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Öğe ARIMA Model Estimation Based on Genetic Algorithm for COVID-19 Mortality Rates(World Scientific Publ Co Pte Ltd, 2021) Deif, Mohanad A.; Solyman, Ahmed A. A.; Hammam, Rania E.This paper presents a forecasting model for the mortality rates of COVID-19 in six of the top most affected countries depending on the hybrid Genetic Algorithm and Autoregressive Integrated Moving Average (GA-ARIMA). It was aimed to develop an advanced and reliable predicting model that provides future forecasts of possible confirmed cases and mortality rates (Total Deaths per 1 million Population of COVID-19) that could help the public health authorities to develop plans required to resolve the crisis of the pandemic threat in a timely and efficient manner. The study focused on predicting the mortality rates of COVID-19 because the mortality rate determines the prevalence of highly contagious diseases. The Genetic algorithm (GA) has the capability of improving the forecasting performance of the ARIMA model by optimizing the ARIMA model parameters. The findings of this study revealed the high prediction accuracy of the proposed (GA-ARIMA) model. Moreover, it has provided better and consistent predictions compared to the traditional ARIMA model and can be a reliable method in predicting expected death rates as well as confirmed cases of COVID-19. Hence, it was concluded that combining ARIMA with GA is further accurate than ARIMA alone and GA can be an alternative to find the parameters and model orders for the ARIMA model.Öğe Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach(MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2021) Deif, Mohanad A.; Solyman, Ahmad Amin Ahmad; Alsharif, Mohammed H.; Uthansakul, PeerapongThe sudden increase in patients with severe COVID-19 has obliged doctors to make admissions to intensive care units (ICUs) in health care practices where capacity is exceeded by the demand. To help with difficult triage decisions, we proposed an integration system Xtreme Gradient Boosting (XGBoost) classifier and Analytic Hierarchy Process (AHP) to assist health authorities in identifying patients’ priorities to be admitted into ICUs according to the findings of the biological laboratory investigation for patients with COVID-19. The Xtreme Gradient Boosting (XGBoost) classifier was used to decide whether or not they should admit patients into ICUs, before applying them to an AHP for admissions’ priority ranking for ICUs. The 38 commonly used clinical variables were considered and their contributions were determined by the Shapley’s Additive explanations (SHAP) approach. In this research, five types of classifier algorithms were compared: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), Random Forest (RF), and Artificial Neural Network (ANN), to evaluate the XGBoost performance, while the AHP system compared its results with a committee formed from experienced clinicians. The proposed (XGBoost) classifier achieved a high prediction accuracy as it could discriminate between patients with COVID19 who need ICU admission and those who do not with accuracy, sensitivity, and specificity rates of 97%, 96%, and 96% respectively, while the AHP system results were close to experienced clinicians’ decisions for determining the priority of patients that need to be admitted to the ICU. Eventually, medical sectors can use the suggested framework to classify patients with COVID-19 who require ICU admission and prioritize them based on integrated AHP methodologies.Öğe A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences(AMER INST MATHEMATICAL SCIENCES-AIMS, PO BOX 2604, SPRINGFIELD, MO 65801-2604, 2021) Deif, Mohanad A.; Solyman, Ahmad Amin Ahmad; Kamarposhti, Mehrdad Ahmadi; Band, Shahab S.; Hammam, Rania E.In this work, Deep Bidirectional Recurrent Neural Networks (BRNNs) models were implemented based on both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells in order to distinguish between genome sequence of SARS-CoV-2 and other Corona Virus strains such as SARS-CoV and MERS-CoV, Common Cold and other Acute Respiratory Infection (ARI) viruses. An investigation of the hyper-parameters including the optimizer type and the number of unit cells, was also performed to attain the best performance of the BRNN models. Results showed that the GRU BRNNs model was able to discriminate between SARS-CoV-2 and other classes of viruses with a higher overall classification accuracy of 96.8% as compared to that of the LSTM BRNNs model having a 95.8% overall classification accuracy. The best hyperparameters producing the highest performance for both models was obtained when applying the SGD optimizer and an optimum number of unit cells of 80 in both models. This study proved that the proposed GRU BRNN model has a better classification ability for SARS-CoV-2 thus providing an efficient tool to help in containing the disease and achieving better clinical decisions with high precision.Öğe Design of Biodegradable Mg Alloy for Abdominal Aortic Aneurysm Repair (AAAR) Using ANFIS Regression Model(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141, 2022) Hammam, Rania E.; Solyman, Ahmad Amin Ahmad; Alsharif, Mohammed H.; Uthansakul, Peerapong; Deif, Mohanad A.ABSTRACT Abdominal aortic aneurysm (AAA) is among the most widespread and dangerous diseases that may cause death. Recently, Endovascular Aneurysm Repair outperformed open aortic surgery, since it is a safe and reliable technique where a stent graft system is placed within the aortic aneurysm. It was aimed to design an Mg biodegradable alloy with bio-friendly alloying elements that enhance the corrosion resistance and mechanical properties of the alloy for the design of stents for Abdominal Aortic Aneurysm (AAA) repair. Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed for the design of the Mg alloy and compared to other traditional machine learning regression models (Multiple Linear Regression (MLR) and Gradient Boosting (GB). The dataset utilized in this work consisted of 600 samples of Mg alloys that were collected from the mat web database and additional papers from Google Scholar. The results revealed the superior prediction capability of the ANFIS model since it attained maximum R 2 scores of 0.926, 0.958, and 0.988 for the prediction of UTS, YS, and Ductility respectively. Furthermore, the ANFIS model was capable of designing an Mg biodegradable alloy having a UTS, YS, and Ductility of 346.148 Mpa, 230.8 Mpa, and 15.4% respectively which are excellent mechanical properties satisfying vascular stents requirements The ANFIS model can be further applied to speed up the design of other alloys in the future for various medical applications, reducing the time, cost, and effort of large searching space.Öğe Development of a Hybrid Support Vector Machine with Grey Wolf Optimization Algorithm for Detection of the Solar Power Plants Anomalies(Mdpi, 2023) Ahmed, Qais Ibrahim; Attar, Hani; Amer, Ayman; Deif, Mohanad A.; Solyman, Ahmed A. A.Solar energy utilization in the industry has grown substantially, resulting in heightened recognition of renewable energy sources from power plants and intelligent grid systems. One of the most important challenges in the solar energy field is detecting anomalies in photovoltaic systems. This paper aims to address this by using various machine learning algorithms and regression models to identify internal and external abnormalities in PV components. The goal is to determine which models can most accurately distinguish between normal and abnormal behavior of PV systems. Three different approaches have been investigated for detecting anomalies in solar power plants in India. The first model is based on a physical model, the second on a support vector machine (SVM) regression model, and the third on an SVM classification model. Grey wolf optimizer was used for tuning the hyper model for all models. Our findings will clarify that the SVM classification model is the best model for anomaly identification in solar power plants by classifying inverter states into two categories (normal and fault).Öğe Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach(HINDAWI LTD, ADAM HOUSE, 3RD FLR, 1 FITZROY SQ, LONDON W1T 5HF, ENGLAND, 2022) Deif, Mohanad A.; Attar, Hani; Amer, Ayman; Elhaty, Ismail A. M.; Khosravi, Mohammad R.; Solyman, Ahmad Amin AhmadOverall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.Öğe Gradient Boosting Machine Based on PSO for prediction of Leukemia after a Breast Cancer Diagnosis(Insight Society, 2021) Deif, Mohanad A.; Hammam, Rania E.; Solyman, Ahmed A. A.The purpose of this study is to develop an accurate risk predictive model for Chronic Myeloid Leukemia (CML) after an early diagnosis of Breast Cancer (BC). Gradient Boosting Machine (GBM) classification algorithm has been applied to the SEER breast cancer dataset for females diagnosed with BC from 2010 to 2016. A practical Swarm optimizer (PSO) was utilized to optimize the GBM algorithm's hyperparameters to find the SEER dataset's best attributes. Nine attributes were carefully selected to study the growth of CML after a lag time of 6 months following BC's diagnosis. The results revealed that the predictive model could classify patients with breast cancer only and patients with breast cancer with Leukemia by an achieved Accuracy, Sensitivity, and Specificity rates of 98.5 %, 99 %, 97.85 %, respectively. To verify the performance of the proposed algorithm, the accuracy of the suggested GBM classifier model was compared with another state-of-the-art model classifiers KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and RF (Random Forest), which are commonly applied algorithms in most of the existing literature. The results also proved the superior ability of the implemented GBM model Classifier in the classification of breast cancer disease and prediction of patients having Leukemia developed after having breast cancer. These results are promising as they show the integral role of the GBM classifier to classify and predict the tumor with high accuracy and efficiency, which will further help in better cancer diagnosis and treatment of the disease. © 2021, IJASEIT. All rights reserved.Öğe A Hybrid Multi-Objective Optimizer-Based SVM Model for Enhancing Numerical Weather Prediction: A Study for the Seoul Metropolitan Area(MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2022) Deif, Mohanad A.; Solyman, Ahmad Amin Ahmad; Alsharif, Mohammed H.; Jung, Seungwon; Hwang, EenjunTemperature forecasting is an area of ongoing research because of its importance in all life aspects. However, because a variety of climate factors controls the temperature, it is a never-ending challenge. The numerical weather prediction (NWP) model has been frequently used to forecast air temperature. However, because of its deprived grid resolution and lack of parameterizations, it has systematic distortions. In this study, a gray wolf optimizer (GWO) and a support vector machine (SVM) are used to ensure accuracy and stability of the next day forecasting for minimum and maximum air temperatures in Seoul, South Korea, depending on local data assimilation and prediction system (LDAPS; a model of local NWP over Korea). A total of 14 LDAPS models forecast data, the daily maximum and minimum air temperatures of in situ observations, and five auxiliary data were used as input variables. The LDAPS model, the multimodal array (MME), the particle swarm optimizer with support vector machine (SVM-PSO), and the conventional SVM were selected as comparison models in this study to illustrate the advantages of the proposed model. When compared to the particle swarm optimizer and traditional SVM, the Gray Wolf Optimizer produced more accurate results, with the average RMSE value of SVM for T max and T min Forecast prediction reduced by roughly 51 percent when combined with GWO and 31 percent when combined with PSO. In addition, the hybrid model (SVM-GWO) improved the performance of the LDAPS model by lowering the RMSE values for T max Forecast and T min Forecast forecasting from 2.09 to 0.95 and 1.43 to 0.82, respectively. The results show that the proposed hybrid (GWO-SVM) models outperform benchmark models in terms of prediction accuracy and stability and that the suggested model has a lot of application potentials.Öğe Hyperparameter Optimization of Regression Model for Electrical Load Forecasting During the COVID-19 Pandemic Lockdown Period(Intelligent Network and Systems Society, 2023) Al-azzawi, Saif Mohammed; Deif, Mohanad A.; Attar, Hani; Amer, Ayman; Solyman, Ahmed A. A.Due to global lockdown policies implemented against COVID-19, there has been an impact on electricity consumption. Several countries have emphasized the significance of ensuring electricity supply security during the pandemic to maintain the livelihood of people. Accurate forecasting of electricity demand plays a crucial role in ensuring energy security across all nations; accordingly to achieve this objective, this study employs metaheuristics optimization algorithms to enhance the prediction model's operation, such as Support Vector Machine (SVM), KNearest Neighbors (KNN), and Random Forest (RF), at an optimized level to minimize errors. Two metaheuristics optimization methods, Particle Swarm Optimization (PSO) and Genetic Algorithms (GA), are utilized. The suggested prediction models are trained using daily power usage data from three US urban regions. In terms of prediction accuracy, the findings show that KNN with PSO surpasses the other models. The COVID-19 pandemic reduced power usage by 20% relative to pre-pandemic levels. © 2023, International Journal of Intelligent Engineering and Systems. All Rights Reserved.Öğe 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.
