Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression
Sign Up to like & getrecommendations! Published in 2020 at "JAMA Network Open"
DOI: 10.1001/jamanetworkopen.2019.18377
Abstract: This prognostic study of patients with major depressive disorder estimates how accurately an outcome of escitalopram treatment can be predicted from electroencephalographic data. read more here.
Keywords: machine learning; learning predicting; predicting escitalopram; treatment ... See more keywords
Machine learning for predicting properties of porous media from 2d X-ray images
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Petroleum Science and Engineering"
DOI: 10.1016/j.petrol.2019.106514
Abstract: Abstract In this paper, Convolutional Neural Networks (CNNs) are trained to rapidly estimate several physical properties of porous media using micro-computed tomography (micro-CT) X-ray images as input data. The tomograms of three different sandstone types… read more here.
Keywords: properties porous; machine learning; learning predicting; ray images ... See more keywords
Machine Learning for Predicting the Band Gaps of ABX3 Perovskites from Elemental Properties
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Physical Chemistry C"
DOI: 10.1021/acs.jpcc.9b11768
Abstract: The band gap is an important parameter that determines light-harvesting capability of perovskite materials. It governs the performance of various optoelectronic devices such as solar cells, light-e... read more here.
Keywords: machine learning; learning predicting; gaps abx3; band gaps ... See more keywords
Machine learning for predicting tourist spots’ preference and analysing future tourism trends in Bangladesh
Sign Up to like & getrecommendations! Published in 2024 at "Enterprise Information Systems"
DOI: 10.1080/17517575.2024.2415568
Abstract: ABSTRACT This study uses machine learning, including Support Vector Machines, Decision Trees, K-Nearest Neighbors, to examine Bangladesh’s tourism industry to forecast traveller preferences. We use time series analysis, including ARIMA, Moving Average, and Auto-regression models,… read more here.
Keywords: tourism; future tourism; learning predicting; machine learning ... See more keywords
Machine Learning for Predicting Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Healthcare Engineering"
DOI: 10.1155/2022/6278854
Abstract: Objective Immune checkpoint inhibitors, such as programmed death-1/ligand-1 (PD-1/L1), exhibited autoimmune-like disorders, and hyperglycemia was on the top of grade 3 or higher immune-related adverse events. Machine learning is a model from past data for… read more here.
Keywords: predicting hyperglycemic; machine; machine learning; prediction ... See more keywords
Harnessing Machine Learning for Predicting Cryptocurrency Returns
Sign Up to like & getrecommendations! Published in 2024 at "Global Business Review"
DOI: 10.1177/09721509241226575
Abstract: The study investigates the predictability of both the individual and basket of 10 major cryptocurrencies’ daily price changes between 2017 and 2023 by employing various machine learning classification algorithms such as random forests, k-nearest neighbour,… read more here.
Keywords: harnessing machine; learning predicting; machine; machine learning ... See more keywords
Deep Learning for Predicting Biomolecular Binding Sites of Proteins
Sign Up to like & getrecommendations! Published in 2025 at "Research"
DOI: 10.34133/research.0615
Abstract: The rapid evolution of deep learning has markedly enhanced protein–biomolecule binding site prediction, offering insights essential for drug discovery, mutation analysis, and molecular biology. Advancements in both sequence-based and structure-based methods demonstrate their distinct strengths… read more here.
Keywords: deep learning; learning predicting; sites proteins; biomolecular binding ... See more keywords