3.327 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Applications of machine learning to machine fault diagnosis: A review and roadmap
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Artikel Jurnal Teks lengkap
Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated learning. The core idea is all learning parties…
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Artikel Jurnal Teks lengkap
Machine Learning and Deep Learning in Energy Systems: A Review
…and detailed study has been conducted on the methods and applications of Machine Learning (ML) and Deep Learning (DL), which are the newest and most practical models based on Artificial Intelligence (AI) for use in energy systems. It should be noted that due to the development of DL algorithms…
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Artikel Jurnal Teks lengkap
Artificial intelligence, machine learning, and deep learning in liver transplantation
…from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to…
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Opportunities and Challenges for Machine Learning in Materials Science
Advances in machine learning have impacted myriad areas of materials science, such as the discovery of novel materials and the improvement of molecular simulations, with likely many more important developments to come. Given the rapid changes in this field, it is challenging to understand both the…
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Artikel Jurnal
Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery
…the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current…
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Artikel Jurnal
A Critical Review of Machine Learning of Energy Materials
Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials…
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Artikel Jurnal Teks lengkap
A comparison of machine learning algorithms for diabetes prediction
Diabetes is a disease that has no permanent cure; hence early detection is required. Data mining, machine learning (ML) algorithms, and Neural Network (NN) methods are used in diabetes prediction in our research. We used the Pima Indian Diabetes (PID) dataset for our research, collected from the…
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Artikel Jurnal
Ethical and Bias Considerations in Artificial Intelligence/Machine Learning
…in our practice settings, especially as an increased number of machine learning (ML) systems are being integrated within our various medical domains. Such ML-based systems have demonstrated remarkable capabilities in specified tasks such as, but not limited to, image recognition, natural language…
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Artikel Jurnal Teks lengkap
Research and application of machine learning for additive manufacturing
…and the in-depth research of AM in academia. To tackle the challenges, machine learning (ML) technologies rise to play a critical role as they are able to provide effective ways to quality control, process optimization, modelling of complex systems, and energy management. Hence, this paper employs…
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Artikel Jurnal Teks lengkap
Machine learning enabled autonomous microstructural characterization in 3D samples
Abstract We introduce an unsupervised machine learning (ML) based technique for the identification and characterization of microstructures in three-dimensional (3D) samples obtained from molecular dynamics simulations, particle tracking data, or experiments. Our technique combines topology…
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Artikel Jurnal Teks lengkap
Machine Learning for Healthcare Wearable Devices: The Big Picture
Using artificial intelligence and machine learning techniques in healthcare applications has been actively researched over the last few years. It holds promising opportunities as it is used to track human activities and vital signs using wearable devices and assist in diseases' diagnosis, and it…
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Artikel Jurnal Teks lengkap
eXtreme Gradient Boosting Algorithm with Machine Learning: a Review
The primary task of machine learning is to extract valuable information from the data that is generated every day, process it to learn from it, and take useful actions. Original language process, pattern detection, search engines, medical diagnostics, bioinformatics, and chemical informatics are…
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High-throughput microbial culturomics using automation and machine learning
…platform for the rapid generation of isolates on demand. We develop a machine learning approach that leverages colony morphology and genomic data to maximize the diversity of microbes isolated and enable targeted picking of specific genera. Application of this platform on fecal samples from 20…
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Involvement of Machine Learning Tools in Healthcare Decision Making
…recognized by humans. In healthcare for computational decision making, machine learning approaches are being used in these types of situations where a crucial data analysis needs to be performed on medical data to reveal hidden relationships or abnormalities which are not visible to humans…
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Artikel Jurnal Teks lengkap
Machine learning and landslide studies: recent advances and applications
Abstract Upon the introduction of machine learning (ML) and its variants, in the form that we know today, to the landslide community, many studies have been carried out to explore the usefulness of ML in landslide research and to look at some classic landslide problems from an ML point of view. ML…
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Artikel Jurnal Teks lengkap
Utilizing graph machine learning within drug discovery and development
Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a…
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