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Balancing Fairness: Unveiling the Potential of SMOTE-Driven Oversampling in AI Model Enhancement

• Authors : Kabir, Md Alamgir; Ahmed, Mobyen Uddin, Dr, 1976; Begum, Shahina, 1977

Subjects: bias mitigation; class balancing; fairness

• Source: ACM International Conference Proceeding Series. :21-29

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Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in South Florida

• Authors : Debarshi Datta; Safiya George Dalmida; Laurie Martinez

Subjects: COVID-19 pandemic; random forest classifier; gini index

• Source: Frontiers in Digital Health, Vol 5 (2023)

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Classification of Coronavirus (COVID-19) from X-ray and CT images using shrunken features

• Authors : Öztürk Ş.; Özkaya U.; Barstuğan M.

Subjects: classification; coronavirus; COVID-19

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Prediction of Polycyclic Aromatic Hydrocarbons (PAHs) Removal from Wastewater Treatment Sludge Using Machine Learning Methods.

• Authors : Caglar Gencosman, Burcu¹ (AUTHOR) ; Eker Sanli, Gizem² (AUTHOR)

Subjects: *SEWAGE sludge; *POLYCYCLIC aromatic hydrocarbons; *WASTEWATER treatment

• Source: Water, Air & Soil Pollution. Mar2021, Vol. 232 Issue 3, p1-17. 17p.

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A recall-optimised machine learning framework for small data improves risk stratification for Hirschsprung's disease

• Authors : Emilie G. Jaroy; Gabriel T. Risa; Inger Nina Farstad

Subjects: Machine learning; Imbalanced data; Rare diseases

• Source: Informatics in Medicine Unlocked, Vol 48, Iss , Pp 101530- (2024)

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An innovative machine learning optimization-based data fusion strategy for distributed wireless sensor networks

• Authors : Sollapure, Naganna Shankar; Govindaswamy, Poornima;

Subjects: Data fusion; DWSNs; GWO

• Source: Indonesian Journal of Electrical Engineering and Computer Science; Vol 36, No 2: November 2024; 1012-1022 ; 2502-4760 ; 2502-4752 ; 10.11591/ijeecs.v36.i2

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A recall-optimised machine learning framework for small data improves risk stratification for Hirschsprung's disease ; ENEngelskEnglishA recall-optimised machine learning framework for small data improves risk stratification for Hirschsprung's disease

• Authors : Jarøy, Emilie Grimnes; Risa, Gabriel T.; Farstad, Inger Nina

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Evaluating Random Forest Algorithm in Educational Data Mining: Optimizing Graduation on-time prediction using Imbalance Methods

• Authors : Bakri, Rizal; Astuti, Niken Probondani; Ahmar, Ansari Saleh

Subjects: Educational Data Mining; Random Forest Algorithm; Imbalance data methods

• Source: ARRUS Journal of Social Sciences and Humanities; Vol. 4 No. 1 (2024); 108-116 ; 2807-3010 ; 2776-7930 ; 10.35877/soshumv4i1

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The impact of sport-specific physical fitness change patterns on lower limb non-contact injury risk in youth female basketball players: a pilot study based on field testing and machine learning

• Authors : Yuanqi Huang; Changfei Li; Zhanshuang Bai

Subjects: injury management; physical fitness; data science

• Source: Frontiers in Physiology, Vol 14 (2023)

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Predicting Financial Literacy via Semi-supervised Learning ; Prédire la littératie financière via l'apprentissage semi-supervisé

• Authors : Rudd, David Hason; Huo, Huan; Xu, Guandong ...

Subjects: Financial literacy semi-supervised regression unbalanced datasets unlabelled Data; Financial literacy; semi-supervised regressionSydney; Australia

• Source: 34th Australasian Joint Conference, AI 2021, Sydney, NSW, Australia, February 2–4, 2022, ProceedingsAI 2021: Advances in Artificial Intelligencehttps://hal.science/hal-04078344AI

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