Left: the algorithm’s best estimate of where to put breakpoints for each number of breakpoints considered. حسب Zhengtian Li (5076704)

Comparison of different algorithms. 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. …"

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)

Morpheus: A fragment-based algorithm to predict metamorphic behaviour in proteins across proteomes حسب Vijay Subramanian (20718933)

"…<p dir="ltr">Supplementary material for Morpheus: A fragment-based algorithm to predict metamorphic behaviour in proteins across proteomes.…"

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

الموضوعات:

The benchmark functions. حسب Shuhui Yu (1535500)

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

"…The index of microvascular resistance (IMR) is a specific physiological parameter used to assess microvascular function. Invasive coronary assessment has been shown to be both feasible and safe. …"

Gillespie algorithm simulation parameters. حسب Nicholas H. Vitale (20469289)

"…Both the ensemble and stochastic models presented in this work have been verified using Monte Carlo molecular dynamic simulations that utilize the Gillespie algorithm. …"

Scheduling time 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. …"

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. …"

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach حسب Sadaf Aqil (22183571)

"…As a case study, a curated dataset of phytocystatin sequences from the UniProt database was used to evaluate the algorithm’s performance. The PhyCysID web server enables rapid classification of both individual and batch-submitted sequences in less than 15 s, providing high-throughput analysis for an accurate identification of phytocystatin class and function. …"

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach حسب Sadaf Aqil (22183571)

"…As a case study, a curated dataset of phytocystatin sequences from the UniProt database was used to evaluate the algorithm’s performance. The PhyCysID web server enables rapid classification of both individual and batch-submitted sequences in less than 15 s, providing high-throughput analysis for an accurate identification of phytocystatin class and function. …"

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach حسب Sadaf Aqil (22183571)

"…As a case study, a curated dataset of phytocystatin sequences from the UniProt database was used to evaluate the algorithm’s performance. The PhyCysID web server enables rapid classification of both individual and batch-submitted sequences in less than 15 s, providing high-throughput analysis for an accurate identification of phytocystatin class and function. …"

PhyCysID: Plant Cystatin Protein Prediction by an Artificial Intelligence Approach حسب Sadaf Aqil (22183571)

"…As a case study, a curated dataset of phytocystatin sequences from the UniProt database was used to evaluate the algorithm’s performance. The PhyCysID web server enables rapid classification of both individual and batch-submitted sequences in less than 15 s, providing high-throughput analysis for an accurate identification of phytocystatin class and function. …"

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. …"

PATH has state-of-the-art performance versus previous binding affinity prediction algorithms. حسب Yuxi Long (11024307)

الموضوعات:

Critical proteins associated with SCA1 pathology. حسب Aimilia-Christina Vagiona (17584758)

الموضوعات:

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)). …"