8.473 hasil untuk machine learning
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Machine learning approaches for predicting preventive maintenance costs of expressways in Xinjiang
With the expansion of high-speed road mileage and the aging of road networks, maintenance workloads have increased dramatically. Accurately forecasting preventive maintenance costs is a practical approach to rationally control maintenance expenditure, enhance capital utilization efficiency, and…
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Correlates of muscle strengthening exercise in adolescents: A machine learning based analysis
…of meeting MSE guidelines among U.S. adolescents using an explainable machine-learning approach. Methods: This cross-sectional study used data from the 2023 National Youth Risk Behavior Survey (YRBS), a nationally representative sample of U.S. high school students (n = 20,103). An eXtreme Gradient…
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Genome-wide machine learning analysis of anosmia and ageusia with COVID-19
…to construct a novel variant and gene prioritization pipeline, utilizing machine learning methods for the classification of patients. Models were assessed using a permutation-based variable importance (PVI) strategy for final prioritization of candidate variants and genes. The highest held-out test…
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Air quality index prediction using machine learning regression models: A comparative analysis
…monitoring and assessing air quality is crucial. With advancements in machine learning (ML) and artificial intelligence (AI), we now have extensive tools to measure the Air Quality Index (AQI). Air quality is influenced by various pollutants, including carbon monoxide (CO), nitrogen dioxide (NO2)…
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Exploring Nile Red and machine learning for microplastics detection in Tridacna maxima
…combining Nile red (NR) fluorescence staining with automated machine-learning detection. We optimized a digestion protocol using KOH and HNO3 for T. maxima viscera, and developed a DAPI-guided multi-spectra composite imaging approach based on triband fluorescence (DAPI, FITC, TRITC), to enhance…
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Machine learning–driven decoding of maternal immune signatures in repeated pregnancy loss
…we employed genotype-based origin analysis coupled with a supervised machine learning model and a transformer-based foundation model (scGPT). This hierarchical approach prioritized maternal T cells over other immune subsets as the population carrying the most distinct and generalizable…
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Investigating coastal ecosystem health in Bangladesh using machine learning and remote sensing
…and present conditions of the ecosystem. For assessment, a random forest machine learning model has been used in conjunction with remote sensing and GIS due to its high accuracy and efficiencies. The PSR (Pressure-State-Response) model has been used in this investigation. This study reveals that…
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Machine learning-supported identification of necrosis in colorectal and pancreatic cancer spheroids
…and Hs766-T cancer spheroids using an accurate, cost-effective, and fast machine learning-based system that relies only on brightfield (BF) images. Approach: In this study, three staining procedures with dyes were performed in colorectal and pancreatic cancer spheroids to highlight necrosis. The…
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Online labor trafficking and machine learning: A scoping review and research agenda
…this article reviews literature that applies or advocates analytics and machine learning (ML) for detecting labor trafficking (LT). It synthesizes methodologies across two contexts: (a) internet-facilitated LT (IF-LT), where digital platforms enable recruitment and coordination of trafficking, and…
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Structural damage detection and safety assessment method based on machine vision and machine learning
…presents a novel multi-scale vision-based framework that combines deep learning and machine learning for accurate and interpretable structural safety evaluation. Specifically, we integrate ResNet-50 and SegFormer models to jointly achieve coarse-level damage classification and fine-grained…
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An efficient leukemia prediction method using machine learning and deep learning with selected features
…the 25 most differentiating selected features for classification using machine and deep learning techniques. This study has a classification accuracy of 96.15% using Random Fores, 92.30 using Linear Regression, 96.15% using SVM, and 100% using LSTM. Deep learning methods have been shown to…
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Automated Discovery of Food Webs from Ecological Data Using Logic-Based Machine Learning
…on large numbers of species. In this paper we demonstrate that Machine Learning of food webs, using a logic-based approach called A/ILP, can generate plausible and testable food webs from field sample data. Our example data come from a national-scale Vortis suction sampling of invertebrates from…
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A Machine Learning Approach to Automated Structural Network Analysis: Application to Neonatal Encephalopathy
…map these networks to a high-dimensional space and then apply standard machine learning algorithms to predict neurological outcome in the cohort. Using nested cross-validation we demonstrate high prediction accuracy that is both statistically significant and robust over a broad range of thresholds…
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Machine Learning for Outcome Prediction of Acute Ischemic Stroke Post Intra-Arterial Therapy
…identification and validation of predictive variables. However, advanced machine learning algorithms offer an alternative, in particular, for large-scale multi-institutional data, with the advantage of easily incorporating newly available data to improve prediction performance. Our aim was to…
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Inference of Functional Relations in Predicted Protein Networks with a Machine Learning Approach
…prediction methods. The AODE (Averaged One-Dependence Estimators) machine learning algorithm is a suitable choice in this case and it provides better results than the individual prediction methods, and it has better performances than other tested alternative methods in this experimental set up. To…
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Developing a dengue forecast model using machine learning: A case study in China
…in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue. Methodology/Principal findings: Weekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during…
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Open source machine-learning algorithms for the prediction of optimal cancer drug therapies
Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate…
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Use of a machine learning framework to predict substance use disorder treatment success
…researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent for a particular prediction problem. SL generates a final model that is at least as good as any of the…
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Sleep in patients with disorders of consciousness characterized by means of machine learning
…to address these issues and investigate the usefulness of a multivariate machine learning technique based on permutation entropy, a complexity measure. Specifically, we used long-term polysomnography (PSG), along with video recordings in day and night periods in a sample of 23 DOC; 12 patients were…
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Prediction of Interactions between Viral and Host Proteins Using Supervised Machine Learning Methods
…using viral-host PPIs from VirusMINT. The three well-known supervised machine learning methods, such as SVM, Naïve Bayes and Random Forest, which are commonly used in the prediction of PPIs, were employed to evaluate the performance measure based on five-fold cross validation techniques. Results…
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