8,473 results for machine learning
Showing 381–400 · 1.16 seconds · RSS
-
Journal Article Full text
Machine learning-inspired similarity measure to forecast M&A from patent data
…M&A deals. MASS is based on an extreme simplification of tree-based machine learning algorithms and naturally incorporates intuitive criteria for deals; as such, it is fully interpretable and explainable. By applying MASS to the Zephyr and Crunchbase datasets, we show that it outperforms a more…
-
Journal Article Full text
Machine learning for the diagnosis of fibromyalgia based on magnetic resonance imaging
…imaging (rs-fMRI) and diffusion tensor imaging (DTI) combined with a machine learning algorithm with the objective of enhancing the clinical diagnostic efficiency of FM. Two-sample t tests revealed differences between FM patients and healthy controls in rs-fMRI and DTI corresponding to brain image…
-
Journal Article Full text
Evaluating machine learning approaches for host prediction using H3 influenza genomic data
Background: H3 influenza A viruses (IAV) have been shown to frequently cross the species barrier which can be an important factor in sustained transmission and spread. Machine learning methods have been widely explored for host prediction of IAV using genomic data; however, this is often done using…
-
Journal Article Full text
An exploratory machine learning study on paediatric abdominal pain phenotyping and prediction
…and comorbidities were extracted from general practitioner records. Machine learning (ML) clustering was used to identify distinct AP phenotypes, and an ML-based predictive model was developed using demographics and clinical features. Results: 1,274 children experienced AP (9.2%) (average age: 8.4…
-
Journal Article Full text
Enhanced machine learning and hybrid ensemble approaches for Coronary Heart Disease prediction
…and imbalanced health datasets. To develop and evaluate enhanced machine learning and hybrid ensemble models for the prediction of coronary heart disease, with a focus on improving diagnostic performance, interpretability, and applicability in resource-constrained settings. We utilized a nationally…
-
Journal Article Full text
Using machine learning for detection of Parkinson’s disease and mild cognitive impairment
…may improve detection. Objectives: We aimed to investigate using machine learning whether a combination of neuroimaging and biofluid biomarkers would result in more effective identification of Parkinson’s disease and mild cognitive impairment. Methods: Utilizing the Parkinson’s Progression Markers…
-
Journal Article Full text
GAN-enhanced machine learning and metabolic modeling identify reprogramming in pancreatic cancer
…computational framework integrating genome-scale metabolic modeling with machine learning to identify metabolic signatures and therapeutic vulnerabilities in pancreatic cancer. To address the inherent class imbalance in cancer datasets, we generated synthetic healthy samples using a Wasserstein…
-
Journal Article Full text
Unveiling socio-demographic determinants of low birth weight using machine learning techniques
…This study aims to identify the key determinants of LBW and develop a machine learning-based predictive model to assess vulnerable mothers of having LBW babies based on risk factors associated with birth weight. Data for this study were obtained from the Bangladesh Demographic and Health Survey…
-
Journal Article Full text
Air pollution macro-regions identification using machine learning and spatio-temporal analysis
Air pollution caused by suspended particulate matter (PM) remains one of the key environmental challenges in Poland, particularly in the context of public health and spatial planning. This study presents a spatio-temporal analysis based on data from 173 air quality monitoring stations collected…
-
Journal Article Full text
Research on epilepsy detection methods based on interpretable features and machine learning
…clinicians. This paper proposes a pathophysiology-driven, interpretable machine learning algorithm to address the limitations of current EEG-based epilepsy detection methods, which include poor interpretability and complex feature engineering. We developed a low-dimensional, interpretable feature…
-
Journal Article Full text
Leveraging spatial data infrastructure for machine learning based building energy performance prediction
…and secondly, to develop energy performance prediction models with Machine Learning (ML) algorithms (i.e., Random Forest (RF), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) in order to estimate the overall performance scores of…
-
Journal Article Full text
Predicting the risk of asthma development in youth using machine learning models
…models for youth with good performance. This study aims to build machine learning models to better predict asthma development in youth using easily accessible national survey data. We analyzed cross-sectional combined 2021 and 2022 National Health Interview Survey (NHIS) data from 9,716 youth…
-
Journal Article Full text
Deep Aramaic: Towards a synthetic data paradigm enabling machine learning in epigraphy
Epigraphy is witnessing a growing integration of artificial intelligence, notably through its subfield of machine learning (ML), especially in tasks like extracting insights from ancient inscriptions. However, scarce labeled data for training ML algorithms severely limits current techniques…
-
Journal Article Full text
VIX constant maturity futures trading strategy: A walk-forward machine learning study
This study employs seven advanced machine learning approaches to conduct numerical predictions of the next-day returns of VIX constant-maturity futures (VIX CMFs) using the term structure information derived from VIX CMFs. Based on precise numerical predictions, this study proposes a new…
-
Journal Article Full text
Machine learning driven optimization of biomedical waste ash concrete for sustainable construction
…Waste Ash (BMWA) concrete from literature sources and applies advanced Machine Learning (ML) techniques to predict the mechanical properties of the mixes. Four ML models (Random Forest (RF), Self-Attention and Intersample Attention Transformer (SAINT), TabNet, and an Ensemble model) were subjected…
-
Journal Article Full text
Who Leads, What Matters? Machine Learning and the Complexity of University Performance
…characteristics, and rector profiles. Using a K-Means unsupervised machine learning algorithm, we identify five distinct clusters of Spanish public universities, each characterised by a specific combination of institutional performance indicators and management characteristics.
-
Journal Article Full text
Exploring the application of machine learning to expert evaluation of research impact
The objective of this study is to investigate the application of machine learning techniques to the large-scale human expert evaluation of the impact of academic research. Using publicly available impact case study data from the UK’s Research Excellence Framework (2014), we trained five machine…
-
Journal Article Full text
An interpretable machine learning framework for diagnosis and prognosis of COVID-19
…physicians and patients increasingly need interpretability when building machine models in healthcare. We propose an interpretable machine framework(KISM) that can diagnose and prognose patients based on blood test datasets. First, we use k-nearest neighbors, isolated forests, and SMOTE to…
-
Journal Article Full text
Prediction of digital transformation of manufacturing industry based on interpretable machine learning
The enhancement of digital transformation is of paramount importance for business development. This study employs machine learning to establish a predictive model for digital transformation, investigates crucial factors that influence digital transformation, and proposes corresponding improvement…
-
Journal Article Full text
Differentiation of white matter histopathology using b-tensor encoding and machine learning
…properties than conventional DW-MRI acquisitions. Additionally, machine learning methods are particularly useful for the study of multidimensional data sets. In this work, we have tested the reach of b-tensor encoding data analyses with machine learning in different histopathological scenarios. We…
Showing at most 50 pages. Narrow your search with filters for more specific results.