https://www.techscience.com/csse/v36n2/41114/pdf

A Quantum Spatial Graph Convolutional Network for Text Classification

We are proposing a new decentralized access control system using ABE algorithm in which it secures the data which is stored in clouds that maintains anonymous authentication of the user In this system, before storing the data the cloud confirms the validity of the series by not knowing the individuality of the users Our system has an extra feature of access control scheme with which allowed only legal user to decrypt the saved content The proposed system avoids the replay attacks of the data which is saved in cloud that supports for creation and modification The proposed system reports the user revocation and it is more robust

Access control with anonymous authentication of data stored in clouds using abe algorithm

The size of multimedia contents is one of the biggest challenges, especially the storage and transmission the same during emergency situations. Compression is one of the fundamental techniques to address this problem. In this work, we have developed an improved Joint Photographic Experts Group (JPEG) compression algorithm called LRJPEG (Luminance Reduction JPEG) using optimized modifications in the luminance quantization table for both color and grayscale images. The quantization in an important step in the JPEG compression process. The default quantization table ( $8\times 8$ ) depends on both the chrominance and luminance components of an image. We have modified the $8\times 8$ default luminance quantization table. To assess the performance of the improved JPEG algorithm, we have used different color and grayscale medical images during the compression process. We have evaluated CR (Compression Ratio), PSNR (Peak signal-to-noise ratio), and MSE (mean squared error) of both standard and a modified quantization table. The results show that the performance of JPEG compression using modified luminance quantization table has improved compared with the default luminance quantization table.

LRJPEG: A Luminance Reduction based Modification for JPEG Algorithm to Improve Medical Image Compression

Cloud computing offers advantages in terms of scalability and availability as the ability to access resources anytime and anywhere over an external server can bring about issues concerning data privacy, however. To combat this, one might have the data go through an encryption process prior to a server upload. This approach however can tamper with the ability to work with the data on a plain text level. Hence, it is necessary to enable the cloud server to make use an encrypted search feature for efficient data accessibility. This paper intends to provide a comparative study of keyword search-based of multi-keyword ranked search implementations with the intention of comparing them in terms of efficiency. The performance of these implementations with respect to speed of searches made over encrypted data with the same is extended in the view of decryption. Results are showcased in graphs to enable the reader to get a comparative understand of these implementations at a glance.

Comparative study on RSA algorithm of multi-keyword search scheme over encrypted cloud data

Researchers have had numerous opportunities to construct new analytical procedures in various scientific domains due to the development of machine learning and soft computing techniques. Livestock identification procedures are used to categorize different breeds of cattle based on their size and colour. A distinctive radio frequency, image frequency, or ear tag is necessary for the current methods used to predict specific breeds of cattle. We advise using different cattle picture datasets for the identification of the breed of cattle. The front, back, and sides of cattle will be photographed to extract key features, and these feature extractions will be carried out to uniquely identify each cattle with its information. The breed of cattle will be identified based on the processed images from each set of image data. Following picture preprocessing, each cow's images will be divided up by breed. Based on a preprocessed image dataset of cattle breeds, another category of rare cattle breeds is identified. In this study, the images of cattle facial morphology and various body shapes are recognized using the SVM machine learning algorithm. We may learn about Breed's fundamental characteristics, such as colour, average milk production, average body mass, etc., thanks to the proposed approach. 

Cattle Breed Detection and Categorization Using Image Processing and Machine Learning

A survey on classification and clustering algorithms for uncompressed and compressed text

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A survey on classification and clustering algorithms for uncompressed and compressed text