Heuristic approaches for configuring network alignment algorithms. حسب Giuseppe Agapito (104634)

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Accuracy of different electrical and optical neural networks for various input noise levels. حسب Reyhane Ahmadi (14857363)

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Comparison of preprocessing methods for network alignment. حسب Giuseppe Agapito (104634)

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Key aspects of network alignment documentation. حسب Giuseppe Agapito (104634)

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Comparison of local and global network alignment approaches. حسب Giuseppe Agapito (104634)

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Representative tools using heuristic strategies for network alignment. حسب Giuseppe Agapito (104634)

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Comparison of network representation formats. حسب Giuseppe Agapito (104634)

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Main code. حسب Jingguo Bi (21439766)

Network topologies and suitable representation formats. حسب Giuseppe Agapito (104634)

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Recommended network representation formats for different types of biological networks. Each biological network type exhibits unique topological characteristics that make certain re... حسب Giuseppe Agapito (104634)

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Flowchart of the algorithm. حسب Xingan Fu (21594713)

Pseudo-code of large neighborhood search (<i>LNS</i>) algorithm. حسب Jiren CAO (20442214)

UpSet diagram illustrating the overlap of DNBs selected by our method and top-ranked genes by the 13 baseline centrality-based ranking algorithms. حسب Fatemeh Keikha (20917848)

Chromosome coding. حسب Junjie He (700642)

Open-source code for the Spatial Optimization Model for Land Use (SSFLA-MLAS) حسب bowen pang (21837359)

"…<p dir="ltr">The open-source content is the source code of a land use spatial optimization model (SSFLA-MLAS) that couples the spatial leapfrog algorithm with multi-level multi-agent systems. …"

The ranking of algorithms with MPTM mutation by TOPSIS. حسب Jalal- ud-Din (21474307)

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The ranking of algorithms with NUM mutation by TOPSIS. حسب Jalal- ud-Din (21474307)

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The ranking of algorithms with PM mutation by TOPSIS. حسب Jalal- ud-Din (21474307)

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CNN model evaluation. حسب Azhar Imran (17720751)

"…The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. …"

ROC curve CNN. حسب Azhar Imran (17720751)

"…The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. …"