Additional file 1 of Cost-Constrained feature selection in binary classification: adaptations for greedy forward selection and genetic algorithms by Rudolf Jagdhuber (8371059)

Correlation matrix of all twelve features. by Toheeb Salahudeen (21368040)

Comparing the performance of One-class classifiers with their respective binary classifier. by Efrat Cohen-Davidi (19485986)

CNN structure for feature extraction. by Md. Sabbir Hossain (9958939)

“…This research offered state-of-the-art results, which achieved remarkable performance metrics with an accuracy, AUC, precision, recall, F1 score, Cohen’s Kappa and Matthews Correlation Coefficient (MCC) of 99.49%, 99.73%, 100%, 99%, 99%, 99.15% and 99.16%, respectively, addressing the prior research gaps and setting a new benchmark in the field. Furthermore, in binary class classification, all the performance indicators attained a perfect score of 100%. …”

Statistical summary of all models. by Amal H. Alharbi (21755906)

Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective by Xiangyang Jia (10026932)

Classification report based on HaPi. by Akib Mohi Ud Din Khanday (19065631)

Result comparison with other existing models. by Md. Sabbir Hossain (9958939)

“…This research offered state-of-the-art results, which achieved remarkable performance metrics with an accuracy, AUC, precision, recall, F1 score, Cohen’s Kappa and Matthews Correlation Coefficient (MCC) of 99.49%, 99.73%, 100%, 99%, 99%, 99.15% and 99.16%, respectively, addressing the prior research gaps and setting a new benchmark in the field. Furthermore, in binary class classification, all the performance indicators attained a perfect score of 100%. …”

Dataset distribution. by Md. Sabbir Hossain (9958939)

“…This research offered state-of-the-art results, which achieved remarkable performance metrics with an accuracy, AUC, precision, recall, F1 score, Cohen’s Kappa and Matthews Correlation Coefficient (MCC) of 99.49%, 99.73%, 100%, 99%, 99%, 99.15% and 99.16%, respectively, addressing the prior research gaps and setting a new benchmark in the field. Furthermore, in binary class classification, all the performance indicators attained a perfect score of 100%. …”

Comparison with previous studies. by Ji Seung Ryu (16187857)

“…This retrospective study used electrocardiographs obtained at Yonsei University Wonju Severance Christian Hospital, Wonju, Korea, from October 2010 to February 2020. Binary classification was performed for primary screening for left ventricular hypertrophy. …”

Dataset characteristics. by Ji Seung Ryu (16187857)

“…This retrospective study used electrocardiographs obtained at Yonsei University Wonju Severance Christian Hospital, Wonju, Korea, from October 2010 to February 2020. Binary classification was performed for primary screening for left ventricular hypertrophy. …”

Acronym table. by Ji Seung Ryu (16187857)

“…This retrospective study used electrocardiographs obtained at Yonsei University Wonju Severance Christian Hospital, Wonju, Korea, from October 2010 to February 2020. Binary classification was performed for primary screening for left ventricular hypertrophy. …”

Classification result on the ModelNet40 dataset. by Lei Wang (6656)

Classification results on the FG3D dataset. by Lei Wang (6656)

Important citation identification by exploiting content and section-wise in-text citation count by Shahzad Nazir (8538129)

“…This research presents a novel approach for binary citation classification by exploiting section-wise in-text citation frequencies, similarity score, and overall citation count-based features. …”

Table_1_An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.pdf by Muhammad Awais (263096)

“…To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. …”

Flow diagram of the proposed model. by Uğur Ejder (22683228)

“…Local Interpretable Model-agnostic Explanations (LIME) were applied to improve interpretability. Across all algorithm models, LR–ABC hybrids outperformed their baseline models (e.g., Random Forest: 85.2% → 91.36% accuracy). …”

Related studies on IDS using deep learning. by Arshad Hashmi (13835488)

“…The attention layer and the BI-LSTM features are concatenated to create mapped features before feeding them to the random forest algorithm for classification. …”

The architecture of the BI-LSTM model. by Arshad Hashmi (13835488)

“…The attention layer and the BI-LSTM features are concatenated to create mapped features before feeding them to the random forest algorithm for classification. …”

Comparison of accuracy and DR on UNSW-NB15. by Arshad Hashmi (13835488)

“…The attention layer and the BI-LSTM features are concatenated to create mapped features before feeding them to the random forest algorithm for classification. …”