Data Sheet 1_Strategies for population-level identification of post-acute sequelae of COVID-19 through health administrative data.pdf by Cristina Mazzali (22090328)

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. …”

Data_Sheet_1_Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI.docx by Qingling Li (1444981)

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. …”

Data_Sheet_1_Reduced detection rate of artificial intelligence in images obtained from untrained endoscope models and improvement using domain adaptation algorithm.PDF by Junseok Park (4167106)

Image_4_Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI.TIF by Qingling Li (1444981)

Image_1_Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI.TIF by Qingling Li (1444981)

Image_7_Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI.TIF by Qingling Li (1444981)

Image_3_Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI.TIF by Qingling Li (1444981)

Image_2_Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI.TIF by Qingling Li (1444981)

Image_5_Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI.TIF by Qingling Li (1444981)

Image_6_Development and Validation of a Deep Learning Algorithm to Automatic Detection of Pituitary Microadenoma From MRI.TIF by Qingling Li (1444981)

Summary of results of naïve Bayes algorithms. by Christopher E. Niemczak (8586861)

“…<div><p>Importance</p><p>Predicting neurocognitive deficits using complex auditory assessments could change how cognitive dysfunction is identified, and monitored over time. Detecting cognitive impairment in people living with HIV (PLWH) is important for early intervention, especially in low- to middle-income countries where most cases exist. …”

Data_Sheet_1_Early detection of Parkinson’s disease through enriching the electronic health record using a biomedical knowledge graph.docx by Karthik Soman (5494406)

Table_1_Validation and comparison of the coding algorithms to identify people with migraine using Japanese claims data.DOCX by Kentaro Yamato (17453724)

Table_3_Validation and comparison of the coding algorithms to identify people with migraine using Japanese claims data.DOCX by Kentaro Yamato (17453724)

Table_2_Validation and comparison of the coding algorithms to identify people with migraine using Japanese claims data.DOCX by Kentaro Yamato (17453724)

Evaluation method of the 8 devices improved at post-market in the USA. MRMC, Multi-reader multi-case; MQSA, Mammography Quality Standards Act. by Mitsuru Yuba (14761712)

Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics by Zhichao Liu (191718)

Trace, Machine Learning of Signal Images for Trace-Sensitive Mass Spectrometry: A Case Study from Single-Cell Metabolomics by Zhichao Liu (191718)