Classical Predictive Coding for motion processing. by Elnaz Nemati (21402730)

Subjects:

Effects of noise level and surround suppression threshold on model convergence time in the RDK task, experiment III. by Elnaz Nemati (21402730)

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The GOR4 and SOPMA algorithm findings. by Ahmad Abdullah Mahdeen (19704401)

XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm by Pankaj Tyagi (20147683)

Previously literatures on feature evaluation for relevance based on linear transformation. by Jia Liu (41593)

Main code. by Jingguo Bi (21439766)

Calculated importance of the features selected based on relevance to MQI. by Naiara Virto (20490408)

Key Findings and Methodologies. by Carlos M. Ardila (19996060)

CatBoost algorithm structure to predict clinical outcome. by Andrew McDonald (2307400)

Subjects: “…provide interpretable predictions…”

Data Sheet 1_Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.pdf by 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 by 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. …”

A comparative summary of relevant work on cardiovascular disease prediction using machine learning and deep learning. The table compares methods, datasets, key performance metrics,... by Medhat A. Tawfeek (22522087)

Relevant Parameters. by Dongyan Wang (195482)

Parameters of stimuli used in the study. by Elnaz Nemati (21402730)

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Prediction performance compared with the CART algorithm. by Yixian Wen (12201388)

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

Relevant computed image data. by Zhaoyu Ku (20660546)

List of technical characteristics of relevant literature. by Haoran Cui (7235234)

Comparison of multi-model performance. by Lei Gao (6566)

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External cohort raw data. by Lei Gao (6566)

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