8.473 hasil untuk machine learning
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Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques
This paper identifies prognosis factors for survival in patients with acute myeloid leukemia (AML) using machine learning techniques. We have integrated machine learning with feature selection methods and have compared their performances to identify the most suitable factors in assessing the…
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Retraction: Tracking COVID-19 vaccine hesitancy and logistical challenges: A machine learning approach
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Exploration of physiological sensors, features, and machine learning models for pain intensity estimation
…present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors. This study uses BioVid Heat Pain Dataset. We extracted features from Electrodermal Activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals…
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Hybrid Machine Learning Approach to Zero-Inflated Data Improves Accuracy of Dengue Prediction
Background: Spatiotemporal dengue forecasting using machine learning (ML) can contribute to the development of prevention and control strategies for impending dengue outbreaks. However, training data for dengue incidence may be inflated with frequent zero values because of the rarity of cases…
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Development of new materials for electrothermal metals using data driven and machine learning
After adopting a combined approach of data-driven methods and machine learning, the prediction of material performance and the optimization of composition design can significantly reduce the development time of materials at a lower cost. In this research, we employed four machine learning…
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Enhancing early autism diagnosis through machine learning: Exploring raw motion data for classification
In recent years, research has been demonstrating that movement analysis, utilizing machine learning methods, can be a promising aid for clinicians in supporting autism diagnostic process. Within this field of research, we aim to explore new models and delve into the detailed observation of certain…
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Comparison of machine learning methods for genomic prediction of selected Arabidopsis thaliana traits
We present a comparison of machine learning methods for the prediction of four quantitative traits in Arabidopsis thaliana. High prediction accuracies were achieved on individuals grown under standardized laboratory conditions from the 1001 Arabidopsis Genomes Project. An existing body of evidence…
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Prediction of antimicrobial resistance of Klebsiella pneumoniae from genomic data through machine learning
…datasets now containing hundreds or even thousands of pathogen genomes, machine learning techniques are on the rise for predicting antibiotic resistance in pathogens, prediction based on gene content and genome composition. Aim of this work is to combine and incorporate machine learning methods on…
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A machine learning-based prediction of hospital mortality in mechanically ventilated ICU patients
…to assist ICU physicians with early-stage alerts. Methods: We developed a Machine Learning (ML) framework to predict hospital mortality in ICU patients receiving MV. Using the MIMIC-III database, we identified 25,202 eligible patients through ICD-9 codes. We employed backward elimination and the…
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The advantages of lexicon-based sentiment analysis in an age of machine learning
…of texts rapidly, replicably, and with high accuracy. Compared to machine learning and large language model (LLM) approaches, lexicon-based methods may sacrifice some in performance, but in exchange they provide generalizability and domain independence, while crucially offering the possibility of…
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Machine learning model to study the rugby head impact in a laboratory setting
…may not represent on-field conditions. This study aimed to create a machine-learning model capable of matching head impacts recorded via wearable sensors to the nearest match in a pre-existing library of laboratory-simulated head impacts for further investigation. Separate random forest models were…
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A novel machine learning model for perimeter intrusion detection using intrusion image dataset
…and precise activity classification. To address these challenges, a new machine learning model is developed. This model utilizes the pre-trained InceptionV3 for feature extraction on PID intrusion image dataset, followed by t-SNE for dimensionality reduction and subsequent clustering. When handling…
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Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms
…optimize energy storage, and manage grid stability. In this study, 5 machine learning models were used including: Gradient Boosting Regressor (GB), XGB Regressor (XGBoost), K-neighbors Regressor (KNN), LGBM Regressor (LightGBM), and CatBoost Regressor (CatBoost). Leveraging a dataset of 21045…
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Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper
…or several antibiotics. It has been suggested that genomic sequencing and machine learning (ML) could make resistance testing more accurate and cost-effective. Given that ML is likely to become an ever more important tool in medicine, we believe that it is important for pre-health students and…
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Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data
In recent years, researchers have proven the effectiveness and speediness of machine learning-based cancer diagnosis models. However, it is difficult to explain the results generated by machine learning models, especially ones that utilized complex high-dimensional data like RNA sequencing data. In…
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Exploring machine learning algorithms in sickle cell disease patient data: A systematic review
This systematic review explores the application of machine learning (ML) algorithms in sickle cell disease (SCD), focusing on diagnosis and several clinical characteristics, such as early detection of organ failure, identification of drug dosage, and classification of pain intensity. A…
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Machine learning estimates on the impacts of detection times on wildfire suppression costs
…in wildfire reporting delays with variation in suppression costs. We use machine learning and orthogonalization methods to isolate the impact of reporting delays from nonlinear impacts of the fire environment. We find that reporting delays account for only three percent of total suppression costs…
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Utilizing machine learning to tailor radiotherapy and chemoradiotherapy for low-grade glioma patients
Background: There is ongoing uncertainty about the effectiveness of various adjuvant treatments for low-grade gliomas (LGGs). Machine learning (ML) models that predict individual treatment effects (ITE) and provide treatment recommendations could help tailor treatments to each patient’s needs…
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Performance assessment of hybrid machine learning approaches for breast cancer and recurrence prediction
…has become a pressing medical issue. Evaluating and contrasting various Machine Learning (ML) techniques for breast cancer and recurrence prediction is crucial to choosing the best successful method. Inaccurate forecasts are common when using datasets with a large number of attributes. This study…
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Machine learning based method for analyzing vibration and noise in large cruise ships
…across different ship districts. Conversely, the rapid development in machine learning present an opportunity to leverage sophisticated algorithms for a more insightful examination of vibration and noise aboard cruise ships. This study designed a machine learning-driven approach to analyze the…
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