Effective cluster radius from Ripley’s H-function for the NPC dataset. by Daniel F. Nino (9745709)

ROC analysis of the Machine Learning Algorithm. by Stephan Ellmann (314565)

Subjects:

WGCNA and machine learning algorithms. by Shushu Wang (6870191)

Meta-analysis of EBV GWAS of NPC. by Ka Wo Wong (18625847)

Subjects:

Rule-in and rule-out criteria provided by the machine learning algorithm. by Stephan Ellmann (314565)

Subjects:

AIF and VOF derived features used to feed the machine learning algorithms. by Ezequiel de la Rosa (11361108)

Machine learning algorithm parameters. by Namal Rathnayake (15361124)

Model selection for machine learning algorithm based on microbial abundances. by Elio L. Herzog (11576876)

Subjects: “…applying independent algorithms…”

Activation functions: Sigmoid function and ReLU function. by Wan-Hua Zhang (21601139)

Great machine learning performance with multiple classification algorithms. by Sydney C. Weiser (15383531)

Performance metrics of 28 and 9 SNPs in predicting NPC. by Ka Wo Wong (18625847)

Subjects:

Algorithms for Sparse Support Vector Machines by Alfonso Landeros (798926)

“…We derive two closely related algorithms to carry out this strategy. Our simulated and real examples vividly demonstrate how the algorithms achieve better sparsity without loss of classification power. …”

Flowchart of the proposed algorithm. by Heewon Chung (2881286)

Subjects:

Construction of the PRG score index using integrated machine learning algorithms. by Xiangxin Wu (22320058)

Subjects:

Calculation results of commonly used machine learning algorithm models. by Qianhui He (13522729)

Automated diagnosis of heart valve degradation using novelty detection algorithms and machine learning by Bernhard Vennemann (7434353)

The Effect of Machine Learning Algorithms on the Prediction of Layer-by-Layer Coating Properties by Tijana Šušteršič (14519970)

Subjects:

EAT-Rice: A predictive model for flanking gene expression of T-DNA insertion activation-tagged rice mutants by machine learning approaches by Chi-Chou Liao (6680852)

“…The three kinds of DNA sequences including UPS1K, DISTANCE, and MIDDLE were retrieved to encode and build a forecast model of two-layer machine learning. In the first-layer models, the features nucleotide context (N-gram), cis-regulatory elements (Motif), nucleotide physicochemical properties (NPC), and CG-island (CGI) were used to build SVM models by analysing the concealed information embedded within the three kinds of sequences. …”

Concept of the Loss function in the case of Supervised Machine Learning and Physics Machine Learning approach. by Akshansh Mishra (21001499)

The structure of the proposed algorithm. by Mingbao Yang (14008799)