Accuracy of different algorithms in binary classification on Alibaba Cloud data set. by Zhanhui Hu (18405183)

The workflow of SCARS-Logistic algorithm. by Maryam Sadiq (7046903)

An illustrated example of Algorithm 1 (Fig 1). by Kathleen M. Jagodnik (10170617)

The developed algorithm to calculate Gene-Disease Prioritization Score (GDPS). by Kathleen M. Jagodnik (10170617)

DataSheet2_Disordered–Ordered Protein Binary Classification by Circular Dichroism Spectroscopy.PDF by András Micsonai (801004)

“…Here, we propose an automatized binary disorder–order classification method by analyzing far-UV CD spectroscopy data. …”

DataSheet3_Disordered–Ordered Protein Binary Classification by Circular Dichroism Spectroscopy.xlsx by András Micsonai (801004)

“…Here, we propose an automatized binary disorder–order classification method by analyzing far-UV CD spectroscopy data. …”

DataSheet1_Disordered–Ordered Protein Binary Classification by Circular Dichroism Spectroscopy.PDF by András Micsonai (801004)

“…Here, we propose an automatized binary disorder–order classification method by analyzing far-UV CD spectroscopy data. …”

Summary of the AdaBoost classification for the two exercises SDT and SLST. by Jindřich Adolf (17334216)

Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)

Flow diagram of the proposed model. by Uğur Ejder (22683228)

“…Clinical, demographic, and supplement variables were preprocessed into 21 predictors. Four algorithms (K-Nearest Neighbors, Classification and Regression Tree, Support Vector Machine, and Random Forest) were implemented alongside their LR–ABC hybrid counterparts. …”

Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective by Xiangyang Jia (10026932)

Supplementary file 1_Comparative evaluation of fast-learning classification algorithms for urban forest tree species identification using EO-1 hyperion hyperspectral imagery.docx by Veera Narayana Balabathina (22518524)

“…</p>Methods<p>Thirteen supervised classification algorithms were comparatively evaluated, encompassing traditional spectral/statistical classifiers—Maximum Likelihood, Mahalanobis Distance, Minimum Distance, Parallelepiped, Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Binary Encoding—and machine learning algorithms including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN). …”

Smaller gene set of DFC was selected by a unique selection criterion. by Takeru Fujii (11731550)

DataSheet_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

“…Two NIRs devices, the portable QualitySpec® Trek (QST) and the benchtop NIRFlex N-500 were used to collect spectral data. Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. …”

Calibration curves for NLP classification. by Charlene Jennifer Ong (8997278)

“…These curves represent different combinations of text featurization (BOW, tf-idf, GloVe) and binary classification algorithms (Logistic Regression, RF, RNN). …”

DataSheet_1_Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.docx by Jun Zhang (48506)

UNet++ type architecture [68] used for all the problems in this paper. by Dylan D. Ray (3741823)

Table_1_Near infrared spectroscopy for cooking time classification of cassava genotypes.docx by Massaine Bandeira e Sousa (7866242)

“…Two NIRs devices, the portable QualitySpec® Trek (QST) and the benchtop NIRFlex N-500 were used to collect spectral data. Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. …”

Table_1_Prediction of spherical equivalent difference before and after cycloplegia in school-age children with machine learning algorithms.DOCX by Bei Du (12545461)

“…Visual acuity, optical biometry, intraocular pressure, accommodation lag, gaze deviation in primary position, non-cycloplegic and cycloplegic autorefraction were conducted. A binary classification model and a three-way classification model were established to predict the necessity of cycloplegia and the refractive status, respectively. …”

Pan-cancer machine learning predictions of MEKi response. by John P. Lloyd (10196288)