In this modern era, it has become much simpler to replicate, sell, and copy the copyright owners' works without their permission as a result of the expansion of digitalization, and it is difficult to identify such violations, posing a threat to the creators' and copyright owners' rights. For many years, the internet has been regarded as one of the most serious threats to copyright, and the content available has varying levels of copyright protection. On the internet, there are numerous copyrighted works, including e-books, movies, news, and so on. Therefore, by using watermarking and steganography techniques, these issues can be solved, which are based on the author's signature information or logo. This paper concluded that the techniques of discrete cosine transform (DCT), discrete wavelet transform (DWT), one-time pad (OTP), and playfair are highly effective when used together to watermark an image or embed a secret message, our lab results validate that our algorithm scheme is robust against several sets of attacks, where the algorithm was assessed by computation of many evaluation metrics such as mean square error (MSE), signal-to-noise ratio (SNR), and peak signal-to-noise ratio (PSNR).

Traffic signs object detection has gained high interest in recent years, as one of the most significant object detector applications. The development of deep learning technologies gives support to traffic signs detector which it offers several advantages, including the benefit of high detection precision and the timely response to condition changes of traffic signs. Therefore, this paper shows an efficient method for detecting traffic signs. Hence, it implements a new Iraqi Traffic Sign Detection Benchmark (IQTSDB) dataset based on You Only Look Once version 5 (YOLOv5) algorithm. The experimental results show that the implementation of the IQTSDB dataset with YOLOv5 has high efficiency in different conditions such as sunny, cloudy, weak light, and rainy conditions. Besides, real images has been captured for the traffic signs in Baghdad. In addition, the results show that the YOLOv5 has high efficiency in detecting traffic signs of different sizes (small, medium, large), and mean Average Precision (mAP) compared to yolov2, and yolov3.

Several recent scientific papers have been proposed on power consumption in Wireless Sensor Networks (WSN). Still, the work focused on control topologies of WSN in the field of increasing and prolonging the whole network is limited. This results from their function as routers that send data from edge nodes to the sink. The nodes around the sink node quickly die while delivering much data through intermediate nodes from one node to the sink node. The network lifetime was reduced because the sensor nodes adjacent to the sink consume twice as much power as the others. This problem is not recognized and addressed in more visual detail by the authors. The nodes closest to the sink node will die first. Therefore, this paper proposed a mathematical model to represent packet energy consumption with control topology for WSN. Due to the simulation, more than 90% of sensors are still operational and optimally arranged. The overall battery power remaining in the sensors was greater than 70%.