Comparison between ILSTM and machine learning-based algorithms in binary classification. حسب Asmaa Ahmed Awad (16726315)

Comparison between ILSTM and deep learning-based algorithms in binary classification. حسب Asmaa Ahmed Awad (16726315)

MSE for ILSTM algorithm in binary classification. حسب Asmaa Ahmed Awad (16726315)

"…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …"

Several loop patterns found in binary code. حسب Alba Pedro-Zapater (8600382)

The operating principle of APDCA. حسب Yangsong He (13909371)

Relative performance of classification algorithms using gene-expression and clinical predictors and performing feature selection. حسب Stephen R. Piccolo (8367780)

"…We used nested cross validation to estimate which features would be optimal for each algorithm in each training set. For each combination of dataset, class variable, and classification algorithm, we calculated the arithmetic mean of area under the receiver operating characteristic curve (AUROC) values across 5 iterations of Monte Carlo cross-validation. …"

Relative predictive performance when training on gene-expression predictors alone vs. using clinical predictors alone or gene-expression predictors in combination with clinical pre... حسب Stephen R. Piccolo (8367780)

Summary of predictive performance per dataset when using gene-expression and clinical predictors and performing hyperparameter optimization. حسب Stephen R. Piccolo (8367780)

"…For classification algorithms that included multiple hyperparameter combinations (n = 47), we performed hyperparameter optimization using the respective training sets. …"

Summary of predictive performance per dataset when using gene-expression predictors. حسب Stephen R. Piccolo (8367780)

Summary of predictive performance per dataset when using gene-expression and clinical predictors. حسب Stephen R. Piccolo (8367780)

Comparison of ranks for classification algorithms across performance metrics. حسب Stephen R. Piccolo (8367780)

Tradeoff between execution time and predictive performance for classification algorithms. حسب Stephen R. Piccolo (8367780)

Summary of predictive performance per dataset when using gene-expression and clinical predictors and performing feature selection. حسب Stephen R. Piccolo (8367780)

Three machine learning algorithms were further employed to select candidate key hub genes. حسب Zhifeng Wu (303016)

"…<p>(A, B) Based on 10-fold cross-validation, the minimum absolute shrinkage and selection operator (LASSO) regression model feature selection identified 11 genes corresponding to the lowest point of the curve, considered optimal biomarkers for studying the shared pathogenic mechanisms of AAA and periodontitis. …"

<i>hi</i>PRS algorithm process flow. حسب Michela C. Massi (14599915)

Table3_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX حسب Zhen Wang (72451)

"…The diagnostic efficiency of the selected biomarkers was evaluated based on gene expression level and receiver operating characteristic (ROC) curve analyses. …"

Table2_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX حسب Zhen Wang (72451)

"…The diagnostic efficiency of the selected biomarkers was evaluated based on gene expression level and receiver operating characteristic (ROC) curve analyses. …"

Table1_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX حسب Zhen Wang (72451)

"…The diagnostic efficiency of the selected biomarkers was evaluated based on gene expression level and receiver operating characteristic (ROC) curve analyses. …"

Convergence without crossover. حسب Dmitrii Smirnov (8822324)

DataSheet1_Comprehensive analysis of immune-related gene signature based on ssGSEA algorithms in the prognosis and immune landscape of hepatocellular carcinoma.ZIP حسب Liangliang Wang (580395)

"…The least absolute shrinkage and selection operator (LASSO) was then employed to screen the optimal genes for the construction of a prognostic predictive signature and to divide patients into high- and low-risk subgroups. …"