204.674 hasil untuk machine learning
Menampilkan 881–900 · 0,81 detik · RSS
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Artikel Jurnal
Machine learning in dentistry: a scoping review.
Artificial intelligence (AI), specifically machine learning (ML), is increasingly applied in decision-making for dental diagnosis, prognosis, and treatment. However, the methodological completeness of published models has not been rigorously assessed. We performed a scoping review of PubMed-indexed…
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Artikel Jurnal
Machine learning on multiple topological materials datasets
…of Materiae and the Topological Materials Database. Thanks to this, machine-learning approaches are developed to categorize materials into five distinct topological types, with the XGBoost model achieving an impressive 85.2% classification accuracy. By conducting generalization tests on different…
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Artikel Jurnal
Balance equations for physics-informed machine learning
Using traditional machine learning (ML) methods may produce results that are inconsistent with the laws of physics. In contrast, physics-based models of complex physical, biological, or engineering systems incorporate the laws of physics as constraints on ML methods by introducing loss terms…
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Artikel Jurnal
Quantum Kernel Machine Learning With Continuous Variables
The popular qubit framework has dominated recent work on quantum kernel machine learning, with results characterising expressivity, learnability and generalisation. As yet, there is no comparative framework to understand these concepts for continuous variable (CV) quantum computing platforms. In…
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Artikel Jurnal Teks lengkap
SuperKEKB positron beam tuning using machine learning
In the KEK injector linac, four-ring simultaneous top-up injection has been achieved, and beam tuning is always performed in various beam modes. As there are four beam modes, the optimum magnet current and RF phase must be selected for each. There are numerous tuning knobs for each mode; thus, it…
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Artikel Jurnal
Variational Methods in Optical Quantum Machine Learning
The computing world is rapidly evolving and advancing, with new ground-breaking technologies emerging. Quantum Computing and Quantum Machine Learning have opened up new possibilities, providing unprecedented computational power and problem-solving capabilities while offering a deeper understanding…
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Artikel Jurnal
Trade Ease With Machine Learning and AWS
…cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by creating a machine learning trading strategy using a customized deep neural network (DNN). The strategy was then tested using a trading station…
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Artikel Jurnal
Intrusion detection method based on machine learning
A new intrusion detection method was presented based on machine learning for intrusion detection systems using shell commands as audit data.In the method,multiple dictionaries of shell command sequences of different lengths were constructed to represent the normal behavior profile of a network…
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Artikel Jurnal
Machine-learning based high-bandwidth magnetic sensing
…dynamic range, and bandwidth. Here we address this issue, and implement machine learning tools to enhance NV magnetic sensing in terms of the sensitivity/bandwidth tradeoff in large dynamic range scenarios. Our results indicate a potential reduction of required data points by at least a factor of…
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Artikel Jurnal
Application of machine learning in hepatocellular carcinoma
…for HCC, the overall prognosis for patients remains challenging. Machine learning (ML) , as a core technology of artificial intelligence, has been increasingly applied in the field of tumor research. Compared to traditional regression models, ML models excel at handling high dimensional data and…
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Artikel Jurnal
Pixel-Based Machine Learning in Medical Imaging
Machine learning (ML) plays an important role in the medical imaging field, including medical image analysis and computer-aided diagnosis, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require…
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Artikel Jurnal
Machine Learning‐Enabled Drug‐Induced Toxicity Prediction
…and time‐consuming. Big data and artificial intelligence (AI), especially machine learning (ML), are robustly contributing to innovation and progress in toxicology research. However, the optimal AI model for different types of toxicity usually varies, making it essential to conduct comparative…
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Artikel Jurnal
Machine Learning-Enriched Cybersecurity in Smart Grids
A smart grid is one of the critical infrastructures that, when targeted by a cyber-attack, could have disastrous effects on the economy and disrupt the lives of the population. Firewalls and Intrusion detection systems, the conventional protective schemes and cyber threat mitigation systems in…
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Artikel Jurnal
Machine Learning Based Classification for Spam Detection
…can be done through various methods such as keyword filtering, machine learning algorithms and image recognition. The goal of spam email classification is to prevent unwanted and potentially harmful emails from reaching the user's inbox. In this study, Random Forest (RF), Logistic Regression (LR)…
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Artikel Jurnal
Machine Learning for FPGA Electronic Design Automation
…lower power consumption as transistor technology continues to scale down. Machine learning (ML) algorithms have become pivotal in the electronic design automation (EDA) of FPGAs, enabling the learning of relationships between input and the desired output based on representative data properties…
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Artikel Jurnal
Enhancing hydraulic fracturing efficiency through machine learning
…and ensuring environmental safety. This study investigates the role of machine learning (ML) methodologies in enhancing the precision of these assessments, with a particular focus on fracture width a critical factor in the effectiveness of HF. By utilizing a comprehensive dataset from hydrocarbon…
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Artikel Jurnal
Some Granular Computing Based Machine Learning Algorithms
Granular computing has emerged as a new computational method that is beneficial when dealing with large amounts of data. In recent years, several machine learning models based on the granular framework have been developed, outperforming traditional machine learning models. This article reviews some…
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Artikel Jurnal
Detecting Textual Propaganda Using Machine Learning Techniques
…to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annotating the text, feature engineering is performed using techniques like term frequency/inverse document frequency…
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