Comparison of Machine Learning Models Including Preoperative, Intraoperative, and Postoperative Data and Mortality After Cardiac Surgery
Sign Up to like & getrecommendations! Published in 2022 at "JAMA Network Open"
DOI: 10.1001/jamanetworkopen.2022.37970
Abstract: This prognostic study compares machine learning models that use preoperative, intraoperative, and postoperative data to predict mortality after cardiac surgery. read more here.
Keywords: machine learning; preoperative intraoperative; learning models; postoperative data ... See more keywords
Machine Learning Models for Predicting Liver Toxicity.
Sign Up to like & getrecommendations! Published in 2022 at "Methods in molecular biology"
DOI: 10.1007/978-1-0716-1960-5_15
Abstract: Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial… read more here.
Keywords: learning models; drug; toxicity; liver toxicity ... See more keywords
Cyberbullying detection solutions based on deep learning architectures
Sign Up to like & getrecommendations! Published in 2020 at "Multimedia Systems"
DOI: 10.1007/s00530-020-00701-5
Abstract: Cyberbullying is disturbing and troubling online misconduct. It appears in various forms and is usually in a textual format in most social networks. Intelligent systems are necessary for automated detection of these incidents. Some of the… read more here.
Keywords: learning; learning models; deep learning; cyberbullying detection ... See more keywords
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Computer-Aided Molecular Design"
DOI: 10.1007/s10822-020-00314-0
Abstract: Difficulties in interpreting machine learning (ML) models and their predictions limit the practical applicability of and confidence in ML in pharmaceutical research. There is a need for agnostic approaches aiding in the interpretation of ML… read more here.
Keywords: methodology; machine learning; interpretation; potency ... See more keywords
Improving the accuracy of machine-learning models with data from machine test repetitions
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Intelligent Manufacturing"
DOI: 10.1007/s10845-020-01661-3
Abstract: The modelling of machining processes by means of machine-learning algorithms is still based on principles that are especially adapted to mechanical approaches, in which very few inputs are varied with little repetition of experimental conditions.… read more here.
Keywords: improving accuracy; test; machine; learning models ... See more keywords
Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation
Sign Up to like & getrecommendations! Published in 2019 at "Water Resources Management"
DOI: 10.1007/s11269-018-2178-z
Abstract: In the present study, prediction of runoff and sediment at Polavaram and Pathagudem sites of the Godavari basin was carried out using machine learning models such as artificial neural network (ANN) and adaptive neuro-fuzzy inference… read more here.
Keywords: machine learning; learning models; current day; runoff sediment ... See more keywords
A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID-19 Chest X-ray Dataset
Sign Up to like & getrecommendations! Published in 2021 at "Cognitive Computation"
DOI: 10.1007/s12559-020-09802-9
Abstract: Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With… read more here.
Keywords: learning models; deep transfer; study; neutrosophic set ... See more keywords
Deep learning models for human centered computing in fog and mobile edge networks
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Ambient Intelligence and Humanized Computing"
DOI: 10.1007/s12652-018-0919-8
Abstract: Deep learning is a model with multi-level layer structure that uses the underlying output as input from the top. From down to above is a process of the unsupervised learning, which automatically learns useful features,… read more here.
Keywords: human centered; models human; learning models; computing fog ... See more keywords
Time-delayed machine learning models for estimating groundwater depth in the Hetao Irrigation District, China
Sign Up to like & getrecommendations! Published in 2021 at "Agricultural Water Management"
DOI: 10.1016/j.agwat.2021.107032
Abstract: Abstract A large amount of continuous input data is used to estimate groundwater level (GWL) by using machine learning models. However, data collection is very difficult and costly in undeveloped countries. Therefore, obtaining a general… read more here.
Keywords: model; learning models; input data; irrigation ... See more keywords
Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran
Sign Up to like & getrecommendations! Published in 2020 at "Atmospheric Pollution Research"
DOI: 10.1016/j.apr.2020.08.029
Abstract: Abstract It is necessary to predict wind erosion events and specify the related effective factors to prioritize management and executive measures to combat desertification caused by wind erosion in arid areas. Therefore, this work aimed… read more here.
Keywords: machine; index; machine learning; learning models ... See more keywords
Spatial modeling of flood probability using geo-environmental variables and machine learning models, case study: Tajan watershed, Iran
Sign Up to like & getrecommendations! Published in 2021 at "Advances in Space Research"
DOI: 10.1016/j.asr.2021.02.011
Abstract: Abstract The main objective of this study was to produce flood susceptibility maps for Tajan watershed, Sari, Iran using three machine learning (ML) models including Self-Organization Map (SOM), Radial Basis Function Neural Network (RBFNN), and… read more here.
Keywords: tajan watershed; study; learning models; flood ... See more keywords