Big data development in biomedical and medical service networks provides a research on medical data benefits, early ailment detection, patient care and network administrations.e-Health applications are particularly important for the patients who are unfit to see a specialist or any health expert. The objective is to encourage clinicians and families to predict disease using Machine Learning (ML) procedures. In addition, diverse regions show important qualities of certain provincial ailments, which may hinder the forecast of disease outbreaks. The objective of this work is to predict the different kinds of diseases using Grey Wolf optimization and auto encoder based Recurrent Neural Network (GWO+RNN). The features are selected using GWO and the diseases are predicted by using RNN method. Initially the GWO algorithm avoids the irrelevant and redundant attributes significantly, after the features are forwarded to the RNN classifier. The experimental result proved that the performance of GWO+RNN algorithm achieved better than existing method like Group Search Optimizer and Fuzzy Min-Max Neural Network (GFMMNN) approach. The GWO-RNN method used the medical UCI database based on various datasets such as Hungarian, Cleveland, PID, mammographic masses, Switzerland and performance was measured with the help of efficient metrics like accuracy, sensitivity and specificity. The proposed GWO+RNN method achieved 16.82% of improved prediction accuracy for Cleveland dataset.

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In digital watermarking, an invisible signal referred as a watermark is embedded into multimedia data for various purposes such as copyright protection, fingerprinting, authentication etc. For applications where the availability of original data is essential, irreversible degradation of the original data is not acceptable and incurred distortions need to be removed. Examples of such applications include multimedia archives, military image processing, and medical image processing for electronic patient records (EPRs).High capacity watermarking is proposed in the paper and implemented using integer to integer wavelet transform. The proposed scheme divides an input image into non-overlapping blocks and embeds a watermark into the high frequency wavelet coefficients of each block. The conditions to avoid both underflow and overflow in the spatial domain are derived for an arbitrary wavelet and block size. The experimental results show that the implemented scheme achieves higher embedding capacity while maintaining distortion at a lower level than the existing invertible watermarking schemes.

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