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

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Relevant computed image data. حسب Zhaoyu Ku (20660546)

A* Path-Finding Algorithm to Determine Cell Connections حسب Max Weng (22327159)

Summary of relevant works. حسب Bashir Olaniyi Sadiq (22478011)

Algorithm testing. حسب Ziad M. Ali (12516532)

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

Model-relevant variable data for external patients. حسب Lei Gao (6566)

Relevant data underlying the findings described in manuscript. حسب Lin Qi (128224)

Data generation algorithm. حسب Aluwani Ramalata (21262977)

TB screening and diagnostic algorithm. حسب Arangba S (21656068)

Relevant Parameters. حسب Dongyan Wang (195482)

Convergence curves of the investigated algorithms. حسب Ziad M. Ali (12516532)

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Data Sheet 1_Algorithmic esthetics meets human intuition: the synergy of innovation and relevance shapes sustained attention.pdf حسب Wen Guan (5612195)

Comparison of the EODA algorithm with existing algorithms in terms of recall. حسب Sunil Kumar (102321)

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Comparison of the EODA algorithm with existing algorithms in terms of precision. حسب Sunil Kumar (102321)

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Definitions of relevant SSA parameters. حسب Yeping Shi (14279118)

Data Sheet 1_Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.pdf حسب Habtamu Setegn Ngusie (12178327)

"…</p>Result<p>The prevalence of health facility delivery in East Africa was found to be 83.71%. The findings showed that the support vector machine (SVM) algorithm and CatBoost performed best in predicting the place of delivery, in which both of those algorithms scored an accuracy of 95% and an AUC of 0.98 after optimized with Bayesian optimization tuning and insignificant difference between them in all comprehensive analysis of metrics performance. …"

Data Sheet 2_Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.pdf حسب Habtamu Setegn Ngusie (12178327)

"…</p>Result<p>The prevalence of health facility delivery in East Africa was found to be 83.71%. The findings showed that the support vector machine (SVM) algorithm and CatBoost performed best in predicting the place of delivery, in which both of those algorithms scored an accuracy of 95% and an AUC of 0.98 after optimized with Bayesian optimization tuning and insignificant difference between them in all comprehensive analysis of metrics performance. …"

Comparison of the EODA algorithm with existing algorithms in terms of F1-Score. حسب Sunil Kumar (102321)

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Accuracy and average dots covered for difficulty level 11 using gradient descent to find the optimal solution. حسب Tarun Khajuria (12117309)