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)

“…The extracted features were then processed by a set of ML algorithms along with a voting ensemble approach. …”

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

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

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

“…The extracted features were then processed by a set of ML algorithms along with a voting ensemble approach. …”

Dataset distribution. by Md. Sabbir Hossain (9958939)

“…The extracted features were then processed by a set of ML algorithms along with a voting ensemble approach. …”

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

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

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

“…However, the coincidence rate of the actual left ventricular hypertrophy and diagnostic findings was low, consequently increasing the interest in algorithms using big data and deep learning. We attempted to diagnose left ventricular hypertrophy using big data and deep learning algorithms, and aimed to confirm its diagnostic power according to the differences between males and females. …”

Dataset characteristics. by Ji Seung Ryu (16187857)

“…However, the coincidence rate of the actual left ventricular hypertrophy and diagnostic findings was low, consequently increasing the interest in algorithms using big data and deep learning. We attempted to diagnose left ventricular hypertrophy using big data and deep learning algorithms, and aimed to confirm its diagnostic power according to the differences between males and females. …”

Acronym table. by Ji Seung Ryu (16187857)

“…However, the coincidence rate of the actual left ventricular hypertrophy and diagnostic findings was low, consequently increasing the interest in algorithms using big data and deep learning. We attempted to diagnose left ventricular hypertrophy using big data and deep learning algorithms, and aimed to confirm its diagnostic power according to the differences between males and females. …”

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

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

“…The study also introduces machine learning algorithms based novel approach for assigning appropriate weights to the logical sections of research papers. …”

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. …”

Smaller gene set of DFC was selected by a unique selection criterion. by Takeru Fujii (11731550)

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. …”