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

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

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

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

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

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

The detected blocks. حسب Xin Li (51274)

Symmetry test result. حسب Jiyong Moon (16819302)

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

"…This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. …"

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

"…This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. …"

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

"…This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. …"

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

"…This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. …"

MERs and their Fourier descriptor figure. حسب Xin Li (51274)

Supportive boundary lines and related results. حسب Xin Li (51274)

Result comparison with other existing models. حسب 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. …"

Dataset distribution. حسب 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. حسب 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. …"

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

"…To address this, we have integrated a pair of productionalized computational models – ‘MiQC’ (Microscopic Quality Control) – into our routine image QC workflows. MiQC combines Local Binary Patterns (LBP) and DeepFocus-based deep learning algorithms to detect and quantify out-of-focus regions in WSIs. …"

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

"…In addition, YOLOv8-FPDW was more competitive than mainstream object detection algorithms. The study predicted the optimal harvest period for litchis, providing scientific support for orchard batch harvesting and fine management.…"

Image_1_Validation of miRNA signatures for ovarian cancer earlier detection in the pre-diagnosis setting using machine learning approaches.pdf حسب 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.…"