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Background Skin cancer is one of the life threating diseases in the world. So, millions of lives can be saved by early detection of skin cancer. In addition, automating the computer‐aided system of skin lesion detection and classification (SLDC) will assist the medical practitioners to ensure more efficacious treatment of skin lesion disease. Material and Method In this article, a hybrid preprocessing‐based transfer learning model for SLDC is proposed, which is named as SLDCNet. Initially, the hybrid Gaussian filter (HGF) with connected component label (CCL) based fast march inpainting procedure is used for hair removal and denoising of skin lesions. Next, full resolution convolutional networks (FrCN) based segmentation method is adapted for detecting the cancer region. Then, feature extraction is performed using deep residual learning and finally, transfer learning mechanism is applied for classification of eight skin lesions. Results The extensive simulation results shows that proposed SLDCNet resulted in a classification accuracy of 99.92%, sensitivity of 99%, and specificity of 99.36%, respectively. Conclusion From the obtained results, it is proven that proposed SLDCNet provides better performance as compared to state‐of‐art SLDC approaches, and even the standard ISIC‐2019 public challenge.

Day by day, the transmission or sharing of digital information online is increasing due to the fact that Internet usage has become somewhat of an addiction for the majority of people. This results in larger copyright violation problems. Nowadays, most of the data or information is being transmitted in the form of digital images or videos, which are rather simple for the violators of copyright to forge or fake and then share for profit. Thus, digital watermarking came into existence as a possible solution to address these violations of copyright. This article proposes a new blind video watermarking (BVW) using redundant discrete wavelet transform (RDWT) in singular value decomposition (SVD) domain, which utilizes the advantages of both RDWT and SVD to embed and extract the watermark information into the cover video without degrading the quality of the watermark. In addition, an efficient meta‐heuristic optimization with multi‐objective optimization (MOO) is employed to further optimize the proposed RDWT‐SVD approach. Further, various attacks were applied to the watermarked frame to disclose the robustness of proposed BVW using RDWT‐SVD with MOO approach. Extensive simulations on several test videos with comparison to the conventional BVW methodologies exhibit the transcendency of the proposed BVW using RDWT‐SVD with MOO approach in terms of watermarking quality evaluation metrics such as peak signal‐to‐noise ratio (PSNR), structural similarity index, normalized correlation, and even that of root mean square error as well.

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Recent years there is a fast development in remote technologies, consistently G-bytes of information hosts been trading between the gatherings. Secure transmission of information is an exceptionally difficult undertaking for private applications, for example, military, common, medicinal and web applications. Here, we expected to present “A New Encryption and Decryption for 3D MRT Images”. Our proposed algorithm has shown the robustness over conventional algorithms in terms of secure concern.