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Image compression techniques are used for reducing the amount of data required to represent a dig... more Image compression techniques are used for reducing the amount of data required to represent a digital image. An image can be compressed with the use of Walsh-Hadamard Transform (WHT), quantization and encoding steps in the compression of the JPEG image format. In this system, RGB components of color image are converted to YCbCr. For image quantization, YCbCr color space is more convenient than RGB color space. The original image is broken into 8×8 blocks of pixel. In this system, the proposed method is mainly performed by modifying Walsh-Hadamard transform (WHT) method to optimize the processing time of image compression. Each block is calculated by using the modified Walsh-Hadamard Transform (MWHT). Then image quantization calculates probability index for each unique quantity. After applying quantization, Huffman code for each unique symbol is calculated so as to compress the image using Huffman compression. The main objective of this system is to decrease the transmission time for...
American Scientific Research Journal for Engineering, Technology, and Sciences, 2017
Recommendation systems were introduced as the computer-based intelligent techniques to deal with ... more Recommendation systems were introduced as the computer-based intelligent techniques to deal with the problem of information overload. Collaborative filtering is a simple recommendation algorithm that executes the similarity (neighborhoods) between items and then computes the missing data predictions. A serious limitation of collaborative filtering is the sparisity problem, referring to the situation where insufficient rating history is available for inferring reliable similarities. This research compares four prediction methods: Weighted Sum, Mean-Centering, Boosted Weighted Sum and Boosted Double Means Centering predictions. Boosting double means centering taken into account information of both users and items in order to overcome the potential decrease of accuracy due to sparsity when predicting the missing value. It tries to capture the user and item biases from the whole effects so as to enable the better concentrating on user-item interaction. Furthermore, ensemble learning wil...
Image compression techniques are used for reducing the amount of data required to represent a dig... more Image compression techniques are used for reducing the amount of data required to represent a digital image. An image can be compressed with the use of Walsh-Hadamard Transform (WHT), quantization and encoding steps in the compression of the JPEG image format. In this system, RGB components of color image are converted to YCbCr. For image quantization, YCbCr color space is more convenient than RGB color space. The original image is broken into 8×8 blocks of pixel. In this system, the proposed method is mainly performed by modifying Walsh-Hadamard transform (WHT) method to optimize the processing time of image compression. Each block is calculated by using the modified Walsh-Hadamard Transform (MWHT). Then image quantization calculates probability index for each unique quantity. After applying quantization, Huffman code for each unique symbol is calculated so as to compress the image using Huffman compression. The main objective of this system is to decrease the transmission time for...
American Scientific Research Journal for Engineering, Technology, and Sciences, 2017
Recommendation systems were introduced as the computer-based intelligent techniques to deal with ... more Recommendation systems were introduced as the computer-based intelligent techniques to deal with the problem of information overload. Collaborative filtering is a simple recommendation algorithm that executes the similarity (neighborhoods) between items and then computes the missing data predictions. A serious limitation of collaborative filtering is the sparisity problem, referring to the situation where insufficient rating history is available for inferring reliable similarities. This research compares four prediction methods: Weighted Sum, Mean-Centering, Boosted Weighted Sum and Boosted Double Means Centering predictions. Boosting double means centering taken into account information of both users and items in order to overcome the potential decrease of accuracy due to sparsity when predicting the missing value. It tries to capture the user and item biases from the whole effects so as to enable the better concentrating on user-item interaction. Furthermore, ensemble learning wil...