8.473 hasil untuk machine learning
Menampilkan 201–220 · 0,81 detik · RSS
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Artikel Jurnal Teks lengkap
A novel machine learning based approach for iPS progenitor cell identification
…approach for the identification of iPS progenitor cells based on machine learning and microscopic image analysis. Firstly, we record the reprogramming process using a live cell imaging system after 48 hours of infection with retroviruses expressing Oct4, Sox2 and Klf4, later iPS progenitor cells…
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An investigation of machine learning methods in delta-radiomics feature analysis
…radiosurgery and bevacizumab, and to investigate the effectiveness of machine learning methods for delta-radiomics feature selection and building classification models. Methods: The pre-treatment, one-week post-treatment, and two-month post-treatment T1 and T2 fluid-attenuated inversion recovery…
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Designing machine learning workflows with an application to topological data analysis
In this paper we define the concept of the Machine Learning Morphism (MLM) as a fundamental building block to express operations performed in machine learning such as data preprocessing, feature extraction, and model training. Inspired by statistical learning, MLMs are morphisms whose parameters…
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FastEmbed: Predicting vulnerability exploitation possibility based on ensemble machine learning algorithm
…resources has become imperative for organizations. Recent works using machine learning techniques predict exploited vulnerabilities by extracting features from open-source intelligence (OSINT). However, in the face of explosive growth of vulnerability information, there is room for improvement in…
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Machine learning discovery of longitudinal patterns of depression and suicidal ideation
Background and aim: Depression is often accompanied by thoughts of self-harm, which are a strong predictor of subsequent suicide attempt and suicide death. Few empirical data are available regarding the temporal correlation between depression symptoms and suicidal ideation. We investigated the…
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Enhancing timeliness of drug overdose mortality surveillance: A machine learning approach
…prior to ICD-10 coding. The objective of this study was to develop a machine learning method to classify free-text death certificates as drug overdoses to provide faster drug overdose mortality surveillance. Methods: Using 2017–2018 Kentucky death certificate data, free-text fields were tokenized…
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Machine learning predicts cancer subtypes and progression from blood immune signatures
…distinguishing immune signatures of the CT26 and 4T1 tumour models using machine learning. Myeloid cells, specifically neutrophils and PD-L1-expressing myeloid cells, were found to correlate with tumour size in both the models. Elevated levels of G-CSF, IL-6 and CXCL13, and B cell counts were…
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CrowdGO: Machine learning and semantic similarity guided consensus Gene Ontology annotation
…consensus-based Gene Ontology (GO) term meta-predictor that employs machine learning models with GO term semantic similarities and information contents. By re-evaluating each gene-term annotation, a consensus dataset is produced with high-scoring confident annotations and low-scoring rejected…
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Brain predictors of fatigue in rheumatoid arthritis: A machine learning study
…a Least Square Linear Discriminant (LSLD), a linear Support Vector Machine (SVM) and a SVM with Radial Basis Function kernel. Results: The highest accuracy (67.9%) was achieved with the sMRI set, followed by the DTI set (63.8%), whereas classification performance using clinical features was at the…
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Predicting risks of low birth weight in Bangladesh with machine learning
…factors of low birth weight and predict low birth weight babies based on machine learning algorithms. Materials and methods: Low birth weight data has been taken from the Bangladesh Demographic and Health Survey, 2017–18, which had 2351 respondents. The risk factors associated with low birth weight…
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Detection of overdose and underdose prescriptions—An unsupervised machine learning approach
…these errors. In the present study, we used the one-class support vector machine (OCSVM), one of the most common unsupervised machine learning algorithms for anomaly detection, to identify overdose and underdose prescriptions. We extracted prescription data from electronic health records in Kyushu…
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Analysis of the mandibular canal course using unsupervised machine learning algorithm
…difficult. This study aims to introduce cluster analysis, unsupervised machine learning method, for classification of three-dimensional (3D) mandibular canal (MC) courses, and to visualize standard MC courses derived from cluster analysis in the Korean population. Materials and methods: A total of…
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Fast and automated biomarker detection in breath samples with machine learning
…that is time-consuming, subjective and may introduce errors. We propose a machine learning-based system to perform GC-MS data analysis that exploits deep learning pattern recognition ability to learn and automatically detect VOCs directly from raw data, thus bypassing expert-led processing. We…
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Machine learning for manually-measured water quality prediction in fish farming
Monitoring variables such as dissolved oxygen, pH, and pond temperature is a key aspect of high-quality fish farming. Machine learning (ML) techniques have been proposed to model the dynamics of such variables to improve the fish farmer’s decision-making. Most of the research on ML in aquaculture…
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Using machine learning as a surrogate model for agent-based simulations
In this proof-of-concept work, we evaluate the performance of multiple machine-learning methods as surrogate models for use in the analysis of agent-based models (ABMs). Analysing agent-based modelling outputs can be challenging, as the relationships between input parameters can be non-linear or…
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A machine learning prediction model for waiting time to kidney transplant
…model to estimate time on a kidney transplant waiting list using a machine learning approach. Methods: A retrospective cohort study including data of patients registered, between January 1, 2000 and December 31, 2017, in the waiting list of São Paulo State Organ Allocation System (SP-OAS) /Brazil…
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Toward characterizing cardiovascular fitness using machine learning based on unobtrusive data
…be obtained continuously. Thus, wearable sensors are associated with machine learning (ML) algorithms to investigate CF. Therefore, this study aimed to predict CF by using ML algorithms using data obtained by wearable technologies. For this purpose, 43 volunteers with different levels of aerobic…
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Adversarial attacks against supervised machine learning based network intrusion detection systems
Adversarial machine learning is a recent area of study that explores both adversarial attack strategy and detection systems of adversarial attacks, which are inputs specially crafted to outwit the classification of detection systems or disrupt the training process of detection systems. In this…
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Neo-epidemiological machine learning based method for COVID-19 related estimations
The 2019 newfound Coronavirus (COVID-19) still remains as a threatening disease of which new cases are being reported daily from all over the world. The present study aimed at estimating the related rates of morbidity, growth, and mortality for COVID-19 over a three-month period starting from Feb…
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Food anaphylaxis diagnostic marker compilation in machine learning design and validation
…diagnostic data across biosimilar proteins, which was used to develop a machine learning model for patient-specific and allergen-specific anaphylaxis assessment. In explanation of construct, this work describes the algorithm design for assignment of peanut allergen score as a quantitative measure…
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