H.3. Artificial Intelligence
Zainab Hasan; Esmaeel Tahanian
Abstract
Today, telecommunications fraud has emerged as a major challenge for operators, resulting in billions of dollars in financial losses annually. the presence of substantial noise and severe class imbalance between legitimate and fraudulent data complicates the identification of fraud patterns within massive ...
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Today, telecommunications fraud has emerged as a major challenge for operators, resulting in billions of dollars in financial losses annually. the presence of substantial noise and severe class imbalance between legitimate and fraudulent data complicates the identification of fraud patterns within massive volumes of Call Detail Records (CDRs). This paper proposes a hybrid ensemble model, termed Hybrid AdaBoost-RF, for telecommunication fraud detection. In this model, Random Forest is employed as the base learner within the AdaBoost framework to enhance the model's robustness against noise. Furthermore, the SMOTE technique is utilized to address the class imbalance problem. Additionally, we applied a decision threshold tuned on the training predictions to improve the model's sensitivity in detecting fraudulent behavior. Experimental results demonstrate that the proposed model outperforms existing methods in recent research, achieving a Recall of 0.87 and an F1-Score of 0.86 on the test partition of the evaluated CDR dataset using the adopted experimental protocol achieving a Recall of 0.87 and an F1-Score of 0.86. Moreover, the Area Under the Curve (AUC) for ROC and PR metrics reach 0.9777 and 0.8733, respectively, validating the high efficiency of the proposed model.
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.
