MCL-FWA-BILSTM binary and multiclass accuracy comparison. by Arshad Hashmi (13835488)

Binary classification accuracy and DR comparison on NSL-KDD. by Arshad Hashmi (13835488)

Comparison of accuracy of MCL-FWA-BILSTM model with existing models for binary classification on UNSW-NB15 and NSL-KDD. by Arshad Hashmi (13835488)

Accuracy comparison with existing approaches for Binary Classification with state of art on UNSW-NB15 and NSL-KDD. by Arshad Hashmi (13835488)

Accuracy comparison with existing approaches for binary classification with state of art on UNSW-NB15 and NSL-KDD. by Arshad Hashmi (13835488)

The main workflow of the proposed algorithm. by Xin Li (51274)

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

“…The model’s binary and multi-class classification accuracies on the UNSW-NB15 dataset are 99.56% and 99.45%, respectively. …”

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

“…This study introduces a novel lung cancer detection method, which was mainly focused on Convolutional Neural Networks (CNN) and was later customized for binary and multiclass classification utilizing a publicly available dataset of chest CT scan images of lung cancer. …”

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

“…The model’s binary and multi-class classification accuracies on the UNSW-NB15 dataset are 99.56% and 99.45%, respectively. …”

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

“…The model’s binary and multi-class classification accuracies on the UNSW-NB15 dataset are 99.56% and 99.45%, respectively. …”

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

“…The model’s binary and multi-class classification accuracies on the UNSW-NB15 dataset are 99.56% and 99.45%, respectively. …”

Dataset distribution. by Md. Sabbir Hossain (9958939)

“…This study introduces a novel lung cancer detection method, which was mainly focused on Convolutional Neural Networks (CNN) and was later customized for binary and multiclass classification utilizing a publicly available dataset of chest CT scan images of lung cancer. …”

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

“…This study introduces a novel lung cancer detection method, which was mainly focused on Convolutional Neural Networks (CNN) and was later customized for binary and multiclass classification utilizing a publicly available dataset of chest CT scan images of lung cancer. …”

Data Sheet 1_Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model.docx by Changjiang Liang (21099887)

“…The improved model demonstrated robust performance in different application scenarios. …”

Enhancing digital pathology workflows: computational blur detection for H&E image quality control in preclinical toxicology by Cyrus Manuel (22770779)

“…MiQC combines Local Binary Patterns (LBP) and DeepFocus-based deep learning algorithms to detect and quantify out-of-focus regions in WSIs. …”

Image_1_Validation of miRNA signatures for ovarian cancer earlier detection in the pre-diagnosis setting using machine learning approaches.pdf by Konrad Stawiski (4753380)

“…We employed the extreme gradient boosting (XGBoost) algorithm to train a binary classification model using 70% of the available data, while the model was tested on the remaining 30% of the dataset.…”

Processed dataset to train and test the WGAN-GP_IMOA_DA_Ensemble model by Ramya Chinnasamy (21633527)

“…<p dir="ltr">In the dynamic landscape of cybersecurity, robust and efficient Intrusion Detection Systems (IDS) are essential. …”

Supplementary Material 8 by Nishitha R Kumar (19750617)

“…It is commonly used with classifiers like decision trees, support vector machines (SVM), and deep learning models to improve predictive accuracy. By mitigating class imbalance, SMOTE enables robust AMR detection, aiding in early identification of drug-resistant bacteria and informing antibiotic stewardship efforts.…”