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

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

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

“…In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. …”

Binary encoding scheme. by Binh Thanh Dang (12368831)

Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple... by Yangyang Liu (807797)

DataSheet1_FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm.xlsx by Wenwu Xu (2023315)

DataSheet2_FMixFN: A Fast Big Data-Oriented Genomic Selection Model Based on an Iterative Conditional Expectation algorithm.doc by Wenwu Xu (2023315)

Relative performance of classification algorithms using gene-expression and clinical predictors and performing feature selection. by 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. …”

Convergence comparison of APDCA, SIMCLDA, MLFDA, and SDLDA over 100 iterations using relative error as the evaluation metric. by Yangsong He (13909371)

Summary of steps in iterative point set registration for scRNA-seq data. by Amir Alavi (9569337)

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

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

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

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

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

Illustration of the BRIDES algorithm and the selection process. by Cindy Bouchard (6844979)

Summary of LITNET-2020 dataset. by Asmaa Ahmed Awad (16726315)

“…In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. …”

SHAP analysis for LITNET-2020 dataset. by Asmaa Ahmed Awad (16726315)

“…In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. …”

Comparison of intrusion detection systems. by Asmaa Ahmed Awad (16726315)

“…In this paper, a novel, and improved version of the Long Short-Term Memory (ILSTM) algorithm was proposed. The ILSTM is based on the novel integration of the chaotic butterfly optimization algorithm (CBOA) and particle swarm optimization (PSO) to improve the accuracy of the LSTM algorithm. …”