Abstract:The drastic increase of websites is one of the causes behind the recent information overload on the internet. A recommender system (RS) has been developed for helping users filter information. However, the cold-start and sparsity problems lead to low performance of the RS. In this paper, we propose methods including the visual-clustering recommendation (VCR) method, the hybrid between the VCR and user-based methods, and the hybrid between the VCR and item-based methods. The user-item clustering is based on the genetic algorithm (GA). The recommendation performance of the proposed methods was compared with that of traditional methods. The results showed that the GA-based visual clustering could properly cluster user-item binary images. They also demonstrated that the proposed recommendation methods were more efficient than the traditional methods. The proposed VCR2 method yielded an F1 score roughly three times higher than the traditional approaches.
The demand to preserve raw image data for further processing has been increased with the hasty growth of digital technology. In medical industry the images are generally in the form of sequences which are much correlated. These images are very important and hence lossless compression Technique is required to reduce the number of bits to store these image sequences and take less time to transmit over the network The proposed compression method combines Super-Spatial Structure Prediction with interframe coding that includes Motion Estimation and Motion Compensation to achieve higher compression ratio. Motion Estimation and Motion Compensation is made with the fast block-matching process Inverse Diamond Search method. To enhance the compression ratio we propose a new scheme Bose, Chaudhuri and Hocquenghem (BCH). Results are compared in terms of compression ratio and Bits per pixel to the prior arts. Experimental results of our proposed algorithm for medical image sequences achieve 30% more reduction than the other state-of-the-art lossless image compression methods.
Medical image security can be enhanced using the reversible watermarking technique, it allows us to embed the relevant information with the image, which provides confidentiality, integrity and authentication by embedding RSA encrypted digital signature with the image. This paper discusses the need for reversible watermarking techniques and security related problems in medical images. Here we are comparing the lossless watermarking techniques for various medical image modalities like MRI (Magnetic resonance imaging), US (Ultrasonic), PET (Positron emission tomography), Endoscopic and angiographic images. For the discussions we can take ROI supporting lossless watermarking systems. This lossless watermarking is responsible for recovering the altered medical image content of the system.
General TermsMedical Image Security, Security using SHA-256, Compression Techniques and performance analysis of various algorithms.
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