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    Convolutional Neural Network-Based Lung Cancer Nodule Detection Based on Computer Tomography
    (Springer Science and Business Media Deutschland GmbH, 2023) Ahmed, A.H.; Alwan, H.B.; Çakmak, M.
    Because of the great responsiveness of aspiratory knob location, computerized tomography (CT) is generally used to analyze cellular breakdown in the lungs without performing biopsy, which could make actual harm nerves and vessels. Notwithstanding, recognizing threatening and harmless aspiratory knobs stays troublesome. Since CT checks are regularly of low goal, it is challenging for radiologists to peruse the output picture’s subtleties. The proceeded with quick development of CT examine examination frameworks lately has made a squeezing need for cutting edge computational apparatuses to remove helpful highlights to help the radiologist in understanding advancement. PC-supported discovery (CAD) frameworks have been created to diminish notable mistakes by distinguishing the dubious highlights a radiologist searches for in a case survey. Our project aims to compare performance of various low memories, lightweight deep neural net (DNN) architectures for biomedical image analysis. It will involve networks like vanilla 2D CNN, U-Net, 2D SqueezeNet, and 2D MobileNet for two case classifications to discover the existence of lung cancer in patient CT scans of lungs with and without primary phase lung cancer. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Energy Efficiency Performance For Next Generation Wireless Communications
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ahmed, A.H.; Alrubaee, S.H.; Hasan, H.K.; Mohammed, A.H.
    The growth demand in wireless communication and its application leads to a great effort should be into researcher's consideration to meet the future requirement of wireless network architecture. It's expected that the traffic will reaches multiple of hundreds than this in nowadays and to increase the capacity of the network with high and efficient energy efficiency. This can be reached by the use of small cell configuration like micro and pico cells, and use massive MIMO (Massive Multiple-input Multiple-output) with low-cost components which are prone to hardware impairments. This configuration leads to high energy efficiency for large number of base stations and user density. This article focuses in simulating an area covered by random deployment of small cells to serve hundreds of users in the simulation area. The results show that the (Energy efficiency) decreases as the SINR values increases, which is why it is important to specify a target SINR; otherwise the energy efficiency maximizing operation point might be very spectrally inefficient, and the efficient energy can be greatly improved by increasing the base station density, meaning that small cells are a promising solution for maximal energy efficiency deployment. © 2022 IEEE.