The flowchart showing the optimization process of the hybrid methods using BEOSA as the base algorithm and SA and FFA as integrated algorithms. حسب Olaide N. Oyelade (14047002)

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S1 Dataset - Novel chaotic oppositional fruit fly optimization algorithm for feature selection applied on COVID 19 patients’ health prediction حسب Nebojsa Bacanin (13932723)

The flowchart of the proposed algorithm. حسب Muhammad Ayyaz Sheikh (18610943)

"…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …"

Hierarchical clustering to infer a binary tree with <i>K</i> = 4 sampled populations. حسب Tristan Mary-Huard (3864)

An optimization process of the proposed hybrid BEOSA (HBEOSA) combining both SA and FFA methods into BEOSA. حسب Olaide N. Oyelade (14047002)

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Data generation process. حسب Nicola Dietler (12551766)

Illustration of the workflow combining BayesFlow with missing data encoding. حسب Zijian Wang (481291)

The overview of the proposed method. حسب Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Top mRNAs based on repeat count in early-stage and late-stage rules. حسب Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Fig 4 - حسب Seyed Mahdi Hosseiniyan Khatibi (16791475)

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The performance of the SVM classifier based on 60 (30 plus 30-zeroed) selected mRNAs for GSE 60542 dataset. حسب Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Fig 3 - حسب Seyed Mahdi Hosseiniyan Khatibi (16791475)

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List of selected mRNAs in the feature selection step. حسب Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Twenty of the top early-stage and late-stage rules based on sorted lift value. حسب Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Graph network of ZNF518B (with lift score > 1.23) and its related association rules with other mRNAs. ZNF518B, its rules, and related mRNAs are represented by orange, yellow, and b... حسب Seyed Mahdi Hosseiniyan Khatibi (16791475)

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The performance of classifiers based on 60 selected mRNAs. حسب Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Pseudocode of NSGAII [23]. حسب Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Selected mRNAs based on association rule mining analysis, for early and late stages of PTC. حسب Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Fig 2 - حسب Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Summary of literature review. حسب Muhammad Ayyaz Sheikh (18610943)

"…To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. …"