This paper presents the design, the development of a new multilingual emotional speech corpus, TaMaR-EmoDB (Tamil Malayalam Ravula-Emotion DataBase) and its evaluation using a deep neural network (DNN)-baseline system. The corpus consists of utterances from three languages, namely, Malayalam, Tamil and Ravula, a tribal language. The database consists of short speech utterances in four emotions-anger, anxiety, happiness, and sadness, along with neutral utterances. The subset of the corpus is first evaluated using a perception test, in order to understand how well the emotional state in emotional speech is identified by humans. Later, machine testing is performed using the fusion of spectral and prosodic features with DNN framework. During the classification phase, the system reports an average precision of 0.78, 0.60, 0.61 and recall of 0.84, 0.61 and 0.53 for Malayalam, Tamil, and Ravula, respectively. This database can potentially be used as a new linguistic resource that will enable future research in speech emotion detection, corpus-based prosody analysis, and speech synthesis.

The growth of abnormal tissue is also called neoplasm which can be differentiated from the surrounding tissues by its structure. This tumour will affect the immune system which is a major leading cause of death around 13% worldwide. Blood diseases such as leukemia replaces the normal blood cells in the bone marrow and the blood. Effective modern drugs can be deployed for the blood diseases such as Chronic Lymphatic Leukemia(CLL). Data mining technique is used to categorize the blood test uniqueness(Hematology) and blood swelling to predict the disease in a early stage. Due to the increase in blood tumour diseases Support Vector Machine(SVM) is proposed for the classification of tumour and hematological data. Fish Swarm algorithm is found more efficient in optimizing the data with high accuracy.

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Researches on applications of mobile devices bring wide variety of uses in healthcare. One such work focus on detection of malignant melanoma using mobile image analysis. Dermoscopy is one of a current use, but need a special expertise for the detection of cancer melanoma. The image taken using smartphone is used for this purpose. It mainly focus on localization of the skin lesion by combining fast skin detection and fusion of two fast segmentation results. This also introduces some set of image features and to capture color variation and border irregularity which are useful for smartphone-captured images. It propose a new feature selection criterion to select a small set of good features used in the final lightweight system. The method introduces a new module for the detection of distorted images such as motion blur and alert users in such situations. The blurred image undergo deblurring to detect the correct result. The result of this application will identify whether the image is malignant melanoma or benign with their intensity value from smartphone captured images used.