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. …”

Table_1_Construction of a risk screening and visualization system for pulmonary nodule in physical examination population based on feature self-recognition machine learning model.X... by Fang Tian (360682)

Dendrogram of the stock prices. by Muhammad Hilal Alkhudaydi (21560690)

“…This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”

Descriptive statistics on stock prices. by Muhammad Hilal Alkhudaydi (21560690)

“…This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”

Correlation heatmap of the principal components. by Muhammad Hilal Alkhudaydi (21560690)

“…This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”

Data_Sheet_1_Robust optimization for casualty scheduling considering injury deterioration and point-edge mixed failures in early stage of post-earthquake relief.docx by Yufeng Zhou (3702208)

The robustness test results of the model. by Xini Fang (20861990)

“…Finally, an improved RF model is constructed by optimizing the parameters of the RF algorithm. The data selected is mainly from RESSET/DB, covering the issuance, trading, and rating data of fixed-income products such as bonds, government bonds, and corporate bonds, and provides basic information, net value, position, and performance data of funds. …”

Construction process of RF. by Xini Fang (20861990)

“…Finally, an improved RF model is constructed by optimizing the parameters of the RF algorithm. The data selected is mainly from RESSET/DB, covering the issuance, trading, and rating data of fixed-income products such as bonds, government bonds, and corporate bonds, and provides basic information, net value, position, and performance data of funds. …”

Reverse engineering information flow using stimulus-encoding and stimulus-transfer estimation algorithms. by Gabriel Matías Lorenz (21094672)

All online review text data. by Yuandi Jiang (16540833)

“…Based on the comprehensive perspective of typology and spatio-temporal dynamic evolution, this study not only provides empirical support for museum space optimization, but also provides new ideas and strategies for functional research and methodological insights of public spaces.…”

Data used in this study. by Qinghua Li (398885)

“…To overcome these shortcomings, this paper cites an improved SSA search algorithm that incorporates the ingestion strategy of the FA algorithm to increase the diversity of solutions and global search capability, the Firefly Algorithm-based Sparrow Search Optimization Algorithm (FA-SSA algorithm) is introduced. …”

Distributions of model parameters mirror differentially expressed ion channel genes. by Daniele Linaro (227790)