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To check out the health of the patient, digital images are generated every single day and are used by the radiologist for extracting out the details and anomalies. The complicated part is to figure out the disease in those images. By the manual diagnosis of the images through the radiologists, the doctors can get to know exact scenario of the abnormalities in images, but is considerably more difficult with Content Based Image Retrieval (CBIR) to get those finer details from MR images. These CBIR approaches are now frequently employed in the automatic diagnosis of disease from MR images, mammograms, and other sources. Bridging this gap can be done with deep learning feature extraction algorithm and the canny edge detection technique we propose, and accuracy closer to the manual results of a human evaluator can be achieved to a significant extent as part of the goal of sustainable development through innovation.

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this paper demonstrates the elementary and basic functioning, noteworthy limitations of the existing scenario, the proposed scheme for Endpoint based Call Admission Control for the Voice over Internet Protocol over IEEE 802.11 Wireless Local Area Network for removing the existing issues and flaws, and it's implementation for proving its necessity as well as importance in the real time scenario. According to the results obtained, the proposed scheme for Call Admission Control is found more efficient, reliable, improved in terms of Quality of Service, overcoming the flaws of the present scenario, and delivering the effective test results, when it was being evaluated on experimental testbed, with the added functionality of Retransmission Timer as a main component.