The relevant code in the manuscript can be found in the supporting information data file. حسب Xini Fang (20861990)

الموضوعات:

Structure of the Kuhn-Munkres Algorithm. حسب Qingnan Ji (22662198)

الموضوعات:

User satisfaction scores for different algorithms. حسب Qingnan Ji (22662198)

الموضوعات:

Performance of different machine learning algorithms. حسب Apichat Suratanee (634281)

Illustration of the Noisy Student algorithm proposed by [30]. حسب Martin Wutke (19786999)

Results of RF algorithm screening factors. حسب Jintao Li (448681)

Flowchart of the proposed Margin Weighted Robust Discriminant Score (MW-RDS) algorithm. حسب Sheema Gul (21519289)

Data process. حسب Maeva El Bouchikhi (20442436)

"…They highlighted an important number of ethical issues which could be grouped into five overarching categories: goal relevance, adverse side effects, role of employees, data process, and vagueness. …"

The relevant data file for this article. حسب Linsha Liu (20378293)

LuGre parameter identification convergence process. حسب Wanjun Zhang (496858)

الموضوعات:

Algorithm process. حسب Wei Cui (92129)

Data (3). حسب Qingnan Ji (22662198)

الموضوعات:

The Search process of the genetic algorithm. حسب Wenguang Li (6528113)

الموضوعات:

The construction process of the FCM clustering algorithm. حسب Xini Fang (20861990)

الموضوعات:

Hair removal preprocessing using the DullRazor algorithm before image generation. حسب Mujung Kim (22367223)

Flowchart diagram illustrating the data analysis process using machine learning models. حسب Isabela Bittencourt Basso (14178739)

الموضوعات:

Relevant computed image data. حسب Zhaoyu Ku (20660546)

Processed results from ECG data with the P-T algorithm. حسب Daehyun Kwon (11985629)

Goal relevance. حسب Maeva El Bouchikhi (20442436)

"…They highlighted an important number of ethical issues which could be grouped into five overarching categories: goal relevance, adverse side effects, role of employees, data process, and vagueness. …"

Data labeling. حسب Saad Hammood Mohammed (20623506)

"…Future research should focus on integrating real-world smart grid data for validation, developing adaptive learning mechanisms, exploring other bio-inspired optimization algorithms, and addressing real-time processing and scalability challenges in large-scale deployments.…"