8.473 hasil untuk machine learning
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A new hybrid machine learning model for predicting the renewal life of patents
…granted patents filed between 1995 and 2005. The popular statistical and machine learning algorithms do not result in accurate predictive models, because the patent renewal life distribution (at least for the Indian patents) shows unusual spikes at the two extreme values, which makes the modeling…
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A refined approach for evaluating small datasets via binary classification using machine learning
Classical statistical analysis of data can be complemented or replaced with data analysis based on machine learning. However, in certain disciplines, such as education research, studies are frequently limited to small datasets, which raises several questions regarding biases and coincidentally…
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Prognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms
…and protein biomarkers in regression models and random forest-based machine learning algorithms to predict poor outcomes and mortality. We report Odds Ratio (OR) or Hazard Ratio (HR) with 95% Confidence Interval (CI). We used five-fold cross-validation and bootstrapping for internal validation of…
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The efficacy of machine learning models in lung cancer risk prediction with explainability
…One of the significant problems in prediction is the black-box nature of machine learning models. Though the detection rate is comparatively satisfactory, people have yet to learn how a model came to that decision, causing trust issues among patients and healthcare workers. This work uses multiple…
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GFPrint™: A machine learning tool for transforming genetic data into clinical insights
The increasing availability of massive genetic sequencing data in the clinical setting has triggered the need for appropriate tools to help fully exploit the wealth of information these data possess. GFPrint™ is a proprietary streaming algorithm designed to meet that need. By extracting the most…
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Ten quick tips for sequence-based prediction of protein properties using machine learning
The ubiquitous availability of genome sequencing data explains the popularity of machine learning-based methods for the prediction of protein properties from their amino acid sequences. Over the years, while revising our own work, reading submitted manuscripts as well as published papers, we have…
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Intelligence against complexity: Machine learning for nonuniform temperature-field measurements through laser absorption
The effect of spatial nonuniformity of the temperature distribution was examined on the capability of machine-learning algorithms to provide accurate temperature prediction based on Laser Absorption Spectroscopy. First, sixteen machine learning models were trained as surrogate models of…
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Physician preference for receiving machine learning predictive results: A cross-sectional multicentric study
Artificial intelligence (AI) algorithms are transforming several areas of the digital world and are increasingly being applied in healthcare. Mobile apps based on predictive machine learning models have the potential to improve health outcomes, but there is still no consensus on how to inform…
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Sodium nitrite promotes atherosclerosis via IL-1β: Network toxicology and machine learning insights
…and results: To address this gap, we integrated network toxicology, machine learning, transcriptomics, molecular docking, and molecular dynamics simulations. Disease-related targets were first identified from public databases, and four core candidates—IL-1β, IL6, PTK2, and NOS3—were prioritized…
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Explaining urban street perception inequities between residents and tourists using interpretable machine learning
Understanding how different social groups perceive urban streets is essential for inclusive and sustainable urban design. This study proposes an interpretable and scalable machine learning framework that integrates Street View Images with subjective evaluations to examine perceptual differences…
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Forecasting crude oil futures price with energy uncertainty: Evidence from machine learning methods
…predictors, in forecasting crude oil prices using an array of machine learning methods. We find that EUI has a significant impact on crude oil prices. Moreover, machine learning methods combined with EUI performed better than the linear regression method due to a lower rate of prediction errors…
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Metabolomics-guided machine learning reveals diagnostic and mechanistic biomarkers in CHB with MASLD
…a predictive model using untargeted metabolomics integrated with machine learning. Methods: Serum metabolomics was performed on 160 subjects (80 CHB + MASLD and 80 healthy controls). Differential metabolites were identified and analyzed using KEGG enrichment and 4 machine learning algorithms…
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Predicting risk of ascending thoracic aortic aneurysm in asymptomatic adults using machine learning
…including aortic dissection and rupture. We aimed to develop and validate machine-learning models for predicting ATAA risk. We developed a predictive model for the risk of ATAA based on data from 18,382 participants from the Kangbuk Samsung Health Study between January 1, 2010, and December 31…
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Predicting coastal erosion susceptibility in Bangladesh under climate scenario via machine learning techniques
Using advanced machine learning methods along with geospatial data and climate estimates, this study found areas in Bangladesh that are likely to experience coastal erosion. Twenty important factors were looked at, such as meteorological, geographical, hydrological, tropological, and land-use…
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Speech-based respiratory diagnostics: A study on COVID-19 detection with machine learning
…of audio and functional features, which are then classified using various machine learning algorithms. Multiple ML classifiers Random Forest (RF), Support Vector Machine, Decision Tree, and Artificial Neural Network are evaluated. To enhance classification performance, five distinct feature…
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Machine learning based classification of aggressive and malignant renal tumors from multimodal data
This study aimed to develop and evaluate a machine learning pipeline using multiphase contrast-enhanced CT images and clinical data to classify renal tumors as benign, malignant-indolent, or malignant-aggressive, while assessing the contribution of each data source to the classification. In this…
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MIASurviveMTP: Machine learning for immediate assessment and survival prediction after massive transfusion protocol
Early triage of trauma patients requiring massive transfusion (MT) may help to marshal appropriate resources and improve treatment and outcome. Artificial intelligence (AI) and machine learning (ML) offer theoretical advantages compared to conventional prediction algorithms but have not been…
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Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact
Despite significant technical advances in machine learning (ML) over the past several years, the tangible impact of this technology in healthcare has been limited. This is due not only to the particular complexities of healthcare, but also due to structural issues in the machine learning for…
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Explainable machine learning for predicting longitudinal dementia status: Establishing a leakage-free benchmark
…Author summary: In our research, we sought to enhance the reliability of machine learning methods for predicting dementia by addressing common flaws in earlier studies. Dementia affects millions worldwide, and accurate early prediction using brain imaging and personal details collected over time…
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A hybrid machine learning and explainable AI framework for optimizing risk-based authentication
…experience. This study proposes a hybrid RBA framework that integrates machine learning ensemble techniques, fuzzy logic, clustering, and optimization to enhance account takeover detection and dynamic risk assessment. The ensemble classifier, combining Gradient Boosting, SVM, and XGBoost, predicts…
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