DataSheet_1_Patient-Level Effectiveness Prediction Modeling for Glioblastoma Using Classification Trees.docx by Tine Geldof (8380125)

“…Possible improvements in the model’s classification can be achieved by including genetic information and collecting primary data on treatment response. …”

tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

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. Our methodology and model performance were validated using NSL-KDD and UNSW-NB-15, two widely available IDS datasets. …”

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. Our methodology and model performance were validated using NSL-KDD and UNSW-NB-15, two widely available IDS datasets. …”

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. Our methodology and model performance were validated using NSL-KDD and UNSW-NB-15, two widely available IDS datasets. …”

Comparison of DR and FPR of 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. Our methodology and model performance were validated using NSL-KDD and UNSW-NB-15, two widely available IDS datasets. …”

Performance metrics for BrC. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

Proposed CVAE model. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

Proposed methodology. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

Loss vs. Epoch. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

Sample images from the BreakHis dataset. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

Accuracy vs. Epoch. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

Segmentation results of the proposed model. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

S1 Dataset - by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

CSCO’s flowchart. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

Data_Sheet_1_Deep Learning-Based Classification of GAD67-Positive Neurons Without the Immunosignal.pdf by Kotaro Yamashiro (10502753)

Minimal Dateset. by Hongwei Yue (574068)

“…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …”

Loss Function Comparison. by Hongwei Yue (574068)

“…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …”

Comparative Results of Different Models. by Hongwei Yue (574068)

“…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …”

Loss Function Comparison. by Hongwei Yue (574068)

“…Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. …”