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

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MLP vs classification algorithms. by Mohd Mustaqeem (19106494)

Construction process of RF. by Xini Fang (20861990)

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Summary of all demographic, epidemiological and clinical data presented in data set used by the primary studies. by Sebastião Rogério da Silva Neto (11944944)

Summary of all non-clinical data (laboratory and others) presented in the data set used by the primary studies. by Sebastião Rogério da Silva Neto (11944944)

Algorithm assessment of temporal drought class using TPM. by Muhammad Asif Khan (7367468)

Clustering evaluation results of the FCM algorithm. by Xini Fang (20861990)

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San Diego PIRC (Primary Infection Resource Consortium) data: 1998-2019. by Sepideh Mazrouee (5191339)

The parameter settings of the RF algorithm after parameter optimization. by Xini Fang (20861990)

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Illustration of the texton detection process. by Vivian Akoto-Adjepong (18165075)

Accession list and passport data. by Diego Felipe Conejo-Rodríguez (18495740)

“…<div><p>High-throughput phenotyping brings new opportunities for detailed genebank accessions characterization based on image-processing techniques and data analysis using machine learning algorithms. …”

Data_Sheet_1_Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre.PDF by Benjamin Hunter (2885960)

Features selected by optimization algorithms. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

Hybrid feature selection algorithm of CSCO-ROA. by Afnan M. Alhassan (18349378)

“…Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. …”

The robustness test results of the model. by Xini Fang (20861990)

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

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Model performance comparison under different parameter settings. by Xini Fang (20861990)

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CRITIC weight calculation results for corporate operational risk warning indicators. by Xini Fang (20861990)

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Feature importance ranking results of model risk indicators. by Xini Fang (20861990)

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The membership distribution of various indicators in different risk categories by the FCM algorithm (Note: Clustering categories (Medium risk (2, 3)/ High risk (5)/ Risk-free (1)/... by Xini Fang (20861990)

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