Convergence speed of five algorithms. حسب Huichao Guo (14515171)

"…The experimental results show that compared with the traditional particle swarm optimization algorithm, NACFPSO performs well in both convergence speed and scheduling time, with an average convergence speed of 81.17 iterations and an average scheduling time of 200.00 minutes; while the average convergence speed of the particle swarm optimization algorithm is 82.17 iterations and an average scheduling time of 207.49 minutes. …"

Modular architecture design of PyNoetic showing all its constituent functions. حسب Gursimran Singh (575288)

الموضوعات:

A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases حسب Maru Song (22593561)

"…Crucially, we propose fitness functions based on approximate measures of the wave function compactness, which enable inexpensive genetic algorithm searches. …"

Construction of non-invasive diagnostic model and nomogram of AR. حسب Xihao Shen (20347942)

الموضوعات:

Multi-algorithm comparison figure. حسب Dawei Wang (471687)

"…A sophisticated optimization model has been developed to simulate the optimal operation of machinery, aiming to maximize equipment utilization efficiency while addressing the challenges posed by worker fatigue. 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. …"

CEC2017 basic functions. حسب Tengfei Ma (597633)

"…During experimental evaluation, the efficiency of OP-ZOA was verified using the CEC2017 test functions, demonstrating superior performance compared to seven recently proposed meta-heuristic algorithms (Bloodsucking Leech Algorithm (BSLO), Parrot Optimization Algorithm (PO), Polar Lights Algorithm (PLO), Red-tailed Hawk Optimization Algorithm (RTH), Bitterling Fish Optimization Algorithm (BFO), Spider Wasp Optimization Algorithm (SWO) and Zebra Optimization Algorithm (ZOA)). …"

Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. حسب Gursimran Singh (575288)

الموضوعات:

List of Abbreviations حسب Gursimran Singh (575288)

الموضوعات:

The results of ICA performed using PyNoetic. حسب Gursimran Singh (575288)

الموضوعات:

Functional enrichment and correlation analysis of hub DE-SRGs. حسب Xihao Shen (20347942)

الموضوعات:

Construction of weighted co-expression network related datasets and identification of related key modules. حسب Wenlong Kuang (20485167)

الموضوعات:

Both Ankle fNIRS MI dataset حسب Hammad Gilani (8060012)

"…<p><br></p><p dir="ltr">This dataset contains functional near-infrared spectroscopy (fNIRS) signals recorded during motor imagery (MI) tasks of lower limb movements. …"

Co-existence atlas of cell types in mouse kidney. حسب Huamei Li (8815955)

Both Knees fNIRS MI dataset حسب Hammad Gilani (8060012)

"…<p><br></p><p dir="ltr">This dataset contains functional near-infrared spectroscopy (fNIRS) signals recorded during motor imagery (MI) tasks of lower limb movements. …"

Parameters of NSGA-II algorithm. حسب Shangyang Jin (21772466)

"…Thus, a combination algorithm optimization strategy for metamaterials in terms of multiple structural parameters is proposed in this paper based on a co-simulation approach. …"

Development of the CO₂ Adsorption Model on Porous Adsorbent Materials Using Machine Learning Algorithms حسب Hossein Mashhadimoslem (11456548)

"…In this research study, we created a data set and collected data points from porous adsorbents (2789) from 21 published papers, including carbon-based, porous polymers, metal–organic frameworks (MOFs), and zeolites, to understand their characteristics for CO₂ adsorption. Different machine learning (ML) algorithms, such as NN, MLP-GWO, XGBoost, RF, DT, and SVM, have been applied to display the CO₂ adsorption performance as a function of characteristics and adsorption isotherm parameters. …"

Algorithm detection steps. حسب José M. Rivera-Arbeláez (12418512)

Completion times for different algorithms. حسب Jianbin Zheng (587000)

"…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). …"

The average cumulative reward of algorithms. حسب Jianbin Zheng (587000)

"…This paper first analyzes the H-beam processing flow and appropriately simplifies it, develops a reinforcement learning environment for multi-agent scheduling, and applies the rMAPPO algorithm to make scheduling decisions. The effectiveness of the proposed method is then verified on both the physical work cell for riveting and welding and its digital twin platform, and it is compared with other baseline multi-agent reinforcement learning methods (MAPPO, MADDPG, and MASAC). …"

Table 6_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx حسب Anjan Kumar Pradhan (9386369)

"…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …"