AUC scores of anomaly detection algorithms. حسب GaoXiang Zhao (21499525)

Recall scores of anomaly detection algorithms. حسب GaoXiang Zhao (21499525)

F1-scores of anomaly detection algorithms. حسب GaoXiang Zhao (21499525)

User satisfaction scores for different algorithms. حسب Qingnan Ji (22662198)

الموضوعات:

Performance of single algorithm in detecting DELs and INSs. حسب De-Min Duan (20685513)

الموضوعات:

The overall performance score comparison between algorithms. حسب Olarewaju Mubashiru Lawal (14710251)

QRS detection in ECG signal using pan-tompkins algorithm with R-peak detection. حسب Rida Nayyab (21519512)

Z-score analysis for outlier detection. حسب Riaz Ud Din (21101290)

Performance stratification of voice-based T2D status detection algorithms. حسب Abir Elbéji (13988706)

Voice-based T2D status detection algorithms’ overall performance. حسب Abir Elbéji (13988706)

Comparison of detection and prediction performance of different algorithm models in static scenes. حسب Shaowei Sun (13392160)

Accuracy score based on different optimization algorithms on the Cc-PhD dataset. حسب Huimin Lu (4434682)

Accuracy score based on different optimization algorithms on the PaHaW dataset. حسب Huimin Lu (4434682)

F1-Score of different algorithms for TMD on SHL dataset. حسب Rui Li (4631)

F1-Score of different algorithms for TMD on HTC dataset. حسب Rui Li (4631)

The comparison of execution time of four algorithms. حسب Zhibo Xie (6790775)

الموضوعات:

The schematic diagram of the iForest algorithm. حسب Zhibo Xie (6790775)

الموضوعات:

AUC score comparison table for all the deep learning algorithms. حسب Khandaker Mohammad Mohi Uddin (13124356)

Comparison with existing work. حسب Khondokar Oliullah (21553289)

"…Furthermore, we introduce a staked ensemble learning approach for intrusion detection that achieves 99.86% accuracy, 99.89% precision, 99.96% recall, and a 99.91% F1-score in detecting anomalous instances, with a support count of 50,376. …"

Registration phase. حسب Khondokar Oliullah (21553289)

"…Furthermore, we introduce a staked ensemble learning approach for intrusion detection that achieves 99.86% accuracy, 99.89% precision, 99.96% recall, and a 99.91% F1-score in detecting anomalous instances, with a support count of 50,376. …"