359.866 hasil untuk machine learning
Menampilkan 441–460 · 2,46 detik · RSS
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Artikel Jurnal Teks lengkap
Machine Learning in Education
This narrative review examines the historical evolution, current applications, and major challenges of machine learning (ML) in education, positioning ML as a transformative yet deeply contested force in contemporary teaching and learning. Tracing developments from early computer-assisted…
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Machine learning electroweakino production
…\nu {\bar{\nu }}$$ Z → ν ν ¯ ) background. In this study, we explore Machine Learning (ML) methods for efficient signal/background discrimination. Our best attempt uses both reconstructed (jets, missing transverse energy, etc.) and low-level (particle-flow) objects. We find that the discrimination…
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Machine Learning in Bioelectrocatalysis
…experimental conditions. These issues can possibly be solved using machine learning (ML) in recent reports although the combination of them is still not mature. To summarize the progress of ML in bioelectrocatalysis, this paper first introduces the modeling process of ML, then focuses on the…
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Weakly supervised machine learning
Abstract Supervised learning aims to build a function or model that seeks as many mappings as possible between the training data and outputs, where each training data will predict as a label to match its corresponding ground‐truth value. Although supervised learning has achieved great success in…
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Federated Quantum Machine Learning
…One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done…
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Machine Learning in Gamification and Gamification in Machine Learning: A Systematic Literature Mapping
Albeit in different ways, both machine learning and gamification have transfigured the user experience of information systems. Although both are hot research topics, so far, little attention has been paid to how these two technologies converge with each other. This relation is not obvious as while…
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Predicting ACL Reconstruction Failure with Machine Learning: Development of Machine Learning Prediction Models
…Purpose: To (1) evaluate the predictive performance of different machine learning algorithms for the occurrence of failure in ACLR and (2) identify the most relevant predictors associated with this outcome. Study Design: Cohort study; Level of evidence, 3. Methods: A total of 680 patients who…
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Peningkatan Performa Klasifikasi Machine Learning Melalui Perbandingan Metode Machine Learning dan Peningkatan Dataset
Classification using machine learning is an alternative that is widely used to classify data. There are various classification methods or also known as machine learning classification algorithms that can be used. However, to get the best classification results, we need a classifier that fits the…
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Study becomes insight: Ecological learning from machine learning
1. The ecological and environmental science communities have embraced machine learning (ML) for empirical modelling and prediction. However, going beyond prediction to draw insights into underlying functional relationships between response variables and environmental ‘drivers’ is less…
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Machine learning and deep learning approaches in IoT
…of an IPS (intrusion prevention system) for these systems. As a result, machine learning and deep learning technologies are utilized to identify and control security in IoMT and IoV devices. This research study aims to investigate the research fields of current IoT security research trends. Papers…
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Histological stain evaluation for machine learning applications
…histological stains for automatic pattern and image analysis. BACKGROUND: Machine learning and image analysis are becoming increasingly important in pathology applications for automatic analysis of histological tissue samples. Pathologists rely on multiple, contrasting stains to analyze tissue…
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Predicting Increased Blood Pressure Using Machine Learning
The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to…
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Machine learning for neuroimaging with scikit-learn
Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or…
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Accelerating materials property predictions using machine learning
…a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that…
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Screening for Prediabetes Using Machine Learning Models
…prediabetes using an artificial neural network (ANN) and support vector machine (SVM) and performed a systematic evaluation of the models using internal and external validation. We compared the performance of our models with that of a screening score model based on logistic regression analysis for…
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Machine learning methods for metabolic pathway prediction
…standard. Feature data were used as input to an extensive collection of machine learning (ML) methods, including naïve Bayes, decision trees, and logistic regression, together with feature selection and ensemble methods. We compared the ML methods to the previous PathoLogic algorithm for pathway…
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