CANDID-II Dataset by Sijing Feng (10187216)

“…This dataset can be used for training and testing for deep learning algorithms for adult chest x rays.</p><p dir="ltr">Unfortunately, since Feb 2024, the New Zealand government is changing the data governance on datasets used for AI development and this affects the process of how the CANDID II dataset is to be accessed by the external users. …”

Table 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Table 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Table 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Table 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Image 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Image 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Image 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tiff by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Image 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tiff by Hanlin Yu (17776399)

“…</p>Methods<p>We analyzed transcriptome data from the GEO dataset to identify differentially expressed genes (DEGs). …”

Table 2_Artificial intelligence in vaccine research and development: an umbrella review.docx by Rabie Adel El Arab (14113425)

“…Nonetheless, persistent challenges emerged—data heterogeneity, algorithmic bias, limited regulatory frameworks, and ethical concerns over transparency and equity.…”

Table 3_Artificial intelligence in vaccine research and development: an umbrella review.docx by Rabie Adel El Arab (14113425)

“…Nonetheless, persistent challenges emerged—data heterogeneity, algorithmic bias, limited regulatory frameworks, and ethical concerns over transparency and equity.…”

Table 1_Artificial intelligence in vaccine research and development: an umbrella review.docx by Rabie Adel El Arab (14113425)

“…Nonetheless, persistent challenges emerged—data heterogeneity, algorithmic bias, limited regulatory frameworks, and ethical concerns over transparency and equity.…”

CANDID-III Dataset by Sijing Feng (10187216)

“…This dataset can be used for training and testing for deep learning algorithms for adult chest x rays.</p><p dir="ltr">Unfortunately, since Feb 2024, the New Zealand government is changing the data governance on datasets used for AI development and this affects the process of how the CANDID III dataset is to be accessed by the external users. …”

Info-graphics summarising how to navigate the field of AI in the form of a tube (metro) map. by Aidan Crilly (21743791)

“…The rules in this paper are ordered according to the flow of scientific explorations: Framing the scientific problem and finding AI algorithms (Zone 2), coding (Zone 3), and testing and interpreting results (Zone 4). …”

Image 3_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.tif by Bin He (75597)

“…In the electrical remodeling investigation, ion channel gene sets and gene expression data were utilized alongside LASSO and SVM machine-learning algorithms combined with ROC curve analysis. …”

Table 3_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…In the electrical remodeling investigation, ion channel gene sets and gene expression data were utilized alongside LASSO and SVM machine-learning algorithms combined with ROC curve analysis. …”

Table 8_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…In the electrical remodeling investigation, ion channel gene sets and gene expression data were utilized alongside LASSO and SVM machine-learning algorithms combined with ROC curve analysis. …”

Table 2_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…In the electrical remodeling investigation, ion channel gene sets and gene expression data were utilized alongside LASSO and SVM machine-learning algorithms combined with ROC curve analysis. …”

Table 2_Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.docx by Rabie Adel El Arab (14113425)

“…</p>Ethical implications<p>Simultaneously, nurses voiced significant ethical concerns—chiefly around safeguarding patient data privacy, mitigating algorithmic bias, and preserving the compassionate, human-centered essence of nursing in an increasingly automated environment.…”

Table 4_Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.docx by Rabie Adel El Arab (14113425)

“…</p>Ethical implications<p>Simultaneously, nurses voiced significant ethical concerns—chiefly around safeguarding patient data privacy, mitigating algorithmic bias, and preserving the compassionate, human-centered essence of nursing in an increasingly automated environment.…”