Yazar "Alhanaf, Ahmed Sami" seçeneğine göre listele

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    Öğe
    Deep transfer learning methods for classification colorectal cancer based on histology images
    (Institute of Electrical and Electronics Engineers Inc., 2022) Alhanaf, Ahmed Sami; Al-Jumaili, Saif; Bilgin, Gökhan; Duru, Adil Deniz; Alyassri, Salam; Balık, Hasan Hüseyin
    Deep transfer learning is one of the common techniques used to classify different types of cancer. The goal of this research is to focus on and adopt a fast, accurate, suitable, and reliable for classification of colorectal cancer. Digital histology images are adjustable to the application of convolutional neural networks (CNNs) for analysis and classification, due to the sheer size of pixel data present in them. Which can provide a lot of information about colorectal cancer. We used ten different types of pr-trained models with two type method of classification techniques namely (normal classification and k-fold crosse validation) to classify the tumor tissue, we used two different kinds of datasets were these datasets consisting of three classes (normal, low tumors, and high tumors). Among all these eight models of deep transfer learning, the highest accuracy achieved was 96.6% with Darknet53 for 5-Fold and for normal classification the highest results obtained was 98.7% for ResNet50. Moreover, we compared our result with many other papers in stat-of-the-art, the results obtained show clearly the proposed method was outperformed the other papers.
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    Öğe
    Renewable energy utilization in demand-side energy management system based on linear programming optimization algorithm
    (American Institute of Physics, 2024) Almashhadani, Muna Kamel; Çevik, Mesut; Al-Jumaili, Saif; Alhanaf, Ahmed Sami; Al-Bhadely, Faraj Khlaf; Uçan, Osman Nuri
    Demand-side management (DSM) is an effectual approach by coordinating utility management and routinely tracking energy usage, the intelligent grid assists in controlling energy demand and promotes its efficiency. However, the paper aims to utilize Linear programming optimization algorithms as an effective tool for managing energy demand and maximizing the use of renewable energy sources. These algorithms are able to estimate which is the best utilization of what resources are accessible and reduce consumption by describing the energy system as a collection of linear equations. The optimization system makes assumptions about the various energy costs when it will be high or low and modifies energy use accordingly. We applied different scenarios to assess the resiliency of the system. The simulation took into account a number of variables, including the weather, energy usage, and pricing fluctuations. MATLAB R2023a and Simulink provide an integrated platform with data analytics to build the proposed system and optimization model to minimize cost in MATLAB. Compared to other methods using various optimization algorithms as the binary orientation search algorithm (BOSA), cockroach swarm optimization (CSO), and the sparrow search algorithm (SSA) were applied to DSM methodology for a residential community with a primary focus on decreasing peak energy consumption results as in previous study was, BOSA has a lower standard deviation (0.8) compared to the other algorithms (1.7 for SSA and 1.3 for CSOA), making it more robust and superior, in addition to minimizing cost (5438.98 cents of USD (mean value) and 16.3% savings), the suggested approach is used for lowering electrical energy costs in a micro-grid system while maintaining their regular load and operating hours.