3.327 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
A machine learning Automated Recommendation Tool for synthetic biology
…we present the Automated Recommendation Tool (ART), a tool that leverages machine learning and probabilistic modeling techniques to guide synthetic biology in a systematic fashion, without the need for a full mechanistic understanding of the biological system. Using sampling-based optimization, ART…
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Designing exceptional gas-separation polymer membranes using machine learning
separation performance. This ML technique, which is trained using a relatively small body of experimental data (and no simulation data), evidently represents an innovative means of exploring the vast phase space available for polymer membrane design.
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Unified representation of molecules and crystals for machine learning
Abstract Accurate simulations of atomistic systems from first principles are limited by computational cost. In high-throughput settings, machine learning can reduce these costs significantly by accurately interpolating between reference calculations. For this, kernel learning approaches crucially…
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An Explainable Machine Learning Framework for Intrusion Detection Systems
In recent years, machine learning-based intrusion detection systems (IDSs) have proven to be effective; especially, deep neural networks improve the detection rates of intrusion detection models. However, as models become more and more complex, people can hardly get the explanations behind their…
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Machine Learning Applications based on SVM Classification A Review
Extending technologies and data development culminated in the need for quicker and more reliable processing of massive data sets. Machine Learning techniques are used excessively. This paper, therefore, attempts to deal with data processing, using a support vector machine (SVM) algorithm in…
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PREDICTIVE ANALYSIS OF HEART DISEASES WITH MACHINE LEARNING APPROACHES
Machine Learning (ML) is used in healthcare sectors worldwide. ML methods help in the protection of heart diseases, locomotor disorders in the medical data set. The discovery of such essential data helps researchers gain valuable insight into how to utilize their diagnosis and treatment for a…
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Prediction of Chronic Kidney Disease - A Machine Learning Perspective
Chronic Kidney Disease is one of the most critical illness nowadays and proper diagnosis is required as soon as possible. Machine learning technique has become reliable for medical treatment. With the help of a machine learning classifier algorithms, the doctor can detect the disease on time. For…
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Early-Stage Alzheimer's Disease Prediction Using Machine Learning Models
Alzheimer's disease (AD) is the leading cause of dementia in older adults. There is currently a lot of interest in applying machine learning to find out metabolic diseases like Alzheimer's and Diabetes that affect a large population of people around the world. Their incidence rates are increasing…
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Machine learning algorithms to forecast air quality: a survey
…concentrations of certain pollutants are expected in the near future. Machine Learning models, in particular, Deep Learning models, have been widely used to forecast air quality. In this paper we present a comprehensive review of the main contributions in the field during the period 2011-2021. We…
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Artikel Jurnal
Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques
…industrial and logistic processes in this scenario. Fusion-based Machine learning provides a platform that may address the issues/limitations of Supply Chain Collaboration. Compared to the Classical probable data fusion techniques, the fused Machine learning method may offer a strong computing…
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The Role of Machine Learning Algorithms for Diagnosing Diseases
Nowadays, machine learning algorithms have become very important in the medical sector, especially for diagnosing disease from the medical database. Many companies using these techniques for the early prediction of diseases and enhance medical diagnostics. The motivation of this paper is to give an…
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A Review of Machine Learning for Near-Infrared Spectroscopy
…Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes…
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Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations
Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the…
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Machine Learning Approaches for EV Charging Behavior: A Review
…provide a comprehensive review for the use of supervised and unsupervised Machine Learning as well as Deep Neural Networks for charging behavior analysis and prediction. Recommendations and future research directions are also discussed.
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Artikel Jurnal
Knowledge Discovery: Methods from data mining and machine learning
…developments, methods, and theory, and enriching scientific discovery. Machine learning builds models and algorithms by learning and improving from data when the explicit model structure is unclear and algorithms with good performance are difficult to attain. The most recent development is to…
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DOME: recommendations for supervised machine learning validation in biology
DOME is a set of community-wide recommendations for reporting supervised machine learning-based analyses applied to biological studies. Broad adoption of these recommendations will help improve machine learning assessment and reproducibility.
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Interpretability of machine learning‐based prediction models in healthcare
Abstract There is a need of ensuring that learning (ML) models are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end‐users. Further, interpretable ML models allow healthcare experts to make reasonable and data‐driven…
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Tslearn, A Machine Learning Toolkit for Time Series Data
International audience
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Artikel Jurnal Teks lengkap
Interpretable machine learning for knowledge generation in heterogeneous catalysis
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