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Association Rule discovery has been an important problem of investigation in knowledge discovery and data mining. An association rule describes associations among the sets of items which occur together in transactions of databases.The Association Rule mining task consists of finding the frequent itemsets and the rules in the form of conditional implications with respect to some prespecified threshold values of support and confidence.The interestingness of Association

The lossy compression scheme is often used to compress data such as digital image and video. Such a video compression scheme based on the wavelet transform is presented in this paper. The multi-resolution/multi-frequency nature of the discrete wavelet transform is an ideal tool for representing images and video signals. Wavelet transform decomposes a video frame into a set of sub-frames with different resolutions corresponding to different frequency bands. These multi resolution frames also provide a representation of the global motion structure of the video signal at different scales. The motion activities for a particular sub-frame at different resolutions are different but highly correlated since they actually specify the same motion structure at different scales. In the multi-resolution motion compensation approach, motion vectors in higher resolution are predicted by the motion vectors in the lower resolution and are refined at each step. In this paper, we propose a variable block-size MRMC (Multi-Resolution Motion Compensation) scheme. The approximate sub-image which carries the maximum information of the image sequence is compensated using the smallest block size and the other approximate sub-images are compensated using relatively bigger block sizes. The motion vectors of the approximate sub-images in lower resolution are used for motion compensation of their corresponding sub-images. This scheme considerably reduces the computing load, storage and transmission bandwidth. During transmission of an image sequence, only the residual image after motion compensation and the motion vector of the first level decomposition need to be transmitted to the decoder in order to reconstruct the original image. The simulation results show thatthe proposed approach has a satisfactory performance in terms of peak-to-peak signal-to-noise ratio (PSNR).

<p>Quality of Service (QoS) is one of the most important parameters to be considered in computer networking and communication. The traditional network incorporates various quality QoS frameworks to enhance the quality of services. Due to the distributed nature of the traditional networks, providing quality of service, based on service level agreement (SLA) is a complex task for the network designers and administrators. With the advent of software defined networks (SDN), the task of ensuring QoS is expected to become feasible. Since SDN has logically centralized architecture, it may be able to provide QoS, which was otherwise extremely difficult in traditional network architectures. Emergence and popularity of machine learning (ML) and deep learning (DL) have opened up even more possibilities in the line of QoS assurance. In this article, the focus has been mainly on machine learning and deep learning based QoS aware protocols that have been developed so far for SDN. The functional areas of SDN namely traffic classification, QoS aware routing, queuing, and scheduling are considered in this survey. The article presents a systematic and comprehensive study on different ML and DL based approaches designed to improve overall QoS in SDN. Different research issues & challenges, and future research directions in the area of QoS in SDN are outlined. <b></b></p>