important aspect of neural interactions is the direction of the information flow, i.e., the causal interaction between the different regions. In this paper, we propose using a directed transinformation measure (T measure) to quantify these causal interactions. This measure is a generalization of Granger causality and quantifies both the linear and nonlinear interactions between the signals. The proposed measure is applied to both simulated and real EEG signals and is shown to be sensitive to the dependencies between signals.

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