With the revolution of information technology and Wide Area Networking, data has become less and less private where the access of media as well as the attempts to change and manipulate the contents of media data have become a common case. For that, we need to use a watermarking technique to protect the copyright of the media as well as for digital right management but without leaving a visual effect. We presented a watermarking technique that deals with images where the used technique to embed a wavelet compressed watermark image within the least significant bit (LSB) of the cover image pixels in a specific pattern which won't be visible after embedding and will cause the cover image to become copyrighted using the embedded watermark image that can be extracted later
In this paper, we propose a neural network approach to forecast AM/PM Jordan electric power load curves based on several parameters (temperature, date and the status of the day). The proposed method has an advantage of dealing with not only the nonlinear part of load curve but also with rapid temperature change of forecasted day, weekend and special day features. The proposed neural network is used to modify the load curve of a similar day by using the previous information. The suitability of the proposed approach is illustrated through an application to actual load data of Electric Power Company in Jordan. The results show an acceptable prediction for Short-Term Electrical Load Forecasting (STELF), with maximum regression factor 90%.
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