I.3.7. Engineering
Elahe Moradi
Abstract
Fault prediction in power transformers is pivotal for safeguarding operational reliability and reducing system disruptions. Leveraging dissolved gas analysis (DGA) data, AI‑driven techniques have recently been employed to enhance predictive performance. This paper introduces a novel machine-learning ...
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Fault prediction in power transformers is pivotal for safeguarding operational reliability and reducing system disruptions. Leveraging dissolved gas analysis (DGA) data, AI‑driven techniques have recently been employed to enhance predictive performance. This paper introduces a novel machine-learning framework that integrates Hist Gradient Boosting (HGB) with a metaheuristic Particle Swarm Optimization (PSO) algorithm for hyperparameter tuning, thereby guaranteeing classifier robustness. The proposed method underwent a two‑stage evaluation: first, Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and HGB were benchmarked, revealing HGB as the most effective method; second, PSO was applied to optimize HGB’s hyperparameters, yielding further performance improvements. Experimental results demonstrate that the hybrid HGB‑PSO model achieves an accuracy of 97.85 %, precision of 98.90 %, recall of 97.33 %, and an F1‑score of 98.99 %. All simulations and comparative analyses against state‑of‑the‑art methods were implemented in Python, and confusion‑matrix analysis was employed to assess predictive performance comprehensively. These findings demonstrate that the hybrid HGB‑PSO method achieves superior accuracy and robustness in transformer fault prediction.
H.3. Artificial Intelligence
Elahe Moradi
Abstract
Liver disorders are among the most common diseases worldwide, and their timely diagnosis and prediction can significantly improve treatment outcomes. In recent years, the application of artificial intelligence, particularly machine learning and deep learning algorithms, in the medical field has gained ...
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Liver disorders are among the most common diseases worldwide, and their timely diagnosis and prediction can significantly improve treatment outcomes. In recent years, the application of artificial intelligence, particularly machine learning and deep learning algorithms, in the medical field has gained tremendous importance and has led to reduced healthcare costs. In this study, the ILPD dataset from the UCI Machine Learning Repository, which comprises 583 liver patient records with 11 features, was utilized. In this research, a predictive framework based on Multilayer Perceptron (MLP) is employed for the prediction of liver disorders. To address the class imbalance in the binary classification dataset, the Synthetic Minority Oversampling Technique (SMOTE)–Tomek approach was implemented to improve data balance. Moreover, due to the presence of a substantial number of outlier values, a robust scaling method was applied for their management. Finally, the performance of the proposed method was compared with three well-known machine learning algorithms. To enhance evaluation robustness, a five-fold cross-validation was employed across all classifiers. All simulations were conducted using Python, and the results illustrate that the proposed method achieves superior performance, with an accuracy of 90.90% compared to state-of-the-art approaches.
I.3.7. Engineering
Elahe Moradi
Abstract
Thyroid disease is common worldwide and early diagnosis plays an important role in effective treatment and management. Utilizing machine learning techniques is vital in thyroid disease diagnosis. This research proposes tree-based machine learning algorithms using hyperparameter optimization techniques ...
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Thyroid disease is common worldwide and early diagnosis plays an important role in effective treatment and management. Utilizing machine learning techniques is vital in thyroid disease diagnosis. This research proposes tree-based machine learning algorithms using hyperparameter optimization techniques to predict thyroid disease. The thyroid disease dataset from the UCI Repository is benchmarked to evaluate the performance of the proposed algorithms. After data preprocessing and normalization steps, data balancing has been applied to the data using the random oversampling (ROS) technique. Also, two methods of grid search (GS) and random search (RS) have been employed to optimize hyperparameters. Finally, employing Python software, various criteria were used to evaluate the performance of proposed algorithms such as decision tree, random forest, AdaBoost, and extreme gradient boosting. The results of the simulations indicate that the Extreme Gradient Boosting (XGB) algorithm with the grid search method outperforms all the other algorithms, obtaining an impressive accuracy, AUC, sensitivity, precision, and MCC of 99.39%, 99.97%, 98.85%, 99.40%, 98.79%, respectively. These results demonstrated the potential of the proposed method for accurately predicting thyroid disease.
