204.674 hasil untuk machine learning
Menampilkan 221–240 · 3,21 detik · RSS
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Artikel Jurnal
Classifying Oromo fricatives with machine learning
Using machine learning, this study investigates how segment duration, spectral moments, and DCT coefficients help differentiating singleton from geminate in Oromo fricatives. Eighteen native Oromo speakers from the western dialect produced speech data consisting of the fricatives in an intervocalic…
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Artikel Jurnal
Machine learning applied to biocatalysis research
Machine learning (ML) is rapidly influencing the development of many research areas, including biocatalysis, the use of enzymes and living systems to mediate chemical reactions, often of pharmaceutical and industrial importance. To find out how can machine learning be applied to biocatalysis…
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Artikel Jurnal Teks lengkap
AI and Machine Learning in Transplantation
Artificial Intelligence (AI) and machine learning (ML) are increasingly being applied across the transplantation care pathway, supporting tasks such as donor–recipient matching, immunological risk stratification, early detection of graft dysfunction, and optimisation of immunosuppressive therapy…
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Artikel Jurnal Teks lengkap
Machine Learning for Optimizing Database Performance
Due to the developing requirement of data storage, today’s database cannot meet the highly developing world. Besides, the wind of Machine Learning (ML) showed a possible solution to the dilemma. Therefore, many researchers have proposed strategies to improve the performance of databases in terms of…
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Artikel Jurnal
Machine Learning for Organic Fluorescent Materials
…to keep up with the rapid development of the field. The advent of machine learning (ML) has introduced transformative possibilities, enabling data‐driven exploration of the intricate relationships between molecular structures and fluorescence properties. Herein, we review the applications of ML in…
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Artikel Jurnal
Transcribing Medieval Manuscripts for Machine Learning
…orthographic variation between different textual witnesses for downstream machine learning tasks. In the final section of the article, we discuss a few examples of how the HTR-created transcriptions allow us to address new questions at scale in medieval manuscripts, such as textual variance across…
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Artikel Jurnal
Machine learning in Alzheimer’s disease genetics
Abstract Traditional statistical approaches have advanced our understanding of the genetics of complex diseases, yet are limited to linear additive models. Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest European consortium on Alzheimer’s disease…
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Artikel Jurnal
Optimizing Machine Learning in Hospitality Industry
…accuracy and adaptability. This research contributes to advancing machine learning applications in hospitality, providing actionable insights to improve operational performance, guest satisfaction, and competitive positioning.
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Artikel Jurnal
Machine learning applications in radiation oncology
Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for…
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Artikel Jurnal Teks lengkap
Quantum Machine Learning for Track Reconstruction
…scaling problems. In this work, we investigate the possibility of using machine learning techniques in combination with quantum computing. In particular, we represent particle trajectories as a graph data structure and train a quantum graph neural network to perform global pattern recognition. We…
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Artikel Jurnal
MACHINE LEARNING FOR EMPLOYMENT POSITION MAPPING
Employee performance directly impacts organizational efficiency, yet traditional HR analytics often lack predictive precision. This study bridges HR theory and machine learning by evaluating tree-based algorithms for employee data analysis. Using a dataset of 15,227 employee records, we tested the…
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Artikel Jurnal
Universal machine learning potentials under pressure
Universal machine learning interatomic potentials (uMLIPs) represent arguably the most successful application of machine learning to materials science, demonstrating remarkable performance across diverse applications. However, critical blind spots in their reliability persist. Here, we address one…
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Artikel Jurnal
Training-efficient density quantum machine learning
Abstract Quantum machine learning (QML) requires powerful, flexible and efficiently trainable models to be successful in solving challenging problems. We introduce density quantum neural networks, a model family that prepares mixtures of trainable unitaries, with a distributional constraint over…
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Artikel Jurnal
Performance analysis of machine learning libraries
The paper presents results of performance analysis of machine learning libraries. The research was based on ML.NET and TensorFlow tools. The analysis was based on a comparison of running time of the libraries, during detection of objects on sets of images, using hardware with different parameters…
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Artikel Jurnal
Machine learning for labor market matching
This paper develops a large-scale machine learning framework to improve labor market matching using rich administrative data. Matching is defined as a job seeker entering employment in a specific occupational field. We exploit comprehensive employment biographies from Germany, covering individual…
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Artikel Jurnal
Machine learning and LHC event generation
…This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and…
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Artikel Jurnal
Machine learning using magnetic stochastic synapses
…We demonstrate functional binary stochastic synapses alongside a gradient learning rule that allows their training with applicability to a range of stochastic systems. The rule, utilising the mean and variance of the neuronal output distribution, finds a trade-off between synaptic stochasticity and…
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Artikel Jurnal
Machine learning predicts electrospray particle size
…relies on processing expertise. The present study demonstrates how machine learning (ML) can expedite the optimization process by accurately predicting particle diameter, for both nano- and micron-sized particles. This was achieved by constructing an informative electrospraying database containing…
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