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Machine learning

Automatic assessment of laparoscopic surgical skill competence based on motion metrics

Koki Ebina, Takashige Abe, [ ... ], Nobuo Shinohara

Applying a deep convolutional neural network to monitor the lateral spread response during microvascular surgery for hemifacial spasm

Minsoo Kim, Sang-Ku Park, [ ... ], Doo-Sik Kong

Assessing incentives to increase digital payment acceptance and usage: A machine learning approach

Jeff Allen, Santiago Carbo-Valverde, [ ... ], Oya Pinar Ardic

Advanced dwarf mongoose optimization for solving CEC 2011 and CEC 2017 benchmark problems

Jeffrey O. Agushaka, Olatunji Akinola, [ ... ], Apu K. Saha

A novel application of neural networks to identify potentially effective combinations of biologic factors for enhancement of bone fusion/repair

Albert T. Anastasio, Bailey S. Zinger, Thomas J. Anastasio

Sign language recognition by means of common spatial patterns: An analysis

Itsaso Rodríguez-Moreno, José María Martínez-Otzeta, Izaro Goienetxea, Basilio Sierra

Lightweight mobile network for real-time violence recognition

Youshan Zhang, Yong Li, Shaozhe Guo

Temporal Events Detector for Pregnancy Care (TED-PC): A rule-based algorithm to infer gestational age and delivery date from electronic health records of pregnant women with and without COVID-19

Tianchu Lyu, Chen Liang, [ ... ], on behalf of the National COVID Cohort Collaborative Consortium

Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese

Thao Nguyen, Tam M. Vo, [ ... ], Ha Q. Nguyen

A competition model for prediction of admission scores of colleges and universities in Chinese college entrance examination

Xiao Chen, Yi Peng, Yachun Gao, Shimin Cai

BOOME: A Python package for handling misclassified disease and ultrahigh-dimensional error-prone gene expression data

Li-Pang Chen

ICD-10 based machine learning models outperform the Trauma and Injury Severity Score (TRISS) in survival prediction

Zachary Tran, Arjun Verma, [ ... ], Peyman Benharash

Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree

Yanning Lu, Yanzheng Xiang, [ ... ], Deyu Zhou

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