3.327 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
A comprehensive review of model compression techniques in machine learning
Abstract This paper critically examines model compression techniques within the machine learning (ML) domain, emphasizing their role in enhancing model efficiency for deployment in resource-constrained environments, such as mobile devices, edge computing, and Internet of Things (IoT) systems. By…
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A review of physics-based machine learning in civil engineering
The recent development of machine learning (ML) and Deep Learning (DL) increases the opportunities in all the sectors. ML is a significant tool that can be applied across many disciplines, but its direct application to civil engineering problems can be challenging. ML for civil engineering…
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Randomized Clinical Trials of Machine Learning Interventions in Health Care
Importance: Despite the potential of machine learning to improve multiple aspects of patient care, barriers to clinical adoption remain. Randomized clinical trials (RCTs) are often a prerequisite to large-scale clinical adoption of an intervention, and important questions remain regarding how…
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Recent Developments in Machine Learning for Energy Systems Reliability Management
This article reviews recent works applying machine learning (ML) techniques in the context of energy systems' reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in…
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Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm
Industry 4.0 is the new industrial revolution. By connecting every machine and activity through network sensors to the Internet, a huge amount of data is generated. Machine Learning (ML) and Deep Learning (DL) are two subsets of Artificial Intelligence (AI), which are used to evaluate the generated…
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Artificial intelligence, machine learning and deep learning in advanced robotics, a review
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have revolutionized the field of advanced robotics in recent years. AI, ML, and DL are transforming the field of advanced robotics, making robots more intelligent, efficient, and adaptable to complex tasks and environments…
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Differentiable modelling to unify machine learning and physical models for geosciences
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Artikel Jurnal Teks lengkap
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
…used to represent these materials in feature space, and review different learning architectures, as well as evaluation and interpretation strategies. In the second part, we review how the different approaches of machine learning have been applied to porous materials. In particular, we discuss…
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Machine Learning: New Ideas and Tools in Environmental Science and Engineering
…advancement in data analytics. Advanced data analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations for which conventional analytical methods face limitations or challenges. However, ML concepts and practices have…
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A review on machine learning approaches and trends in drug discovery
…an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational…
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Brain tumor detection and classification using machine learning: a comprehensive survey
…techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and…
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New machine learning method for image-based diagnosis of COVID-19
COVID-19 is a worldwide epidemic, as announced by the World Health Organization (WHO) in March 2020. Machine learning (ML) methods can play vital roles in identifying COVID-19 patients by visually analyzing their chest x-ray images. In this paper, a new ML-method proposed to classify the chest…
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Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review
With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide…
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Ambulatory Cardiovascular Monitoring Via a Machine‐Learning‐Assisted Textile Triboelectric Sensor
…of 40 ms, and a sensitivity of 0.21 µA kPa −1 . With the assistance of machine learning algorithms, the textile triboelectric sensor can continuously and precisely measure systolic and diastolic pressure, and the accuracy is validated via a commercial blood pressure cuff at the hospital…
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A review of molecular representation in the age of machine learning
Abstract Research in chemistry increasingly requires interdisciplinary work prompted by, among other things, advances in computing, machine learning, and artificial intelligence. Everyone working with molecules, whether chemist or not, needs an understanding of the representation of molecules in a…
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Discovery of antimicrobial peptides in the global microbiome with machine learning
Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and 87,920 prokaryotic genomes from environmental…
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Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis
Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehensive comparative analysis of various…
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Artikel Jurnal
Challenges to the Reproducibility of Machine Learning Models in Health Care
This Viewpoint reviews how machine learning models are developed and trained, discusses the challenges of reproducing medical machine learning model accuracy, and proposes practices and standards to improve the models’ reproducibility and replicability.
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Machine learning in materials science: From explainable predictions to autonomous design
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