Cloud computing is a newly developed concept that aims to provide computing resources in the most effective and economical manner. The fundamental idea of cloud computing is to share computing resources among a user group. Cloud computing security is a collection of control-based techniques and strategies that intends to comply with regulatory compliance rules and protect cloud computing-related information, data apps, and infrastructure. On the other hand, data integrity is a guarantee that the digital data are not corrupted, and that only those authorized people can access or modify them (i.e., maintain data consistency, accuracy, and confidence). This review presents an overview of cloud computing concepts, its importance in many applications, and tools that can be used for providing the integrity and security to the data located in the cloud environment.
The design of superimposed codes for the multiaccess OR-channel is considered. The performance of constant weight ( CW ) codes when used as superimposed codes is investigated. Several constructions for CW codes are compared : afine geometry codes, projective geometry codes, and codes obtained by code concatenation. A comparison t o the sphere packing bound and the Johnson bounds is made. L IntroductionConsider the situation when a large number of users share a common channel. The classical solution of fixed assignment ( i.e. time division multiple access ,TDMA, or frequency division multiple access ,FDMA ) is adequate if most of the users are active most of the time. But if only a small subset is active at any time interval, the fixed assignment solution is clearly inefficient. Superimposed codes can be used in such situations. These codes are especially useful when immediate feedback is not possible, as in satellite channels. Ground stations can ,for example, use these codes to make reservations for data channels. We investigate the performance of a class of codes that can easily be characterized as superimposed codes. This class is CW codes. In section I1 the system model and formal definitions of the codes are presented. The relation between CW codes and superimposed codes is described in section 111. Bounds on superimposed codes and CW codes are given in section IV. In section V several constructions for CW codes are presented and their performance as superimposed codes is analysed. The system modelBefore we describe the system model we need some definitions.
Human skin detection, which usually performed before image processing, is the method of discovering skin-colored pixels and regions that may be of human faces or limbs in videos or photos. Many computer vision approaches have been developed for skin detection. A skin detector usually transforms a given pixel into a suitable color space and then uses a skin classifier to mark the pixel as a skin or a non-skin pixel. A skin classifier explains the decision boundary of the class of a skin color in the color space based on skin-colored pixels. The purpose of this research is to build a skin detection system that will distinguish between skin and non-skin pixels in colored still pictures. This performed by introducing a metric that measures the distances of pixel colors to skin tones. Results showed that the YCbCr color space performed better skin pixel detection than regular Red Green Blue images due to its isolation of the overall energy of an image in the luminance band. The RGB color space poorly classified images with wooden backgrounds or objects. Then, a histogram-based image segmentation scheme utilized to distinguish between the skin and non-skin pixels. The need for a compact skin model representation should stimulate the development of parametric models of skin detection, which is a future research direction.
Big data of different types, such as texts and images, are rapidly generated from the internet and other applications. Dealing with this data using traditional methods is not practical since it is available in various sizes, types, and processing speed requirements. Therefore, data analytics has become an important tool because only meaningful information is analyzed and extracted, which makes it essential for big data applications to analyze and extract useful information. This paper presents several innovative methods that use data analytics techniques to improve the analysis process and data management. Furthermore, this paper discusses how the revolution of data analytics based on artificial intelligence algorithms might provide improvements for many applications. In addition, critical challenges and research issues were provided based on published paper limitations to help researchers distinguish between various analytics techniques to develop highly consistent, logical, and information-rich analyses based on valuable features. Furthermore, the findings of this paper may be used to identify the best methods in each sector used in these publications, assist future researchers in their studies for more systematic and comprehensive analysis and identify areas for developing a unique or hybrid technique for data analysis.
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