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Machine learning

Assessing the potential of polygenic scores to strengthen medical risk prediction models of COVID-19

Aldo Córdova-Palomera, Csaba Siffel, [ ... ], Sandor Szalma

A divide and conquer approach to maximise deep learning mammography classification accuracies

Adam Jaamour, Craig Myles, [ ... ], David Harris-Birtill

The analysis of infrared high-speed motion capture system on motion aesthetics of aerobics athletes under biomechanics analysis

Yaoyu Qiu, Yingrong Guan, Shuang Liu

Semi-supervised nuclei segmentation based on multi-edge features fusion attention network

Huachang Li, Jing Zhong, [ ... ], Peng Shi

Comparison between single and serial computed tomography images in classification of acute appendicitis, acute right-sided diverticulitis, and normal appendix using EfficientNet

So Hyun Park, Young Jae Kim, [ ... ], Jung Han Hwang

Examining financial distress of the Vietnamese listed firms using accounting-based models

Thao Tran, Ngoc Hong Nguyen, [ ... ], Duc Hong Vo

Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified

Takuma Shibahara, Chisa Wada, [ ... ], Yoshimasa Ono

Screening of adolescent idiopathic scoliosis using generative adversarial network (GAN) inversion method in chest radiographs

Jun Soo Lee, Keewon Shin, [ ... ], Namkug Kim

Recurrent neural network modeling of multivariate time series and its application in temperature forecasting

Edward Appau Nketiah, Li Chenlong, Jing Yingchuan, Simon Appah Aram

Accurate deep learning model using semi-supervised learning and Noisy Student for cervical cancer screening in low magnification images

Yuki Kurita, Shiori Meguro, [ ... ], Toshihide Iwashita

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19

Rohitash Chandra, Chaarvi Bansal, [ ... ], Seshadri Vasan

An object detection algorithm combining self-attention and YOLOv4 in traffic scene

Kewei Lu, Fengkui Zhao, Xiaomei Xu, Yong Zhang

Devising novel performance measures for assessing the behavior of multilayer perceptrons trained on regression tasks

Giuliano Armano, Andrea Manconi

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