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

The detected blocks. by Xin Li (51274)

Symmetry test result. by Jiyong Moon (16819302)

Related studies on IDS using deep learning. by 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). …”

The architecture of the BI-LSTM model. by 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). …”

Comparison of accuracy and DR on UNSW-NB15. by 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). …”

Comparison of DR and FPR of UNSW-NB15. by 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). …”

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

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

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

“…The main objective of this research is to harness the noble strategies of artificial intelligence for identifying and classifying lung cancers more precisely from CT scan images at the early stage. 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. by Md. Sabbir Hossain (9958939)

“…The main objective of this research is to harness the noble strategies of artificial intelligence for identifying and classifying lung cancers more precisely from CT scan images at the early stage. 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)

“…The main objective of this research is to harness the noble strategies of artificial intelligence for identifying and classifying lung cancers more precisely from CT scan images at the early stage. 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 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. …”

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)

“…To address these limitations, this study proposed a method for detecting litchi maturity states based on UAV remote sensing and YOLOv8-FPDW. …”

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)

“…By mitigating class imbalance, SMOTE enables robust AMR detection, aiding in early identification of drug-resistant bacteria and informing antibiotic stewardship efforts.…”