Influence of underlying networks on the performance of Graph CNNs trained gene expression data. by Halima Alachram (11570633)

Influence of underlying networks on the performance of Graph CNNs trained gene expression data. by Halima Alachram (11570633)

Influence of underlying networks on the performance of Graph CNNs trained gene expression data. by Halima Alachram (11570633)

Influence of underlying networks on the performance of Graph-CNNs trained gene expression data. by Halima Alachram (11570633)

Design and implementation of the Multiple Criteria Decision Making (MCDM) algorithm for predicting the severity of COVID-19. by Jiaqing Luo (10975030)

“…<p>(A). The MCDM algorithm-Stage 1. Preprocessing, this stage is the process of refining the collected raw data to eliminate noise, including correlation analysis and feature selection based on P values. …”

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. …”

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. …”

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. …”

GSE96058 information. by Sepideh Zununi Vahed (9861298)

“…</p><p>Results</p><p>In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP algorithm. …”

The performance of classifiers. by Sepideh Zununi Vahed (9861298)

“…</p><p>Results</p><p>In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP algorithm. …”

Fairness in Machine Learning: A Review for Statisticians by Xianwen He (22529252)

“…The discussion focuses on fairness in binary classification models using numerical tabular data, which serve as a foundation for addressing fairness in more complex algorithms. …”

Image_1_A predictive model based on random forest for shoulder-hand syndrome.JPEG by Suli Yu (14947807)

“…</p>Results<p>A binary classification model was trained based on 25 handpicked features. …”

Table_1_Identification of sources of DIF using covariates in patient-reported outcome measures: a simulation study comparing two approaches based on Rasch family models.DOCX by Yseulys Dubuy (16809567)

UMAHand: Hand Activity Dataset (Universidad de Málaga) by Eduardo Casilari (3333906)

“…<p dir="ltr">The objective of the UMAHand dataset is to provide a systematic, Internet-accessible benchmarking database for evaluating algorithms for the automatic identification of manual activities. …”

Schematic overview of SINATRA Pro: A novel framework for discovering biophysical signatures that differentiate classes of proteins. by Wai Shing Tang (9541179)

“…Thus, for a study with <i>N</i>-proteins, an <i>N</i> × <i>J</i> design matrix is statistically analyzed using a Gaussian process classification model. <b>(D)</b> Evidence of association measures for each topological feature vector are determined using relative centrality measures. …”

Raw LC-MS/MS and RNA-Seq Mitochondria data by Stefano Martellucci (16284377)

“…<p dir="ltr"><b>LC-MS/MS raw data</b></p><p dir="ltr">Spectrum matching and protein identification and validation were performed with MSFragger, and quantification of protein intensities with matching between runs was performed with IonQuant as components of the FragPipe analysis pipeline using the default settings of each module. …”

Predicting childhood obesity using electronic health records and publicly available data by Robert Hammond (3631525)

“…</p><p>Methods and findings</p><p>We trained a variety of machine learning algorithms to perform both binary classification and regression. …”

Development of a Battery of <i>In Silico</i> Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment by James Rathman (1632322)

“…Both human and mammalian data sets were processed using various learning algorithms, including artificial intelligence approaches. …”

Data for Exploring the Mechanism of Path-Creating Strategy for Latecomers: A Combined Approach of Econometrics and Causal Machine Learning by Yuanyang Teng (19096724)

“…TechCatchup-selected_features.xlsx is further processed data meet the requirements of binary classification and algorithm in the research. …”