8.473 hasil untuk machine learning
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Integrating machine learning into life cycle assessment: Review and future outlook
Life Cycle Assessment (LCA) is widely used to quantify environmental impacts but often faces data gaps, heterogeneous practices, and limited timeliness. This review examines how machine learning (ML) can strengthen LCA across all four phases—goal & scope, life cycle inventory (LCI), life cycle…
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High throughput machine learning pipeline to characterize larval zebrafish motor behavior
Using machine learning, we developed models that rigorously detect and classify larval zebrafish spontaneous and stimulus-evoked behaviors in various well plate formats. Zebrafish are an ideal model system for investigating the neural substrates underlying behavior due to their simple nervous…
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Nine quick tips for trustworthy machine learning in the biomedical sciences
As machine learning (ML) becomes increasingly central to biomedical research, the need for trustworthy models is more pressing than ever. In this paper, we present nine concise and actionable tips to help researchers build ML systems that are technically sound but ethically responsible, and…
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Privacy-preserving AUC computation in distributed machine learning with PHT-meDIC
Ensuring privacy in distributed machine learning while computing the Area Under the Curve (AUC) is a significant challenge because pooling sensitive test data is often not allowed. Although cryptographic methods can address some of these concerns, they may compromise either scalability or accuracy…
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Machine learning for predicting cognitive deficits using auditory and demographic factors
…capability beyond demographic factors is unknown. Objective: Use machine learning to predict neurocognitive deficits, using auditory tests and demographic factors. Setting: The Infectious Disease Center in Dar es Salaam, Tanzania Participants: Participants were 939 Tanzanian individuals from Dar es…
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VesiclePy: A machine learning vesicle analysis toolbox for volume electron microscopy
…electron microscopy data. Our package integrates the efficiency of deep learning and the accuracy of human proofreading and provides a streamlined package in chunked processing and accurate indexing, localization, and visualization of single vesicle resolution in large vEM data. We demonstrate the…
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Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning
…can be endowed with an inductive bias towards abstraction through meta-learning, where they are trained on a distribution of tasks that share some abstract structure that can be learned and applied. However, because neural networks are hard to interpret, it can be difficult to tell whether agents…
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Enhanced wave overtopping simulation at vertical breakwaters using machine learning algorithms
…in computational efficiency, data-driven techniques including advanced Machine Learning (ML) methods have become more readily applicable. However, the methodological appropriateness and performance evaluation of ML techniques for predicting wave overtopping at vertical seawalls has not been…
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Prediction of hospital readmission of multimorbid patients using machine learning models
…and two aggregated multimorbidity and frailty scores. Second, we use machine learning prediction models on these multimorbid patients characteristics to predict rehospitalization within 30 and 365 days and their length of stay. Methods: This study was conducted on 8 882 anonymized patients…
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Deception detection with machine learning: A systematic review and statistical analysis
Several studies applying Machine Learning to deception detection have been published in the last decade. A rich and complex set of settings, approaches, theories, and results is now available. Therefore, one may find it difficult to identify trends, successful paths, gaps, and opportunities for…
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Classification of T lymphocyte motility behaviors using a machine learning approach
…is time and labor-intensive. In this work, we trained and validated a machine-learning model for the automated classification of behaviors of CD8+ T lymphocytes interacting with astrocytes and neurons. A balanced random forest was trained for the binary classification of established classes of cell…
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Study of various machine learning approaches for Sentinel-2 derived bathymetry
…compares the performance and precision of the most common one to modern machine learning algorithms. More specifically, the models based on shallow neural networks, decision trees and Random Forest algorithms have been proposed, investigated and confronted with the performance of pure analytical…
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Machine learning evaluation for identification of M-proteins in human serum
…diseases, such as multiple myeloma. Recent studies have shown that machine learning can be used to assess protein electrophoresis by, for example, examining protein glycan patterns to follow up tumor surgery. In this study we compared 26 different decision tree algorithms to identify the presence…
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Institutions and the resource curse: New insights from causal machine learning
There is a widely held belief that natural resource rents are a blessing if institutions are strong, but a curse if institutions are weak. We use data from 3,800 Sub-Saharan African districts and apply a causal forest estimator to reassess the relationship between institutions and the effects of…
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Sound symbolism in Japanese names: Machine learning approaches to gender classification
This study investigates the sound symbolic expressions of gender in Japanese names with machine learning algorithms. The main goal of this study is to explore how gender is expressed in the phonemes that make up Japanese names and whether systematic sound-meaning mappings, observed in Indo-European…
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Machine learning algorithm for ventilator mode selection, pressure and volume control
…inhalation. To tackle this problem, this study presents an Iterative Learning PID Controller (ILC-PID), a unique current cycle feedback type controller that helps in gaining the correct pressure and volume. The paper also offers a clear and complete examination of the primarily efficient neural…
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Significant duration prediction of seismic ground motions using machine learning algorithms
This study aims to predict the significant duration (D5-75, D5-95) of seismic motion by employing machine learning algorithms. Based on three parameters (moment magnitude, fault distance, and average shear wave velocity), two additional parameters(fault top depth and epicenter mechanism parameters)…
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Recognition of bird species with birdsong records using machine learning methods
…envelop (MA), and Normalized Difference Soundscape Index (NDSI). Three machine learning models, Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were constructed. The results showed that the XGBoost model had the best performance among the three models…
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Machine learning identification of Pseudomonas aeruginosa strains from colony image data
…due to increased computing capacities and widespread application of machine learning tools. In this paper, we revisit the topic of colony morphotype on the within-species scale and apply concepts from image processing, computer vision, and deep learning to a dataset of 69 environmental and clinical…
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Artificial intelligence with temporal features outperforms machine learning in predicting diabetes
…Leveraging longitudinal electronic medical record (EMR) data with deep learning has great potential for diabetes prediction. This paper examines the predictive competency of deep learning models in contrast to state-of-the-art machine learning models to incorporate the time dimension of risk. The…
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