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Öğe Diagnosing coronary artery disease on the basis of hard ensemble voting optimization(2022) Mohammedqasim, Hayder; Mohammedqasim, Roa'a; Ata, Oğuz; Alyasin, Eman IbrahimBackground and Objectives: Recently, many studies have focused on the early diagnosis of coronary artery disease (CAD), which is one of the leading causes of cardiac-associated death worldwide. The effectiveness of the most important features influencing disease diagnosis determines the performance of machine learning systems that can allow for timely and accurate treatment. We performed a Hybrid ML framework based on hard ensemble voting optimization (HEVO) to classify patients with CAD using the Z-Alizadeh Sani dataset. All categorical features were converted to numerical forms, the synthetic minority oversampling technique (SMOTE) was employed to overcome imbalanced distribution between two classes in the dataset, and then, recursive feature elimination (RFE) with random forest (RF) was used to obtain the best subset of features. Materials and Methods: After solving the biased distribution in the CAD data set using the SMOTE method and finding the high correlation features that affected the classification of CAD patients. The performance of the proposed model was evaluated using grid search optimization, and the best hyperparameters were identified for developing four applications, namely, RF, AdaBoost, gradient-boosting, and extra trees based on an HEV classifier. Results: Five fold cross-validation experiments with the HEV classifier showed excellent prediction performance results with the 10 best balanced features obtained using SMOTE and feature selection. All evaluation metrics results reached > 98% with the HEV classifier, and the gradient-boosting model was the second best classification model with accuracy = 97% and F1-score = 98%. Conclusions: When compared to modern methods, the proposed method perform well in diagnosing coronary artery disease, and therefore, the proposed method can be used by medical personnel for supplementary therapy for timely, accurate, and efficient identification of CAD cases in suspected patients.Öğe Enhancing self-care prediction in children with impairments: a novel framework for addressing imbalance and high dimensionality(2024) Alyasin, Eman Ibrahim; Ata, Oğuz; Mohammedqasim, Hayder; Mohammedqasem, Roa'aAddressing the challenges in diagnosing and classifying self-care difficulties in exceptional children's healthcare systems is crucial. The conventional diagnostic process, reliant on professional healthcare personnel, is time-consuming and costly. This study introduces an intelligent approach employing expert systems built on artificial intelligence technologies, specifically random forest, decision tree, support vector machine, and bagging classifier. The focus is on binary and multi-label SCADI datasets. To enhance model performance, we implemented resampling and data shuffling methods to tackle data imbalance and generalization issues, respectively. Additionally, a hyper framework feature selection strategy was applied, using mutual-information statistics and random forest recursive feature elimination (RF-RFE) based on a forward elimination method. Prediction performance and feature significance experiments, employing Shapley value explanation (SHAP), demonstrated the effectiveness of the proposed model. The framework achieved a remarkable overall accuracy of 99% for both datasets used with the fewest number of unique features reported in contemporary literature. The use of hyperparameter tuning for RF modeling further contributed to this significant improvement, suggesting its potential utility in diagnosing self-care issues within the medical industry.Öğe Enhancing the diagnosis of liver disease : combining machine learning with the Indian liver patient dataset(Springer Science and Business Media Deutschland GmbH, 2024) Alyasin, Eman Ibrahim; Ata, OğuzThus, this study illustrates a comprehensive examination of machine learning techniques for liver disease diagnosis using the Indian Liver Disease Patients Dataset (ILPD). In view of the critical need to identify liver disorders early and accurately, we used a multimodal machine learning approach involving feature selection, advanced preprocessing, and classifier integration. The use of stacking classifier with ExtraTrees at the meta level, and RF (Random Forest), XGBoost, DT (Decision Tree) and ExtraTrees at the base level is a novelty in our method. When combined with tenfold cross-validation, this technique facilitates extensive evaluation across various data partitions. In contrast to other works that have concentrated on minimizing data imbalances and increasing feature relevance to enhance model prediction accuracies; our work stands out as unique. There was an impressive improvement in accuracy precision and reliability as compared to previous models by our stacking classifier which achieved over 90% accuracy and an AUC score. This demonstration shows why it is necessary to combine several machine learning methods including their application within medical institutions. Also, our study compares itself with the latest researches on similar issues so as to show what has been done differently in our work.Öğe Novel hybrid classification model for multi-class imbalanced lithology dataset(Elsevier GmbH, 2022) Alyasin, Eman Ibrahim; Ata, Oğuz; Mohammedqasim, HayderDeep learning methods and applications assist geologists in predicting and identifying lithologies in different surveys, hence lowering operational costs and uptime. This allows accurate data analysis and completion of scientific research on data obtained at geologically different places. This study used 4 lithologies datasets with high dimensionality and multiclass imbalance problems for analysis and classification. The imbalance of data classification is one of the most important problems facing current data analysis. Data imbalance can considerably influence classification performance, especially when dealing with other difficulty variables such as the presence of overlapping class distributions. This impact is especially obvious in multi-class conditions when mutual imbalance relations across classes complicate matters even further. Moreover, the problem of high dimensionality can lead to increased computing complexity and overfitting, and thus these issues can affect classification performance. To overcome these problems, we developed a new hybrid deep learning multi-class imbalanced learning method that combines Synthetic Minority Oversampling (SMOTE) to resample the data, and Recursive Feature Elimination (RFE) to identify the most useful predictive features. Finally, we believe that our developments can help improve geology research by providing accurate classification and rapid answers about interpreting data obtained in various study areas.Öğe The web applications cross site scripting attacks and preventions using machine learning technique(2024) Alyasin, Eman Ibrahim; Ata, Oğuz; Özturk, Bilal A.Web applications are utilized everywhere these days to share services and data online. Because companies deal with sensitive data, hackers have found them attractive targets. Vulnerabilities persist despite the numerous security procedures we've created to safeguard these applications. Major security issues have been identified in web applications used by various organizations, such as banks, healthcare providers, finance companies, and retail businesses. Cross-site scripting (XSS) attacks are one of the most significant issues, according to a report from White Hat Security. These attacks enable hackers to execute harmful programs on a user's web browser, resulting in issues such as the theft of data, cookies, passwords, and credit card numbers. This study focuses on the primary weaknesses present in contemporary web applications, particularly XSS attacks. We go over the many kinds of XSS attacks, provide instances from the real world, and describe how they operate. We also examine defenses against these attacks, discussing what works and what doesn't.
