Assessment of a diabetic wound is very much important to determine the healing status. Foot ulcer is most commonly observed problem of diabetic patients. A diabetic wound is observed for approximately 15 per cent of diabetic patients. Diabetic wound is a major concern of diabetes mellitus. The foot ulcer is the very much harm full problem related to diabetes mellitus. Here particle swarm optimization (PSO) based optimization technique is used for segmentation of diabetic wounds and classifying into three types of tissues i.e. granulation, necrotic and slough. After the segmentation the different textural features are extracted through Gray Level Co-occurrence Matrix (GLCM). All these features were then fed to two different classifiers, Naive bayes and Hoeffding tree for classifying the tissue types. The experimental results showed that the classification accuracy, sensitivity, specificity are 90.90%, 100%, 87.5% by Naive bayes, and 81.81%, 100%, 77.7% by Hoeffding tree respectively. Hence the PSO optimization techniques along with Naive bayes classifier could be used for the effective segmentation of diabetic wound images.

Online news is an emerging channel where the internet users can get news. Analyzing huge volume of online news articles is a challenging one, because online news articles are generated and updated time to time. Big data techniques are used to tackle this problem. In order to classify the news articles into different categories, an approach based on Evolving Fuzzy Systems(EFS) was used. It categories news articles based on the changes in the content of the corresponding articles. However, it has the problem in the selection of threshold value. Moreover Gaussian membership function is used in EFS that describes the closeness to the prototype. Sometimes it is ha rd to justify. So in this paper, a Penguins Search Optimization Algorithm(PeSOA) is introduced to optimize the pruning threshold value and a bell shaped fuzzy membership function is introduced to define the closeness to the prototype. The optimized pruning threshold is used in term filtering which prune the generated terms based on their frequencies of occurrence throughout the collection. Then the fuzzy rules are generated by EFS where bell shaped fuzzy membership function is used to define the closeness to the prototype. Based on t he fuzzy rules the online news articles are categorized.

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