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    Investigating and calculating the temperature of hot-spot factor for transformers
    (Institute of Advanced Engineering and Science, 2023) Yahya, Khalid; Attar, Hani; Issa, Haitham; Ramadan Dofan, Jamal Ali; Iqteit, Nassim A.; Yahya, Adel E.M.; Solyman, Ahmed Amin Ahmed
    This article explores the measurement of temperature in transient states, utilizing the principles of heat transfer and thermal-electrical metaphor. The study focuses on the nonlinear thermal resistances present in various locations within a distribution transformer, while taking into account variations in oil physical variables and temperature loss. Real-time data obtained from heat run tests on a 250-MVA-ONAF cooled unit, conducted by the transformer manufacturer, is used to verify the thermal designs. The observations are then compared to the loading framework of the IEC 60076-7:2005 system. The findings of this research provide a better understanding of temperature measurement in transient states, particularly in distribution transformers, and can be applied to the design and development of more efficient and reliable transformer systems. © 2023 Institute of Advanced Engineering and Science. All rights reserved.
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    A New Feature Selection Method Based on Hybrid Approach for Colorectal Cancer Histology Classification
    (Hindawi Limited, 2022) Deif, Mohanad A.; Attar, Hani; Amer, Ayman; Issa, Haitham; Khosravi, Mohammad R.; Solyman, Ahmed A. A.
    Colorectal cancer (CRC) is one of the most common malignant cancers worldwide. To reduce cancer mortality, early diagnosis and treatment are essential in leading to a greater improvement and survival length of patients. In this paper, a hybrid feature selection technique (RF-GWO) based on random forest (RF) algorithm and gray wolf optimization (GWO) was proposed for handling high dimensional and redundant datasets for early diagnosis of colorectal cancer (CRC). Feature selection aims to properly select the minimal most relevant subset of features out of a vast amount of complex noisy data to reach high classification accuracy. Gray wolf optimization (GWO) and random forest (RF) algorithm were utilized to find the most suitable features in the histological images of the human colorectal cancer dataset. Then, based on the best-selected features, the artificial neural networks (ANNs) classifier was applied to classify multiclass texture analysis in colorectal cancer. A comparison between the GWO and another optimizer technique particle swarm optimization (PSO) was also conducted to determine which technique is the most successful in the enhancement of the RF algorithm. Furthermore, it is crucial to select an optimizer technique having the capability of removing redundant features and attaining the optimal feature subset and therefore achieving high CRC classification performance in terms of accuracy, precision, and sensitivity rates. The Heidelberg University Medical Center Pathology archive was used for performance check of the proposed method which was found to outperform benchmark approaches. The results revealed that the proposed feature selection method (GWO-RF) has outperformed the other state of art methods where it achieved overall accuracy, precision, and sensitivity rates of 98.74%, 98.88%, and 98.63%, respectively. © 2022 Mohanad A. Deif et al.
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    Prediction of Wear Rates of UHMWPE Bearing in Hip Joint Prosthesis with Support Vector Model and Grey Wolf Optimization
    (Hindawi Limited, 2022) Hammam, Rania E.; Attar, Hani; Amer, Ayman; Issa, Haitham; Vourganas, Ioannis; Solyman, Ahmed; Venu, P.
    One of the greatest challenges in joint arthroplasty is to enhance the wear resistance of ultrahigh molecular weight polyethylene (UHMWPE), which is one of the most successful polymers as acetabular bearings for total hip joint prosthesis. In order to improve UHMWPE wear rates, it is necessary to develop efficient methods to predict its wear rates in various conditions and therefore help in improving its wear resistance, mechanical properties, and increasing its life span inside the body. This article presents a support vector machine using a grey wolf optimizer (SVM-GWO) hybrid regression model to predict the wear rates of UHMWPE based on published polyethylene data from pin on disc (PoD) wear experiments typically performed in the field of prosthetic hip implants. The dataset was an aggregate of 29 different PoD UHMWPE datasets collected from Google Scholar and PubMed databases, and it consisted of 129 data points. Shapley additive explanations (SHAP) values were used to interpret the presented model to identify the most important and decisive parameters that affect the wear rates of UHMWPE and, therefore, predict its wear behavior inside the body under different conditions. The results revealed that radiation doses had the highest impact on the model's prediction, where high values of radiation doses had a negative impact on the model output. The pronounced effect of irradiation doses and surface roughness on the wear rates of polyethylene was clear in the results when average disc surface roughness Ra values were below 0.05 ?m, and irradiation doses were above 95 kGy produced 0 mg/MC wear rate. The proposed model proved to be a reliable and robust model for the prediction of wear rates and prioritizing factors that most significantly affect its wear rates. The proposed model can help material engineers to further design polyethylene acetabular linings via improving the wear resistance and minimizing the necessity for wear experiments. © 2022 Rania E. Hammam et al.