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Machine learning

Real-time recognition of spraying area for UAV sprayers using a deep learning approach

Shahbaz Khan, Muhammad Tufail, [ ... ], Arsalan Wasim

predPhogly-Site: Predicting phosphoglycerylation sites by incorporating probabilistic sequence-coupling information into PseAAC and addressing data imbalance

Sabit Ahmed, Afrida Rahman, [ ... ], Shamim Ahmad

Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19

Limin Yu, Alexandra Halalau, [ ... ], Girish B. Nair

Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO/OTA classification system

Anna Lind, Ehsan Akbarian, [ ... ], Max Gordon

ReCiter: An open source, identity-driven, authorship prediction algorithm optimized for academic institutions

Paul J. Albert, Sarbajit Dutta, [ ... ], Curtis L. Cole

Automated system for diagnosing endometrial cancer by adopting deep-learning technology in hysteroscopy

Yu Takahashi, Kenbun Sone, [ ... ], Tomoyuki Fujii

Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer’s disease

Ray O. Bahado-Singh, Sangeetha Vishweswaraiah, [ ... ], Uppala Radhakrishna

A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data

Daniella Vos, Richard Stafford, Emma L. Jenkins, Andrew Garrard

Body fat predicts exercise capacity in persons with Type 2 Diabetes Mellitus: A machine learning approach

Tanmay Nath, Rexford S. Ahima, Prasanna Santhanam

Adaptive dimensionality reduction for neural network-based online principal component analysis

Nico Migenda, Ralf Möller, Wolfram Schenck

Optimal strategy of sEMG feature and measurement position for grasp force estimation

Changcheng Wu, Qingqing Cao, [ ... ], Aiguo Song

Development and performance of CUHAS-ROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia

Bumi Herman, Wandee Sirichokchatchawan, Sathirakorn Pongpanich, Chanin Nantasenamat

Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields

Ephrem Habyarimana, Faheem S. Baloch

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