Comparison of different algorithms. حسب Dawei Wang (471687)

"…An innovative algorithm, the improved hybrid gray wolf and whale algorithm fused with a penalty function for construction machinery optimization (IHWGWO), is introduced, incorporating a penalty function to handle constraints effectively. …"

Python code for a rule-based NLP model for mapping circular economy indicators to SDGs حسب Zahir Barahmand (18008947)

"…The package includes:</p><ul><li>The complete Python codebase implementing the classification algorithm</li><li>A detailed manual outlining model features, requirements, and usage instructions</li><li>Sample input CSV files and corresponding processed output files to demonstrate functionality</li><li>Keyword dictionaries for all 17 SDGs, distinguishing strong and weak matches</li></ul><p dir="ltr">These materials enable full reproducibility of the study, facilitate adaptation for related research, and offer transparency in the methodological framework.…"

Pseudo-code of DMDDPG algorithm. حسب Guohua Cao (697580)

"…Next, a reward function is designed by integrating the decoupled multi-agent deterministic deep deterministic policy gradient (DMDDPG) algorithm. …"

Convergence graphs of CEC2021 test functions. حسب Yu Liu (6938)

"…<div><p>The competition of tribes and cooperation of members algorithm (CTCM) is a novel swarm intelligence algorithm, which increases the diversity of the population to a certain extent through tribal competition and member cooperation mechanisms. …"

Depiction of how the label of is assigned as a function of whether are identical (row =), nested (rows ), or partially overlapping (row “else”), and as a function of the labels of... حسب Jordan Moutet (21800145)

Genetic algorithm flowchart. حسب Junjie He (700642)

"…Additionally, sensitivity analysis was conducted to explore the effect of different parameters on the maximum fire risk value that can be covered by a fire station. A compromise coordinated siting scheme with a hierarchy of fire stations can be obtained by solving the proposed model. …"

Algorithm Parameter Setting. حسب Tong Zhou (3770)

"…Therefore, an Improved Dueling Double DQN (D3QN) On-ramp Merging Strategy (IDS stands for the initials of Improved, D3QN, and Strategy) combined with a sine function is proposed, establishing a Vehicle Coordination System (VCS) to guide the merging of vehicles in multi-lane mainline traffic. …"

Code snippets to add user-specific custom functionality in PyNoetic. حسب Gursimran Singh (575288)

الموضوعات:

IEEE CEC 2017 benchmark function specifications. حسب Kankan Tang (21474968)

IEEE CEC 2022 benchmark function specifications. حسب Kankan Tang (21474968)

Biological Function Assignment across Taxonomic Levels in Mass-Spectrometry-Based Metaproteomics via a Modified Expectation Maximization Algorithm حسب Gelio Alves (51850)

"…To overcome this limitation, we implemented an expectation-maximization (EM) algorithm, along with a biological function database, within the MiCId workflow. …"

Experiment 1b: Evaluating modularity-based consensus clustering pipelines on the algorithm design datasets as a function of the mixing parameter. حسب Yasamin Tabatabaee (18095491)

Iteration curve of each algorithm: (a) Convergence curves of the average best fitness for functions F1-F10, (b) Convergence curves of the average best fitness for functions F11-F20 and (c) Correspondence between curve colors and algorithms. حسب Ruyi Dong (9038174)

"…<p>Iteration curve of each algorithm: (a) Convergence curves of the average best fitness for functions F1-F10, (b) Convergence curves of the average best fitness for functions F11-F20 and (c) Correspondence between curve colors and algorithms.…"

The benchmark functions. حسب Shuhui Yu (1535500)

Benchmark functions. حسب Yun Qi (560401)

Study proposed algorithm. حسب Ainhoa Pérez-Guerrero (21377457)

"…The index of microvascular resistance (IMR) is a specific physiological parameter used to assess microvascular function. …"

The training process for PV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. حسب Hiam Alquran (17636906)

The training process for AV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. حسب Hiam Alquran (17636906)

The training process for TV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. حسب Hiam Alquran (17636906)

The training process for MV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. حسب Hiam Alquran (17636906)