8.473 hasil untuk machine learning
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A detection method for android application security based on TF-IDF and machine learning
…in this paper, a new static detection method based on TF-IDF and Machine Learning is proposed. The system permissions are extracted in Android application package’s (Apk’s) manifest file. TF-IDF algorithm is used to calculate the permission value (PV) of each permission and the sensitivity value of…
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Person identification from EEG using various machine learning techniques with inter-hemispheric amplitude ratio
…training set using signal augmentation techniques and tested with several machine learning (ML) algorithms, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and k-Nearest Neighbor (kNN). Most of the ML algorithms showed 100% accuracy with 14 electrodes, and according to…
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Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning
…composition of multiple elements in organic amendments. We developed machine learning methods to rapidly quantify the concentrations of macro- and micronutrient elements present in the samples and propose a novel system for the quality assessment of organic amendments. Two types of machine learning…
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Improvement of electrocardiographic diagnostic accuracy of left ventricular hypertrophy using a Machine Learning approach
The electrocardiogram (ECG) is the most common tool used to predict left ventricular hypertrophy (LVH). However, it is limited by its low accuracy (
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Intracranial pressure based decision making: Prediction of suspected increased intracranial pressure with machine learning
…of the parameters including ONSD on CT were obtained. A supervised machine learning was applied to predict suspected increased ICP based on CT measurements. A linear correlation was shown between ln(age) and mean ONSD (mONSD) in normal children, revealing mONSD = 0.36ln(age)+2.26 (R2 = 0.60). This…
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Advanced machine learning model for better prediction accuracy of soil temperature at different depths
…very important for land-atmosphere interactions. The study compares four machine learning techniques, extreme learning machine (ELM), artificial neural networks (ANN), classification and regression trees (CART) and group method of data handling (GMDH) in estimating monthly soil temperatures at four…
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Development of a prediction model for hypotension after induction of anesthesia using machine learning
…by its diverse causes. We investigated the feasibility of developing a machine-learning model to predict postinduction hypotension. Naïve Bayes, logistic regression, random forest, and artificial neural network models were trained to predict postinduction hypotension, occurring between tracheal…
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VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning
Antimicrobial resistance (AMR) is an increasing threat to public health. Current methods of determining AMR rely on inefficient phenotypic approaches, and there remains incomplete understanding of AMR mechanisms for many pathogen-antimicrobial combinations. Given the rapid, ongoing increase in…
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3D nanostructural characterisation of grain boundaries in atom probe data utilising machine learning methods
Boosting is a family of supervised learning algorithm that convert a set of weak learners into a single strong one. It is popular in the field of object tracking, where its main purpose is to extract the position, motion, and trajectory from various features of interest within a sequence of video…
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Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning
Techniques using machine learning for short term blood glucose level prediction in patients with Type 1 Diabetes are investigated. This problem is significant for the development of effective artificial pancreas technology so accurate alerts (e.g. hypoglycemia alarms) and other forecasts can be…
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Machine learning-based microarray analyses indicate low-expression genes might collectively influence PAH disease
Accurately predicting and testing the types of Pulmonary arterial hypertension (PAH) of each patient using cost-effective microarray-based expression data and machine learning algorithms could greatly help either identifying the most targeting medicine or adopting other therapeutic measures that…
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Machine learning of human plasma lipidomes for obesity estimation in a large population cohort
…based on the plasma lipidome in a large population cohort using advanced machine learning modeling. A total of 1,061 participants of the FINRISK 2012 population cohort were randomly chosen, and the levels of 183 plasma lipid species were measured in a novel mass spectrometric shotgun approach…
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Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches
…climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), artificial neural networks (ANN), k-nearest neighbor (KNN), logistic regression (LR), and support vector machines (SVM) were used to develop models. Training and validation of these…
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Prediction of prognosis in immunoglobulin a nephropathy patients with focal crescent by machine learning
…heterogeneous in clinical outcomes. Therefore, we aim to develop a machine learning (ML) based predictive model for predicting the prognosis of IgAN with focal crescent formation and without obvious chronic renal lesions (glomerulosclerosis
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Machine Learning augmented docking studies of aminothioureas at the SARS-CoV-2—ACE2 interface
…relationship modeling either by classical linear QSAR or Machine Learning techniques. In this contribution, we focus on the comparison of the results obtained using different docking protocols on the example of the search for bioactivity of compounds containing N-N-C(S)-N scaffold at the S-protein…
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Feature engineering and machine learning for computer-assisted screening of children with speech disorders
…A systematic study and comparison of different linear and nonlinear machine learning classification techniques based on the raw features and the proposed features is conducted to assess the effectiveness of the novel features in classifying speech disorder patients from normal speakers.
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Functional classification and validation of yeast prenylation motifs using machine learning and genetic reporters
…FTase has broader target specificity than previously considered. Using a machine learning approach and training sets based on canonical (prenylated, proteolyzed, and carboxymethylated) and recently identified shunted motifs (prenylation only), this study aims to improve prenylation predictions with…
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Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization
…to successful pregnancy have been widely explored. In this study, we used machine learning algorithms to construct prediction models for clinical pregnancies in IVF. Materials and methods: A total of 24,730 patients entered IVF and intracytoplasmic sperm injection cycles with clinical pregnancy…
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Discriminatory Gleason grade group signatures of prostate cancer: An application of machine learning methods
…pathologists. As a result, computational approaches are being sought and machine learning has been gaining momentum in the prediction of the Gleason grade group. To date, machine learning literature has addressed this problem by using features from magnetic resonance imaging images, whole slide…
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Discriminating and classifying odontocete echolocation clicks in the Hawaiian Islands using machine learning methods
…multi-species analyses had not yet been completed. This study shows how a machine learning toolkit can effectively mitigate this problem by detecting and classifying echolocation clicks using a combination of unsupervised clustering methods and human-mediated analyses. Using these methods, it was…
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