Optimization-Driven Steganographic System Based on Fused Maps and Blowfish Encryption by Ahmed Aljughaiman (21793526)

Pseudo-code for encrypting high-dimensional medical data attributes. by Yonggang Huang (223155)

Access control pseudo-code. by Yonggang Huang (223155)

Line charts of the elbow method and the k-value selection algorithm: (A) elbow method; (B) k-value selection algorithm. by Jingnan Yan (20727331)

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Variable selection for microbiome data. by Evan Gorstein (20553314)

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Variable selection for GWAS data. by Evan Gorstein (20553314)

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Proposed genetic algorithm for feature selection. by Shirin Dehghan (19837936)

“…Using GA, Random Forest also demonstrated strong performance, achieving an accuracy rate of 87.4%. Genetic Algorithm significantly improved the performance of all classifiers, emphasizing the importance of feature selection. …”

Variable selection for gene expression data. by Evan Gorstein (20553314)

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Runtime of fitting algorithm (for single λ) on each of three real omics data sets. by Evan Gorstein (20553314)

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Algorithms included in Neuroptimus. by Máté Mohácsi (20469514)

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IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. These results demonstrate the potential and wide applicability of IRBMO in feature selection for medical data.…”

Data Encryption and Compression by Chandra Shekhar Ram (20325126)

“…Advanced compression techniques, including <b>Huffman coding</b> and <b>Lempel-Ziv algorithms</b>, are covered to illustrate their practical applications in modern data systems. …”

Overall rankings of optimization algorithms. by Máté Mohácsi (20469514)

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The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

“…In addition, used in conjunction with the KNN classifier, IRBMO significantly improves the classification accuracy, with an average accuracy improvement of 43.89% on 12 medical datasets compared to the original Red-billed Blue Magpie algorithm. These results demonstrate the potential and wide applicability of IRBMO in feature selection for medical data.…”

Variable selection for GWAS data with <i>q</i> = 1 or 5. by Evan Gorstein (20553314)

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Convergence curves for all algorithms. by Genliang Li (5816264)

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Parameter Settings for competitive algorithms. by Genliang Li (5816264)

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Details of multiple GWO algorithm variants. by Genliang Li (5816264)

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Overall rankings of single-objective optimization algorithms. by Máté Mohácsi (20469514)

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Comparative overview of multi-objective optimization algorithms. by Xiaodan Li (132227)