214.423 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Machine Learning and Student Performance in Teams
This project applies a variety of machine learning algorithms to the interactions of first year college students using the GroupMe messaging platform to collaborate online on a team project. The project assesses the efficacy of these techniques in predicting existing measures of team member…
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Optimization of investment strategies through machine learning
The main objective of this research is to develop a sustainable stock quantitative investing model based on Machine Learning and Economic Value-Added techniques for optimizing investment strategies. Quantitative stock selection and algorithmic trading are the two features of the model. Principal…
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Machine learning for mHealth apps quality evaluation
…quality of mHealth apps. The objective of this paper is to use supervised machine learning algorithms to evaluate the quality of mHealth apps according to the ISO/IEC 25010 quality model based on user feedback. For this purpose, a total of 1682 user reviews have been collected from 86 mHealth apps…
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Prediction of hypercholesterolemia using machine learning techniques
…resources for conducting screening and/or prevention programs. Using machine learning techniques to identify factors contributing to hypercholesterolemia and developing predictive models can help early detection of hypercholesterolemia, especially in developing countries. METHODS: Data from the…
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Machine Learning in Dentistry: A Scoping Review
Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry…
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Algal community structure prediction by machine learning
…on the algal community shifts. This study highlighted the power of machine learning in predicting complex algal community structures and provided insights into the model interpretability.
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Supervised Machine Learning Enables Geospatial Microbial Provenance
…able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental…
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Technology readiness levels for machine learning systems
The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences…
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Machine learning for a sustainable energy future
…of renewable energy. Energy researchers have begun to incorporate machine learning (ML) techniques to accelerate these advances. In this Perspective, we highlight recent advances in ML-driven energy research, outline current and future challenges, and describe what is required to make the best use…
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IoT-based disease prediction using machine learning
…attempts to develop a novel system that implements the most efficient machine learning (ML) algorithm and takes input from the patients such as symptoms, audio recordings, available medical reports, and other histories of illnesses to accurately and holistically predict the disease that the…
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Machine learning based multipurpose medical image watermarking
Digital data security has become an exigent area of research due to a huge amount of data availability at present time. Some of the fields like medical imaging and medical data sharing over communication platforms require high security against counterfeit access, manipulation and other processing…
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Interpretable Machine Learning of Two‐Photon Absorption
…TPA database with ≈900 molecules, analyzing with interpretable machine learning (ML) the key molecular features explaining TPA magnitudes, and building a fast ML model for predictions. The ML model has prediction errors of similar magnitude compared to experimental and affordable QC methods errors…
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Machine learning assisted quantum super-resolution microscopy
…time-consuming acquisition of multi-photon event histograms. We present a machine learning-assisted approach for the realization of rapid antibunching super-resolution imaging and demonstrate 12 times speed-up compared to conventional, fitting-based autocorrelation measurements. The developed…
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Machine Learning and Medication Adherence: Scoping Review
BACKGROUND: This is the first scoping review to focus broadly on the topics of machine learning and medication adherence. OBJECTIVE: This review aims to categorize, summarize, and analyze literature focused on using machine learning for actions related to medication adherence. METHODS: PubMed…
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On the Applicability of Quantum Machine Learning
In this article, we investigate the applicability of quantum machine learning for classification tasks using two quantum classifiers from the Qiskit Python environment: the variational quantum circuit and the quantum kernel estimator (QKE). We provide a first evaluation on the performance of these…
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Characterizing Uncertainty in Machine Learning for Chemistry
[Image: see text] Characterizing uncertainty in machine learning models has recently gained interest in the context of machine learning reliability, robustness, safety, and active learning. Here, we separate the total uncertainty into contributions from noise in the data (aleatoric) and…
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