3.327 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Machine Learning for Plant Breeding and Biotechnology
…interpretation of results affected by G × E. Nonlinear nonparametric machine learning techniques are more efficient than classical statistical models in handling large amounts of complex and nondeterministic information with “multiple-independent variables versus multiple-dependent variables”…
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Machine Learning Techniques for 5G and Beyond
…of 6G systems will be the use of Artificial Intelligence (AI) and Machine Learning (ML) for such wireless networks. Every component and building block of a wireless system that we currently are familiar with from our knowledge of wireless technologies up to 5G, such as physical, network and…
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Machine-learning-based diagnostics of EEG pathology
Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approaches (with learned features). Previous studies on EEG pathology decoding have typically analyzed a limited number of…
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Machine Learning and Deep Learning Applications-A Vision
The application of artificial intelligence is machine learning which is one of the current topics in the computer field as well as for the new COVID-19 pandemic. Researchers have given a lot of input to enhance the precision of machine learning algorithms and lot of work is carried out rapidly to…
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Federated Learning: Collaborative Machine Learning without Centralized Training Data
Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm without transferring data samples across numerous decentralized edge devices or servers. This strategy differs from standard centralized machine learning techniques in which all local…
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Machine learning and deep learning applications in microbiome research
…interest from the scientific community, which has recently turned to machine learning and deep learning methods to interrogate the microbiome and elucidate the relationships between its composition and function. Here, we provide an overview of how the latest microbiome studies harness the inductive…
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Machine learning in construction: From shallow to deep learning
The development of artificial intelligence technology is currently bringing about new opportunities in construction. Machine learning is a major area of interest within the field of artificial intelligence, playing a pivotal role in the process of making construction “smart”. The application of…
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Artikel Jurnal
Smart farming using Machine Learning and Deep Learning techniques
…uses advanced technologies such as IOT, Data Mining, Data Analytics, Machine Learning to collect the data, train the systems and predict the results. With the help of technologies Precise farming helps to reduce manual labor and increase productivity. Farmers have been facing various challenges in…
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Machine learning and deep learning—A review for ecologists
Abstract The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI) has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML and DL algorithms are often perceived as opaque, and their relationship to classical data analysis…
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Artikel Jurnal
Introduction to Machine Learning, Neural Networks, and Deep Learning.
Purpose: To present an overview of current machine learning methods and their use in medical research, focusing on select machine learning techniques, best practices, and deep learning. Methods: A systematic literature search in PubMed was performed for articles pertinent to the topic of artificial…
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Swarm Learning for decentralized and confidential clinical machine learning
…precision medicine 1,2 . Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes 3 . However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation 4,5 . Here, to facilitate the…
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Emerging materials intelligence ecosystems propelled by machine learning
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Machine Learning for Electronically Excited States of Molecules
…are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of…
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Fake News Detection Using Machine Learning Ensemble Methods
…on the truthfulness of an article. In this work, we propose to use machine learning ensemble approach for automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a…
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Artificial intelligence and machine learning in cancer imaging
An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and…
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Identifying Ethical Considerations for Machine Learning Healthcare Applications
Along with potential benefits to healthcare delivery, machine learning healthcare applications (ML-HCAs) raise a number of ethical concerns. Ethical evaluations of ML-HCAs will need to structure the overall problem of evaluating these technologies, especially for a diverse group of stakeholders…
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Machine Learning and the Future of Cardiovascular Care
…guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clinical experts in some of these tasks. Machine learning has the potential to benefit patients and cardiologists, but only if clinicians take an active role in bringing these new…
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Machine Learning Applications for Mass Spectrometry-Based Metabolomics
…interact nonlinearly, and the data structures themselves are complex. Machine learning methods have become immensely popular for statistical analysis due to the inherent nonlinear data representation and the ability to process large and heterogeneous data rapidly. In this review, we address recent…
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Artikel Jurnal Teks lengkap
Machine learning for large-scale crop yield forecasting
Many studies have applied machine learning to crop yield prediction with a focus on specific case studies. The data and methods they used may not be transferable to other crops and locations. On the other hand, operational large-scale systems, such as the European Commission's MARS Crop Yield…
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