Skin diseases are common and are mainly caused by virus, bacteria, fungus, or chemical disturbances. Timely analysis and identification are of utmost importance in order to control the further spread of these diseases. Control of these diseases is even more difficult in rural and resource-poor environments due to a lack of expertise in primary health centers. Hence, there is a need for providing self-assisting and innovative measures for the appropriate diagnosis of skin diseases. Use of mobile applications may provide inexpensive, simple, and efficient solutions for early diagnosis and treatment. This paper investigates the application of the Gaussian mixture model (GMM) based on the analysis and classification of skin diseases from their visual images using a Mahalanobis distance measure. The GMM has been preferred over the convolution neural network (CNN) because of limited resources available within the mobile device. Gray-level co-occurrence matrix (GLCM) parameters contrast, correlation, energy, and homogeneity derived from skin images have been used as the input data for the GMM. The analysis of the results showed that the proposed method is able to predict the classification of skin diseases with satisfactory efficiency. It was also observed that different diseases occupy distinct spatial positions in multidimensional space clustered using the Mahalanobis distance measure.

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This letter presents a study on the various features of the cloud computing its models and services. Cloud computing has become the newest concern in IT world to provide various services via internet computing. Thus cloud computing is based on the internet based computing where indispensable collective servers offers software, infrastructure, platform, storage and other resources and presents to customers on a pay-as-you-use foundation. The overall objective of this work is to evaluate the various failures of the cloud data centers. To achieve the objectives a review has been conducted on the various research papers and the various failures has been presented.

This paper presents an evaluation of different image fusion techniques. There are many image fusion techniques which have been developed in a number of applications. Image fusion incorporates the data from several images of one scene to obtain an enlightening image which is more appropriate for human visual perception or additional vision processing. Image quality is closely connected to image focus. Image fusion has become one of the most recent and popular methods in the field of image processing. The discrete cosine transforms (DCT) based methods of image fusion are more suitable for energy consumption and time-saving in real-time systems using DCT based standards of still image.

Analysis of different visual textures present in the given images is one of the important perspectives of human vision for objects segregation and identification. Texture-based features are widely used in medical diagnosis for informal prediction of dermatological diseases. Dermatological diseases are the most universal diseases affecting all the living beings worldwide. Recent advancements in image processing have considerably improved the classification, identification, and treatment of various dermatological diseases. Present paper reports the results of Gray Level Co-occurrence Matrix (GLCM) based texture analysis of skin diseases for parametric variations. The investigations were carried out using three Pyoderma variants (Boil, Carbuncle, and Impetigo Contagiosa) using GLCM. GLCM parameters (Energy, Correlation, Contrast, and Homogeneity) were extracted for each colour component of the images taken for the investigation. Contrast, correlation, energy, and homogeneity represent the coarseness, linear dependency, textural uniformity, and pixel distribution of the texture, respectively. The analysis of the GLCM parameters and their histograms showed that the said textural features are disease dependent. The approach may be used for the identification of dermatological diseases with satisfactory accuracy by employing a suitable machine learning algorithm.