The need for reliable and flexible wireless networks has significantly increased in recent years, according to the growing reliance of an enormous number of devices on these networks to establish communications and access service. Mobile Ad-hoc Networks (MANETs) allow the wireless network to establish communications without the need for infrastructure by allowing the nodes to deliver each other’s packets to their destination. Such networks increased flexibility but require more-complex routing methods. In this study, we proposed a new routing method, based on Deep Reinforcement Learning (DRL), that distributes the computations in a Software Defined Network (SDN) controller and the nodes, so that, no redundant computations are executed in the nodes to save the limited resources available on these nodes. The proposed method has been able to significantly increase the lifetime of the network, while maintaining a high Packet Delivery Rate (PDR) and throughput. The results also show that the End-to-End delay of the proposed method is slightly larger than existing routing methods, according to the need for longer alternative routes to balance the loading among the nodes of the MANET.
The goal of the research paper is to design and development of a computer-based system for the segmentation and classification of malignant skin diseases and a comparison between the accuracy of their detection, as two malignant diseases of skin diseases were detected. Namely, basal cell carcinoma and melanoma separately with images of nevus, and the images were collected from the ISIC 2020 archive group, as the total, The images used: 17,846 images include 3,008 images of basal cell carcinoma (BCC), 5,272 images of melanoma, and 9,566 images of a nevus, and validation data contains 20% of the images used which are not classified and randomly taken from the set of images, and the final test data contains 1,500 anonymous images. An architecture for the convolutional neural network technology in deep learning has been proposed that consists of a set of layers for processing. Processing raw input images for a group of pre-treatment transformations, the data augmentation process, so the number of images used became 86094 images of nevus, 27,072 images of BCC, and 47,448 images of melanoma. Through the detection process, the classification and detection accuracy of BCC was 98.25%, which is higher than the classification accuracy of melanoma is 91.61%.
In other way misuse of correct illumination at the capture moment could affect the image landmarks ; regarding color brightness and the increasing "color cast " which might cause the image to appear in an unacceptable Or unexpected manner. Thus; several algorithms have been developed to solve these problems and balancing image color and recover the real color of the landscape. In this research an algorithm has been developed, depending on some statistics tools like (Mean, Variance and Equivalent Circle). Which leads to finding out the influential color in the image which leads to the alteration of the nature of its colors. It is called "color cast ". It could be classified into evident cast, predominant color, ambiguous cast or no cast. Then removing the cast distortion from the image and using error back propagation network for images classification into color cast carrier or uncarrier. This research has been applied on colored digital photos (BMP). More than (100) colored images were also used containing all sorts of color cast that will be found out, classified and finally removed from the image by using algorithm. The percentage of images which have no cast are (27%),The images have evident cast are (25%), where the images which have ambiguous cast are (16%),At the last ;the images which classified as predominant color are (12%),as well as there are (20%) of images classified as wrong .
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