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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.

In this paper, we present an efficient speaker identification system based on generalized gamma distribution. This system comprises of three basic operations, namely speech features classification and metrics for evaluation. The features extracted using MFCC are passed to shifted delta cepstral coefficients (SDC) and then applied to linear predictive coefficients (LPC) to have effective recognition. To demonstrate our method, a database is generated with 200 speakers for training and around 50 speech samples for testing.Above 90% accuracy reported.

Wound healing is a slow process in diabetic patients due to levels of insulin variations in the body. Thus, we present an automated system to analyze and assess different stages of wound healing process of diabetic patients. The diabetic wound healing stages have been defined into three types, such as the level of tissues present in the wound: The percentage of granulations tissues, Necrotic tissues and Slough tissues present in the diabetic patients. The performance of the proposed method shall be assessed based on the clear accuracy of segmentation of wound region present in the patient body. The Decision Tree-based Segmentation method has yielded a good segmentation accuracy of 98.32% in comparison with the ground truth results of clinical data. Further, the assessment of wound healing stages of proposed method has given a good accuracy of measuring the stages of diabetic patients by measuring the percentage of different types of tissues present in the wound region. Based on the results of classification accuracy of the proposed method, we assess whether the wound is going to heal quickly or not. Thus, we presented an algorithm of Decision Trees for the purpose of segmentation and assessment of wound healing process of diabetic patients.