The main evaluation indicators of RRT* and RE-RRT* path planning results of environment 3 from Figs 16 and 18. حسب Naeem Ul Islam (19837966)

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The main evaluation indicators of RRT, RRT* and RE-RRT* path planning results of environment 1. حسب Naeem Ul Islam (19837966)

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The result of RE-RRT* for experiment 1. حسب Naeem Ul Islam (19837966)

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The 1st result of RE-RRT* for experiment 3. حسب Naeem Ul Islam (19837966)

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The result of RRT* for experiment 2. حسب Naeem Ul Islam (19837966)

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Vehicle kinematics model. حسب Naeem Ul Islam (19837966)

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The hyperparameters setting of RE-RRT*. حسب Naeem Ul Islam (19837966)

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Schematic diagram for selecting the nearest node. حسب Naeem Ul Islam (19837966)

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The main evaluation indicators of RRT* and RE-RRT* path planning results of environment 2. حسب Naeem Ul Islam (19837966)

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The main evaluation indicators of RRT* and RE-RRT* path planning results of environment 4. حسب Naeem Ul Islam (19837966)

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The result of RRT* for experiment 1. حسب Naeem Ul Islam (19837966)

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The result of RRT* for experiment 4. حسب Naeem Ul Islam (19837966)

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The 2nd result of RRT* for experiment 3. حسب Naeem Ul Islam (19837966)

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Exploring the achievements and forecasting of SDG 3 using machine learning algorithms: Bangladesh perspective. حسب Md. Maeen Molla (20827041)

Overall performance indicators of the algorithm model in this article for dynamic detection of traffic anomalies on highways. حسب Shaowei Sun (13392160)

Transient Simulation Performance at 800<i>W</i>/<i>m</i>² and 400<i>W</i>/<i>m</i>² (a) Conventional P&O Algorithm, (b) Proposed SF Algorithm. حسب Rana Ahmed (4923127)

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Table 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf حسب Valentina Di Salvatore (6296114)

"…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …"

Presentation 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf حسب Valentina Di Salvatore (6296114)

"…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …"

Table 2_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.pdf حسب Valentina Di Salvatore (6296114)

"…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …"

Image 1_A computational framework for optimizing mRNA vaccine delivery via AI-guided nanoparticle design and in silico gene expression profiling.png حسب Valentina Di Salvatore (6296114)

"…Parallelly, we trained a Random Forest regression model on simulated lipid nanoparticles formulations to predict immune activation values and embedded this model into a genetic algorithm to identify optimal lipid nanoparticles design parameters (size, charge, polyethylene glycol content, and targeting). …"