Machine Learning is seeing its growth more rapidly in this decade. Many applications and algorithms evolve in Machine Learning day to day. One such application found in journals is house price prediction. House prices are increasing every year which has necessitated the modeling of house price prediction. These models constructed, help the customers to purchase a house suitable for their need. Proposed work makes use of the attributes or features of the houses such as number of bedrooms available in the house, age of the house, travelling facility from the location, school facility available nearby the houses and Shopping malls available nearby the house location. House availability based on desired features of the house and house price prediction are modeled in the proposed work and the model is constructed for a small town in West Godavari district of Andhrapradesh. The work involves decision tree classification, decision tree regression and multiple linear regression and is implemented using Scikit-Learn Machine Learning Tool.

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Designing Universal embedded hardware architecture for discrete wavelet transform is a challenging problem because of diversity among wavelet kernel filters. In this work, DWT is used for compression application. Wavelet transform divides the information of an image into approximation and details sub signals. The approximation sub signals shows the general trend of pixel values and other three detail sub signals show the vertical, horizontal and diagonal details or changes in the images. If these details are very small (threshold) then they can be set to zero without significantly changing the image. The greater the number of zeros the greater the compression ratio. If the energy retained (amount of retained by an image after compression and decompression) is 100% then the compression is lossless as the image can be reconstructed exactly. The design follows the JPEG2000 standard and can be used for both lossy and lossless compression. The High-performance and memory-efficient pipeline architecture which performs the one-level (2-D) DWT in the 5/3 and 9/7 filters.Index terms -Sub-band coding, discrete wavelet transform (DWT).I.

-Video coding plays an important role in video transmission and storage applications.Today's increasing order of multimedia applications led to a lot of research works in video coding in such a way that high compression ratio is achieved with the available bandwidth. Wavelet based image compression has witnessed great success in the past decade. Wavelet transform based motion compensated video codec performs better compression in order to meet the rate and distortion constraint in video transmission than the block based techniques. However, it is well known that the 2D DWT does not represent directional features of images efficiently. Lots of efforts have been put into multiscale directional representation. In this paper, video coding using directional transform DDWT is considered and its expansive nature is reduced by noise shaping algorithm. High compression ratio is achieved through the selection of optimal coefficients of DDWT using Multi Objective Particle Swarm Optimization (MOPSO) method. In this video coding technique, the objective functions of Entropy, Computation Time and Mean Square Error are considered for optimization with the constraints of bits per pixel and frame rate. The selected optimum coefficients are encoded using EZW method. The performance of the proposed method is compared with the standard 3D SPIHT coding.