8,473 results for machine learning
Showing 981–1,000 · 0.45 seconds · RSS
-
Journal Article Full text
Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning
…body areas (e.g., feet, groin and ears). The spectra were analysed using machine learning to develop predictive models for infection. Findings: Using NIRS spectra of in vitro cultures and machine learning algorithms, we successfully detected low densities (
-
Journal Article Full text
Interpretable machine learning-based individual analysis of acute kidney injury in immune checkpoint inhibitor therapy
…(SHAP). Methods: We developed a gradient-boosting decision tree-based machine learning model continuously predicting AKI within 7 days, using the medical records of 616 immune checkpoint inhibitor-treated patients. The temporal changes in individual predictive reasoning in AKI prediction models…
-
Journal Article Full text
Damage detection of road domain waveform guardrail structure based on machine learning multi-module fusion
The current highway waveform guardrail recognition technology has encountered problems with low segmentation accuracy and strong noise interference. Therefore, an improved U-net semantic segmentation model is proposed to improve the efficiency of road maintenance detection. The model training is…
-
Journal Article Full text
A machine learning based depression screening framework using temporal domain features of the electroencephalography signals
Depression is a serious mental health disorder affecting millions of individuals worldwide. Timely and precise recognition of depression is vital for appropriate mediation and effective treatment. Electroencephalography (EEG) has surfaced as a promising tool for inspecting the neural correlates of…
-
Journal Article Full text
Tensor decomposition and machine learning for the detection of arteriovenous fistula stenosis: An initial evaluation
…of B-mode ultrasound cine loops (videos) of blood flow and machine learning classification. Classification of stenosis was based on the DUS assessment of blood flow volume, vessel diameter size, flow velocity, and spectral waveform features. Real-time B-mode cine loops of the arterial inflow…
-
Journal Article Full text
Integrating machine learning with otolith isoscapes: Reconstructing connectivity of a marine fish over four decades
…nominal and continuous assignment approaches by (1) developing a machine-learning multi-model ensemble classifier using Bayesian model averaging (nominal); and (2) integrating nominal predictions with continuous isoscapes to estimate the probability of origin across the spatial domain (continuous)…
-
Journal Article Full text
Predicting radiographic outcomes of vertebral body tethering in adolescent idiopathic scoliosis patients using machine learning
…been mixed. To improve surgical outcomes, this study aimed to develop a machine learning-based tool to predict short- and midterm spinal curve correction in AIS patients who underwent AVBT surgery, using the most predictive clinical, radiographic, and surgical parameters. After institutional review…
-
Journal Article Full text
Identification of integrated proteomics and transcriptomics signature of alcohol-associated liver disease using machine learning
Distinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcriptomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with…
-
Journal Article Full text
Common institutional investors and the quality of management earnings forecasts—Empirical and machine learning evidences
Based on the data of the Chinese A-share listed firms in China Shanghai and Shenzhen Stock Exchange from 2014 to 2021, this article explores the relationship between common institutional investors and the quality of management earnings forecasts. The study used the multiple linear regression model…
-
Journal Article Full text
Near-infrared spectroscopy and machine learning algorithms for rapid and non-invasive detection of Trichuris
…of the near-infrared spectroscopy (NIRS) technique coupled with machine learning algorithms to detect Trichuris muris in faecal, blood, serum samples and non-invasively through the skin of mice. Methodology: We orally infected 10 mice with 30 T. muris eggs (low dose group), 10 mice with 200 eggs…
-
Journal Article Full text
Role of convolutional features and machine learning for predicting student academic performance from MOODLE data
…and provide measures to help students avoid failing and learn better. Learning platforms complement traditional learning settings by analyzing student performance, which can help reduce the chance of student failure. Existing methods for student performance prediction in educational data mining…
-
Journal Article Full text
Using normative modeling and machine learning for detecting mild traumatic brain injury from magnetoencephalography data
…variability even across healthy people. Here, we used a multivariate machine-learning approach to detect mTBI from resting-state MEG measurements. To address the heterogeneity of the condition, we employed a normative modeling approach and modeled MEG signal features of individual mTBI patients as…
-
Journal Article Full text
A method for rapid machine learning development for data mining with doctor-in-the-loop
…projects. The aim of this study was to (a) develop interactive active machine-learning model training methodology using readily available software that was (b) easily adaptable to a wide range of natural language databases and allowed customised researcher-defined categories, and then (c) evaluate…
-
Journal Article Full text
Machine learning to predict bacteriologic confirmation of Mycobacterium tuberculosis in infants and very young children
Diagnosis of tuberculosis (TB) among young children (
-
Journal Article Full text
Experimental characterization and machine learning modeling of leakage-induced soil fluidization in water distribution systems
…covering a wide range of leakage rates and soil types. Three ensemble machine learning models—Random Forest, XGBoost, and a Stacking model integrating support vector regression, multilayer perceptron, and linear regression—were employed to enhance predictive accuracy and stability. The results of…
-
Journal Article Full text
Machine learning-based drought prediction using Palmer Drought Severity Index and TerraClimate data in Ethiopia
…the classification of the Palmer Drought Severity Index (PDSI) using machine learning models trained on TerraClimate data, incorporating variables such as precipitation, temperature, soil moisture, and vapor pressure deficit. We employed several classifiers Logistic Regression, Support Vector…
-
Journal Article Full text
Behavior classification: Introducing machine learning approaches for classification of sign-tracking, goal-tracking and beyond
Classifying behaviors in research often relies on predetermined or subjective cutoff values, which can introduce inconsistencies and reduce objectivity. For example, in Pavlovian conditioning studies, rodents display diverse behaviors which can be quantified using the Pavlovian Conditioning…
-
Journal Article Full text
RETRACTED: An innovative efficiency of incubator to enhance organization supportive business using machine learning approach
…decision-making. USAV analyses different financial eras using federated learning to find performance inefficiencies using a Kaggle dataset on small business success and failure. USAV recommends actionable improvements during innovation using non-recurrent statistical patterns, unlike standard…
-
Journal Article Full text
Establishment of two pathomic-based machine learning models to predict CLCA1 expression in colon adenocarcinoma
…on pathological image features using the random forest (RF) and XGBoost machine learning algorithms. The RF pathomics model demonstrated superior predictive performance, achieving area under the curve (AUC) values of 0.846 and 0.776 in the training and validation cohorts, respectively, and was…
-
Journal Article Full text
A comparative approach of machine learning models to predict attrition in a diabetes management program
…is critical to helping the growing diabetic population. Utilizing machine learning is gaining popularity in healthcare settings. The objective of this study is to assess the effectiveness of several machine learning methods in predicting attrition from a diabetes self-management program, utilizing…
Showing at most 50 pages. Narrow your search with filters for more specific results.