To protect the information integrity of the medical images, this paper proposes a secure lossless recovery scheme for medical images based on image block compression and permutation ordered binary (POB) number system, which includes two parts: share generation, and lossless recovery. In the shares generation stage, the region of interest (ROI) of the original medical image and the image block compression coding algorithm JPEG-LS, are firstly adopted to generate the compressed data, which can use limited storage space to place the data repeatedly. Then, after separating the bit-plane, the compressed data are executed by data reorganization and data encryption respectively on the high-plane and the low-plane. Finally, two authentication bits are extracted by 8-bit pixel of two planes to be inserted into the pixel itself, and then the 10-bit pixel is converted into an 8-bit POB value to produce two shares of ShareH and ShareL, respectively. In the lossless recovery stage, data of placing repeatedly can restore the original image. At the same time, three attacks are carried in the two shares, which contain content cropping, content exchange, and text addition. Some comparisons with other schemes present that the proposed scheme implements a better performance under some criteria. Theoretical analysis and experimental results demonstrate that the original image can be recovered losslessly even if two shares are tampered at the ration more than 50%.
Investors and other business persons have a desire to know about the future market price because, if the investors know about the future price of a certain commodity or stock it will help them to make appropriate business decisions and they can also get profit out of their investment. There are many previous researches that has been done on stock market predictions but there is no related research that has been done on Ethiopia commodity exchange (ECX). Performing future price prediction with better accuracy and performing comparative analysis between the algorithms for two of Ethiopia commodity exchange (ECX) items which are Coffee and Sesame as the research key objectives. Three different types of prediction algorithms to predict the future price, such as Linear Regression (LR), Extreme Gradient Boosting (XGB), Long Short-Term Memory (LSTM) was utilized. There are limited researches worked on price prediction of ECX items specifically, the idea of the price prediction on different Stock markets like New York stock market Exchange and other commodity market items prediction in order to develop our research in ECX was presented. The study apart from predicting the future price, comparative analysis was implemented between the prediction algorithms that we used based on their performance. Two different datasets from ECX: coffee and sesame were used. The reason for the utilization of these datasets is, the commodity items are the largest export items in Ethiopia which makes them very important for Ethiopian economy, and the different datasets helps us to get the advantage of evaluating the algorithms with different number of datasets, since sesame dataset has 7205 instances and coffee dataset has 1540 instances and both of them has 11 attributes. We build an android application in order two implement our algorithms on mobile applications and see if it is possible to implement the prediction algorithms on mobile platforms and make it easy and accessible to users. We call this mobile application Ethiopia Coffee Prices Predictor (ECPP). This application will be used to display the prediction result of Ethiopia Coffee price for short period and it is designed in the way to be user friendly. The programming environment used to implement the prediction algorithms is python, java programming language to design our android application and we used PHP to implement the API, and finally we used MySQL database in order to store information’s online and make them accessible everywhere.
A theoretical concept of the GMDH technique using a non-linear regression model, multilayered neural nets, model assessment, and selection to determine the prediction error versus selection model complexity was reviewed and evaluated. The model selection was experimented and evaluated with MATLAB.
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