204.674 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Insider Threat Detection Using Machine Learning Approach
…safe and preventing intrusions at any level. Recent advances in modern machine learning algorithms, such as deep learning and ensemble models, facilitate solving many challenging problems by learning latent patterns and modelling data. We used the Deep Feature Synthesis algorithm to derive…
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Detection of trachoma using machine learning approaches.
BackgroundThough significant progress in disease elimination has been made over the past decades, trachoma is the leading infectious cause of blindness globally. Further efforts in trachoma elimination are paradoxically being limited by the relative rarity of the disease, which makes clinical…
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Mitigating Webshell Attacks through Machine Learning Techniques
…a low detection rate for obfuscated webshells. However, with the help of machine learning algorithms, webshells can be detected more efficiently and accurately. In this paper, we propose a new PHP webshell detection model, the NB-Opcode (naïve Bayes and opcode sequence) model, which is a…
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Stroke Risk Prediction with Machine Learning Techniques
…and promoting a healthy life. In this research work, with the aid of machine learning (ML), several models are developed and evaluated to design a robust framework for the long-term risk prediction of stroke occurrence. The main contribution of this study is a stacking method that achieves a high…
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Brief Review of Machine Learning in Neurosurgery
Learning from experience is inherent to animals and humans and when used in computer models it is termed as Machine learning (ML) which was coined by Arthur Samuel the pioneer of computer gaming and artificial intelligence in 1959. This field grew out during the search for artificial intelligence…
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Machine Learning for Human Motion Intention Detection
…sensors for future mobile applications. Furthermore, we tested using two machine learning networks: a linearfFeedforward neural network and a long short-term memory network. The gait data are from five subjects for training and testing. The study results show that: 1. The network can successfully…
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Decoding quantum field theory with machine learning
Abstract We demonstrate how one can use machine learning techniques to bypass the technical difficulties of designing an experiment and translating its outcomes into concrete claims about fundamental features of quantum fields. In practice, all measurements of quantum fields are carried out through…
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Predicting Diabetes Mellitus With Machine Learning Techniques
…this alarming figure needs great attention. With the rapid development of machine learning, machine learning has been applied to many aspects of medical health. In this study, we used decision tree, random forest and neural network to predict diabetes mellitus. The dataset is the hospital physical…
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Predicting Renewable Energy Investment Using Machine Learning
In order to combat climate change, many countries have promised to bolster Renewable Energy (RE) production following the Paris Agreement with some countries even setting a goal of 100% by 2025. The reasons are twofold: capitalizing on carbon emissions whilst concomitantly benefiting from reduced…
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Machine Learning: Models, Challenges, and Research Directions
Machine learning techniques have emerged as a transformative force, revolutionizing various application domains, particularly cybersecurity. The development of optimal machine learning applications requires the integration of multiple processes, such as data pre-processing, model selection, and…
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Machine learning and health need better values
Health care is a human process that generates data from human lives, as well as the care they receive. Machine learning has worked in health to bring new technology into this sociotechnical environment, using data to support a vision of healthier living for everyone. Interdisciplinary fields of…
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Interpretable discovery of semiconductors with machine learning
Abstract Machine learning models of material properties accelerate materials discovery, reproducing density functional theory calculated results at a fraction of the cost1–6. To bridge the gap between theory and experiments, machine learning predictions need to be distilled in the form of…
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Intrusion Detection System Using machine learning Algorithms
…paper, the focus will be on three different classifications; starting by machine learning, algorithms NB, SVM and KNN. These algorithms will be used to define the best accuracy by means of the USNW NB 15 DATASET in the first stage. Based on the result of the first stage, the second one is used to…
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Machine learning renormalization group for statistical physics
We develop a machine-learning renormalization group (MLRG) algorithm to explore and analyze many-body lattice models in statistical physics. Using the representation learning capability of generative modeling, MLRG automatically learns the optimal renormalization group (RG) transformations from…
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Metastable vacua from torsion and machine learning
Abstract By implementing an error function on a Machine Learning algorithm we look for minimal conditions to construct stable Anti de Sitter and de Sitter vacua from dimensional type IIB String theory compactification on Kähler manifolds with torsion. This allows to have contributions to the scalar…
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Machine Learning Alternatives to Response Surface Models
…models, mainly nonlinear and nonparametric. We present a large panel of Machine Learning approaches that may be good alternatives to the classical RSM approximation. The state of the art of such approaches is given, including classification and regression trees, ensemble methods, support vector…
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Artikel Jurnal Teks lengkap
Animal species classification using machine learning techniques
…of ears and eyes from 10 animals and an experiment was conducted using machine learning techniques such as SVM and MLP to classify them as predators or pets. The evaluation results achieved the classification accuracies of 82% for MLP and 78% for SVM, which justify its effectiveness for the…
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Artikel Jurnal
Machine Learning Security: Threats, Countermeasures, and Evaluations
Machine learning has been pervasively used in a wide range of applications due to its technical breakthroughs in recent years. It has demonstrated significant success in dealing with various complex problems, and shows capabilities close to humans or even beyond humans. However, recent studies show…
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