359.866 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Mycobacterium abscessus Drug Discovery using Machine Learning
…M. abscessus infections. Our approach to drug discovery integrates machine learning, medicinal chemistry and in vitro testing and has been previously applied to Mycobacterium tuberculosis. We have now identified several novel 1-(phenylsulfonyl)-1H-benzimidazol-2-amines that have weak activity on M…
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Perspectives in machine learning for wildlife conservation
…can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require…
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Machine learning toward high-performance electrochemical sensors
The so-coined fourth paradigm in science has reached the sensing area, with the use of machine learning (ML) toward data-driven improvements in sensitivity, reproducibility, and accuracy, along with the determination of multiple targets from a single measurement using multi-output regression…
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Machine Learning Assisted Clustering of Nanoparticle Structures
[Image: see text] We propose a scheme for the automatic separation (i.e., clustering) of data sets composed of several nanoparticle (NP) structures by means of Machine Learning techniques. These data sets originate from atomistic simulations, such as global optimizations searches and molecular…
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Human Gait Activity Recognition Machine Learning Methods
…different gait activities within each gait recording by utilizing Machine Learning algorithms. In line with this, various Machine Learning methods were tested and compared to establish the best-performing algorithm for the classification of the recorded gait information. The combination of…
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Inferred networks, machine learning, and health data
This paper presents a network science approach to investigate a health information dataset, the Sexual Acquisition and Transmission of HIV Cooperative Agreement Program (SATHCAP), to uncover hidden relationships that can be used to suggest targeted health interventions. From the data, four key…
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Automatic detection of pain using machine learning
…non-invasive, objective pain measurements through biosensors coupled with machine learning algorithms. In the current study, a physiological dataset associated with acute pain induction in healthy adults was leveraged to develop an algorithm capable of detecting pain in real-time and in natural…
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Machine-Learning Classification of Pulse Waveform Quality
…The present study aimed to determine the effectiveness of using machine-learning analysis in discriminating between high-quality and low-quality pulse waveforms induced by applying different contact pressures. Radial blood pressure waveform (BPW) signals were measured noninvasively in healthy young…
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Machine learning in knee osteoarthritis: A review
OBJECTIVE: The purpose of present review paper is to introduce the reader to key directions of Machine Learning techniques on the diagnosis and predictions of knee osteoarthritis. DESIGN: This survey was based on research articles published between 2006 and 2019. The articles were divided into four…
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Prediction of antifreeze proteins using machine learning
…Scoring Matrix Tri-Slicing (PseTS-PSSM). Keeping the benefits of ensemble learning, these feature sets are concatenated into different combinations. The best feature set is selected by Extremely Randomized Tree-Recursive Feature Elimination (ERT-RFE). The models are trained by Light eXtreme…
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Machine-Learning-Inspired Workflow for Camera Calibration
…and characterizing the calibration outcomes that is inspired by typical machine learning workflows and practical requirements of camera-based measurements. Combining standard calibration tools and the technique of active targets with phase-shifted cosine patterns, we demonstrate that the imaging…
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Coding for Large-Scale Distributed Machine Learning
This article aims to give a comprehensive and rigorous review of the principles and recent development of coding for large-scale distributed machine learning (DML). With increasing data volumes and the pervasive deployment of sensors and computing machines, machine learning has become more…
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Unsupervised Machine Learning to Identify Depressive Subtypes
OBJECTIVES: This study evaluated an unsupervised machine learning method, latent Dirichlet allocation (LDA), as a method for identifying subtypes of depression within symptom data. METHODS: Data from 18,314 depressed patients were used to create LDA models. The outcomes included future emergency…
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Mitigating urinary incontinence condition using machine learning
…consequences. OBJECTIVE: This paper proposes a framework based on machine learning for predicting urination time, which can benefit people with various degrees of UI. METHOD: A total of 850 data points were self-recorded by 51 participants to investigate how different factors impact urination time…
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Autism screening: an unsupervised machine learning approach
Early screening of autism spectrum disorders (ASD) is a key area of research in healthcare. Currently artificial intelligence (AI)-driven approaches are used to improve the process of autism diagnosis using computer-aided diagnosis (CAD) systems. One of the issues related to autism diagnosis and…
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Machine Learning on Early Diagnosis of Depression
To review the recent progress of machine learning for the early diagnosis of depression (major depressive disorder). The source of data was 32 original studies in the Web of Science. The search terms were “depression” (title) and “random forest” (abstract). The eligibility criteria were the…
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Machine Learning-Based Analytical Systems: Food Forensics
…by the advancements in multivariate analytical techniques as well as machine learning-based approaches. These techniques allow one to solve the typical problem associated with the interpretation of the complex response patterns generated in array-based strategies. Consequently, the machine…
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Vickers hardness prediction from machine learning methods
…or require coding knowledge. In this investigation, we built a successful machine learning model that implements Gradient Boosting Regressor (GBR) to predict hardness and uses the mechanical properties of a solid (bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio) as input…
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PAMMELA: Policy Administration Methodology using Machine Learning
…this paper, we propose PAMMELA, a Policy Administration Methodology using Machine Learning to assist system administrators in creating new ABAC policies as well as augmenting existing policies. PAMMELA can generate a new policy for an organization by learning the rules of a policy currently…
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