A framework for improving localisation prediction algorithms. by Sven B. Gould (12237287)

“…Proteins with non-canonical internal motifs, or those dually targeted need to be taken into account (as they help to better distinguish between pNTS and mNTS features) and validated data could be sorted according to whether it is part of a core- or pan-proteome. Classifiers on which the algorithms are trained could include parameters such as the evolutionary distance of a species, non-coding regions, or a protein’s abundance as a currently neglected factor. …”

F1-scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

“…This strategy is integrated into a random forest algorithm by replacing the conventional voting method. …”

Simulation settings of rMAPPO algorithm. by Jianbin Zheng (587000)

“…In response to the multi-agent system of the H-beam riveting and welding work cell, a recurrent multi-agent proximal policy optimization algorithm (rMAPPO) is proposed to address the multi-agent scheduling problem in the H-beam processing. …”

Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large. by Panfei Li (22379086)

“…<p>Objective function values of the algorithms across individual problem instances grouped by scale: (a) Small, (b) Medium, (c) Large, (d) Super-Large.…”

Images of partial benchmark functions. by ZeSheng Lin (20501356)

“…EAWOA demonstrates superior optimization accuracy compared to WOA across 21 test functions, with a notable edge on certain functions. …”

Automated Analysis of Astrocyte Cell Connectivity After Laser Ablation Using Machine Learning and Path-Finding Algorithms by Connor Lee (22327243)

“…The study integrates Cellpose 2.0 for automated segmentation of nuclei and cytoplasm with a heuristic A* path-finding algorithm to classify cellular connectivity. …”

Details of the metaheuristic algorithms. by Yanzhao Gu (21192659)

“…The experimental results showed that GWOA achieved better convergence speed and solution accuracy than other algorithms in most test functions, especially in multimodal and compositional optimization problems, with an Overall Efficiency (OE) value of 74.46%. …”

Function testing and iterative variation diagram. by Yun Qi (560401)

Description of unimodal benchmark functions. by Yi-Qiang Xia (20161326)

“…In essence, it prevents algorithms from settling for suboptimal solutions too soon, encouraging exploration of a broader solution space before converging, by incorporating cauchy variation and a perturbation term, MWOA achieve optimization over a wide search space. …”

Description of multimodal benchmark functions. by Yi-Qiang Xia (20161326)

“…In essence, it prevents algorithms from settling for suboptimal solutions too soon, encouraging exploration of a broader solution space before converging, by incorporating cauchy variation and a perturbation term, MWOA achieve optimization over a wide search space. …”

A Novel Drone Delivery Problem with Stochastic Arrival Time under Uncertain Wind Conditions for Last-mile Delivery Using Iterated Adaptive Tabu Search Algorithm by Yuan Fang (20504030)

A Novel Drone Delivery Problem with Stochastic Arrival Time under Uncertain Wind Conditions for Last-mile Delivery Using Iterated Adaptive Tabu Search Algorithm by Authors (for anonymous review purpose) (6946952)

Steps in the proposed Apriori algorithm. by Yongkang Ding (21807539)

“…<div><p>Physical fitness refers to the health of all body functions, including cardiorespiratory endurance, muscle strength, flexibility, stamina, and body composition, which can help individuals effectively cope with daily activities and sports challenges. …”

IEEE CEC 2022 benchmark function specifications. by Yang Gao (18005)

IEEE CEC 2017 benchmark function specifications. by Yang Gao (18005)

The fitness score results of CEC2020 functions. by Sarah A. Alzakari (19704611)

“…Through rigorous testing on CEC2020 benchmark functions, en-CSA demonstrates robust performance, surpassing other optimization algorithms. …”

Attack and defense behaviors with the sensory–motor algorithm between a single predator and a single prey. by Hiroyuki Ichijo (13891153)

Log-likelihood as a function of the number of selected edges on the chain graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

Log-likelihood as a function of the number of selected edges on the random graph dataset (black dashed line: The true number of edges). by Tomokaze Shiratori (9635271)

Image 4_Construction of a right ventricular function assessment model in patients undergoing invasive mechanical ventilation based on VExUS grading and the classification and regression tree algorithm.pdf by Jun Gao (203098)

“…Objective<p>Investigate the correlation between right ventricular function ultrasound indicators and the Venous Excess Ultrasound (VExUS) grading system in patients undergoing invasive mechanical ventilation (IMV), and develop a right ventricular function assessment model using VExUS grading and the Classification and Regression Tree (CART) algorithm.…”