Competitive Algorithms for the Online Minimum Peak Appointment Scheduling

2021 ◽  
Author(s):  
Célia Escribe ◽  
Michael Hu ◽  
Retsef Levi
Keyword(s):  
Appointment Scheduling ◽  
Competitive Algorithms

Overbooked and Overlooked: Machine Learning and Racial Bias in Medical Appointment Scheduling

2019 ◽  
Author(s):  
Michele Samorani ◽  
Shannon Harris ◽  
Linda Goler Blount ◽  
Haibing Lu ◽  
Michael A. Santoro
Keyword(s):  
Machine Learning ◽  
Racial Bias ◽  
Appointment Scheduling ◽  
Medical Appointment

Appointment Scheduling with a Quantile Objective

2021 ◽  
pp. 105295
Author(s):  
Peijun Sang ◽  
Mehmet A. Begen ◽  
Jiguo Cao
Keyword(s):  
Appointment Scheduling

An Image Segmentation Method Based on Two-Dimensional Entropy and Chaotic Lightning Attachment Procedure Optimization Algorithm

2020 ◽  
Vol 34 (11) ◽  
pp. 2054030 ◽  
Author(s):  
Wei Liu ◽  
Shuai Yang ◽  
Zhiwei Ye ◽  
Qian Huang ◽  
Yongkun Huang
Keyword(s):  
Image Segmentation ◽  
Maximum Entropy ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Computational Time ◽  
Two Dimensional ◽  
Threshold Segmentation ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Competitive Algorithms

Threshold segmentation has been widely used in recent years due to its simplicity and efficiency. The method of segmenting images by the two-dimensional maximum entropy is a species of the useful technique of threshold segmentation. However, the efficiency and stability of this technique are still not ideal and the traditional search algorithm cannot meet the needs of engineering problems. To mitigate the above problem, swarm intelligent optimization algorithms have been employed in this field for searching the optimal threshold vector. An effective technique of lightning attachment procedure optimization (LAPO) algorithm based on a two-dimensional maximum entropy criterion is offered in this paper, and besides, a chaotic strategy is embedded into LAPO to develop a new algorithm named CLAPO. In order to confirm the benefits of the method proposed in this paper, the other seven kinds of competitive algorithms, such as Ant–lion Optimizer (ALO) and Grasshopper Optimization Algorithm (GOA), are compared. Experiments are conducted on four different kinds of images and the simulation results are presented in several indexes (such as computational time, maximum fitness, average fitness, variance of fitness and other indexes) at different threshold levels for each test image. By scrutinizing the results of the experiment, the superiority of the introduced method is demonstrated, which can meet the needs of image segmentation excellently.

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