Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists
Sign Up to like & getrecommendations! Published in 2019 at "European Radiology"
DOI: 10.1007/s00330-019-06244-2
Abstract: ObjectiveThe purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare… read more here.
Keywords: prostate; learning classifiers; machine learning; predict gleason ... See more keywords
Quantitative Colorimetric Detection of Dissolved Ammonia Using Polydiacetylene Sensors Enabled by Machine Learning Classifiers
Sign Up to like & getrecommendations! Published in 2022 at "ACS Omega"
DOI: 10.1021/acsomega.2c01419
Abstract: Easy-to-use and on-site detection of dissolved ammonia are essential for managing aquatic ecosystems and aquaculture products since low levels of ammonia can cause serious health risks and harm aquatic life. This work demonstrates quantitative naked… read more here.
Keywords: learning classifiers; dissolved ammonia; machine learning; detection ... See more keywords
Comparison of machine learning classifiers for differentiating level and sport using movement data
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Sports Sciences"
DOI: 10.1080/02640414.2022.2145430
Abstract: ABSTRACT The purposes of this study were to determine if 1) recurrent neural networks designed for multivariate, time-series analyses outperform traditional linear and non-linear machine learning classifiers when classifying athletes based on competition level and… read more here.
Keywords: learning classifiers; machine learning; sport; level sport ... See more keywords
Fully Complex Deep Learning Classifiers for Signal Modulation Recognition in Non-Cooperative Environment
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3151980
Abstract: Deep learning (DL) classifiers have significantly outperformed traditional likelihood-based or feature-based classifiers for signal modulation recognition in non-cooperative environments. However, despite these recent improvements, the conventional DL classifiers still have an unintended problem in handling… read more here.
Keywords: learning classifiers; deep learning; modulation recognition; modulation ... See more keywords
Verifiable Homomorphic Secret Sharing for Machine Learning Classifiers
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3271319
Abstract: When using machine learning classifiers to classify data in cloud computing, it is crucial to maintain data privacy and ensure the correctness of classification results. To address these security concerns, we propose a new verifiable… read more here.
Keywords: secret sharing; verifiable homomorphic; machine; learning classifiers ... See more keywords
Can Machine Learning classifiers be used to regulate nutrients using small training datasets for aquaponic irrigation?: A comparative analysis
Sign Up to like & getrecommendations! Published in 2022 at "PLoS ONE"
DOI: 10.1371/journal.pone.0269401
Abstract: With the recent advances in the field of alternate agriculture, there has been an ever-growing demand for aquaponics as a potential substitute for traditional agricultural techniques for improving sustainable food production. However, the lack of… read more here.
Keywords: regulate nutrients; learning classifiers; machine learning; used regulate ... See more keywords
Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Medical Internet Research"
DOI: 10.2196/17478
Abstract: Background Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets… read more here.
Keywords: machine; learning classifiers; machine learning; surveillance ... See more keywords
Diagnostic Performance of Deep Learning Classifiers in Measuring Peripheral Anterior Synechia Based on Swept Source Optical Coherence Tomography Images
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Medicine"
DOI: 10.3389/fmed.2021.775711
Abstract: Purpose To develop deep learning classifiers and evaluate their diagnostic performance in detecting the static gonioscopic angle closure and peripheral anterior synechia (PAS) based on swept source optical coherence tomography (SS-OCT) images. Materials and Methods… read more here.
Keywords: learning classifiers; angle closure; deep learning; diagnostic performance ... See more keywords
Identifying Alcohol Use Disorder With Resting State Functional Magnetic Resonance Imaging Data: A Comparison Among Machine Learning Classifiers
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Psychology"
DOI: 10.3389/fpsyg.2022.867067
Abstract: Alcohol use disorder (AUD) is a burden to society creating social and health problems. Detection of AUD and its effects on the brain are difficult to assess. This problem is enhanced by the comorbid use… read more here.
Keywords: machine; learning classifiers; machine learning; alcohol use ... See more keywords
Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators
Sign Up to like & getrecommendations! Published in 2021 at "Applied Sciences"
DOI: 10.3390/app11188352
Abstract: Cardiovascular diseases (CVDs) kill about 20.5 million people every year. Early prediction can help people to change their lifestyles and to ensure proper medical treatment if necessary. In this research, ten machine learning (ML) classifiers… read more here.
Keywords: learning classifiers; machine learning; heart; heart disease ... See more keywords
Evaluating Information-Retrieval Models and Machine-Learning Classifiers for Measuring the Social Perception towards Infectious Diseases
Sign Up to like & getrecommendations! Published in 2019 at "Applied Sciences"
DOI: 10.3390/app9142858
Abstract: Recent outbreaks of infectious diseases remind us the importance of early-detection systems improvement. Infodemiology is a novel research field that analyzes online information regarding public health that aims to complement traditional surveillance methods. However, the… read more here.
Keywords: learning classifiers; evaluating information; infectious diseases; machine learning ... See more keywords