8.473 hasil untuk machine learning
Menampilkan 661–680 · 0,40 detik · RSS
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SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications
…We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing…
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Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications
Objective: To compare performance of risk prediction models for forecasting postoperative sepsis and acute kidney injury. Design: Retrospective single center cohort study of adult surgical patients admitted between 2000 and 2010. Patients: 50,318 adult patients undergoing major surgery…
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Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression
Background: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to…
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Automated classification of synaptic vesicles in electron tomograms of C . elegans using machine learning
…we present here a new automated approach to classify vesicles. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, the ImageJ macro 3D ART VeSElecT. With that we reliably distinguish CCVs and DCVs in electron tomograms of C. elegans NMJs using…
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Predictive classifier models built from natural products with antimalarial bioactivity using machine learning approach
…natural products to build accurate predictive models, utilizing classical machine learning approaches, which can find potential antimalarial hits from new sets of natural products. Classical machine learning approaches were used to build four classifier models (Naïve Bayesian, Voted Perceptron…
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Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning
The accuracy of machine learning tasks critically depends on high quality ground truth data. Therefore, in many cases, producing good ground truth data typically involves trained professionals; however, this can be costly in time, effort, and money. Here we explore the use of crowdsourcing to…
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A machine learning method to monitor China’s AIDS epidemics with data from Baidu trends
…uses search engine data to monitor and forecast AIDS in China. Methods: A machine learning method, artificial neural networks (ANNs), is used to forecast AIDS incidences and deaths. Search trend data related to AIDS from the largest Chinese search engine, Baidu.com, are collected and selected as…
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Prediction of Incident Diabetes in the Jackson Heart Study Using High-Dimensional Machine Learning
Statistical models to predict incident diabetes are often based on limited variables. Here we pursued two main goals: 1) investigate the relative performance of a machine learning method such as Random Forests (RF) for detecting incident diabetes in a high-dimensional setting defined by a large set…
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Correction: Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning
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Prediction models for high risk of suicide in Korean adolescents using machine learning techniques
…(= who have history of suicide ideation/attempt in previous year) using machine learning techniques. Methods: A nationally representative dataset of Korea Youth Risk Behavior Web-based Survey (KYRBWS) was used (n = 59,984 of middle and high school students in 2017). The classification process was…
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Identifying incident dementia by applying machine learning to a very large administrative claims dataset
…using traditional logistical regression methods. We hypothesized that machine-learning algorithms using administrative claims data may represent a novel approach to predicting ADRD. Using a national de-identified dataset of more than 125 million patients including over 10,000 clinical…
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Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy
…if a patient will benefit from surgery for DCM. We applied a supervised machine learning approach to develop a classification model to predict individual patient outcome after surgery for DCM. Patients undergoing surgery for DCM as a part of the AOSpine CSM-NA or CSM-I prospective, multi-centre…
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A comparative study on machine learning based algorithms for prediction of motorcycle crash severity
…that, if flouted, can generate inaccurate results. In this study, machine learning based algorithms were employed to predict and classify motorcycle crash severity. Machine learning based techniques are non-parametric models without the presumption of relationships between endogenous and exogenous…
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Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients
…cystectomy, i.e., cancer recurrence and survival. Information theory and machine learning algorithms are employed to create predictive models using a large prospective, continuously collected, temporally resolved, primary bladder cancer dataset comprised of 3503 patients (1971-2016). Patient…
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Machine learning meets partner matching: Predicting the future relationship quality based on personality traits
To what extent is it possible to use machine learning to predict the outcome of a relationship, based on the personality of both partners? In the present study, relationship satisfaction, conflicts, and separation (intents) of 192 partners four years after the completion of questionnaires…
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Association of specific gene mutations derived from machine learning with survival in lung adenocarcinoma
…are not been well known yet. The objective of this study was to perform machine learning with data from The Cancer Genome Atlas of patients with lung adenocarcinoma (LUAD) to find survival-specific gene mutations that could be used as survival-predicting biomarkers. To identify survival-specific…
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Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology
…heterogeneous genotype, which difficult biological marker identification. Machine learning methods are widely used to identify these markers, but their performance is highly dependent upon the size and quality of available data. In this study, we demonstrated that machine learning classifiers…
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US primary care in 2029: A Delphi survey on the impact of machine learning
Objective: To solicit leading health informaticians’ predictions about the impact of AI/ML on primary care in the US in 2029. Design: A three-round online modified Delphi poll. Participants: Twenty-nine leading health informaticians. Methods: In September 2019, health informatics experts were…
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Application of machine learning in the diagnosis of gastric cancer based on noninvasive characteristics
…were analyzed. We used gradient boosting decision tree (GBDT), a type of machine learning method, to construct a predictive model for the diagnosis of gastric cancer and evaluate the accuracy of the model. Results: Of the 709 patients, 398 were diagnosed with gastric cancer; 311 were health people…
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