359.866 hasil untuk machine learning
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Artikel Jurnal Teks lengkap
Designing optimal behavioral experiments using machine learning
…work, we provide a tutorial on leveraging recent advances in BOED and machine learning to find optimal experiments for any kind of model that we can simulate data from, and show how by-products of this procedure allow for quick and straightforward evaluation of models and their parameters against…
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Machine-Learning-Based Calibration of Temperature Sensors
Temperature sensors are widely used in industrial production and scientific research, and accurate temperature measurement is crucial for ensuring the quality and safety of production processes. To improve the accuracy and stability of temperature sensors, this paper proposed using an artificial…
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Machine learning techniques for classifying dangerous asteroids
…tuning of Extra Tree, Random Forest, Light Gradient Boosting Machine, Gradient Boosting, and Ada Boost, the hazards of asteroids around the Earth were classified, and the results of ROC Curves for these algorithms were compared. • Reviewing the list of NASA-certified asteroids classified as the…
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Novel machine learning approaches revolutionize protein knowledge
Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most…
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Emergency Floor Plan Digitization Using Machine Learning
An increasing number of special-use and high-rise buildings have presented challenges for efficient evacuations, particularly in fire emergencies. At the same time, however, the use of autonomous vehicles within indoor environments has received only limited attention for emergency scenarios. To…
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Applications of machine learning in familial hypercholesterolemia
…for FH identification and management have been proposed. Recently, machine learning (ML) has made great progress in the field of medicine, including many innovative applications in cardiovascular medicine. In this review, we discussed how ML can be used for FH screening, diagnosis and risk…
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MLpronto: A tool for democratizing machine learning
The democratization of machine learning is a popular and growing movement. In a world with a wealth of publicly available data, it is important that algorithms for analysis of data are accessible and usable by everyone. We present MLpronto, a system for machine learning analysis that is designed to…
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Advancing Computational Toxicology by Interpretable Machine Learning
…toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity…
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Discriminative machine learning for maximal representative subsampling
Biased population samples pose a prevalent problem in the social sciences. Therefore, we present two novel methods that are based on positive-unlabeled learning to mitigate bias. Both methods leverage auxiliary information from a representative data set and train machine learning classifiers to…
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Machine Learning for Benchmarking Critical Care Outcomes
…The last two decades have seen the development of several models using machine learning (ML) for clinical outcome prediction. ML is a field of artificial intelligence focused on creating algorithms that enable computers to learn from and make predictions or decisions based on data. This narrative…
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Predicting ward transfer mortality with machine learning
…of admission, the second set is more clinically relevant. We trained 16 machine learning models using both datasets. The best-performing models were fine-tuned and evaluated. The LightGBM model achieved the best results for both datasets. The model trained with 22 variables achieved a Receiver…
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Machine Learning assisted systematic reviewing in orthopaedics
BACKGROUND: Machine learning assisted systematic reviewing may help to reduce the work burden in systematic reviews. The aim of this study is therefore to determine by a non-developer the performance of machine learning assisted systematic reviewing on previously published orthopaedic reviews in…
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Machine learning methods for tracer kinetic modelling
…routine has been constrained by its complexity and computational costs. Machine learning poses an opportunity to improve modelling processes in terms of arterial input function prediction, the prediction of kinetic modelling parameters and model selection in both clinical and preclinical studies…
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Simplifying complex antibody engineering using machine learning
Machine learning is transforming antibody engineering by enabling the generation of drug-like monoclonal antibodies with unprecedented efficiency. Unsupervised algorithms trained on massive and diverse protein sequence datasets facilitate the prediction of panels of antibody variants with…
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Machine learning models’ assessment: trust and performance
ABSTRACT: The common black box nature of machine learning models is an obstacle to their application in health care context. Their widespread application is limited by a significant “lack of trust.” So, the main goal of this work is the development of an evaluation approach that can assess…
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Machine-learning diagnostic models for ovarian tumors
PURPOSE: To create a diagnostic framework for clinical behavior and pathological tissue prognosis in ovarian cancer by using machine-learning (ML) methods based on multiple biomarkers. EXPERIMENTAL DESIGN: Overall, 713 patients with ovarian tumors at Sun Yat Sen Memorial Hospital were randomized…
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Identification of eupneic breathing using machine learning
…eupneic breathing challenging. The present article uses an unsupervised machine learning model to identify eupneic breathing amidst a deluge of different DIAm electromyography (EMG) burst patterns in awake rats. This technique offers a scalable and reliable tool that improves efficiency of DIAm EMG…
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Integrating machine learning to advance epitope mapping
…but vary in accuracy, throughput, cost, and feasibility. By comparing machine learning epitope mapping tools, we discuss the importance of data selection, feature design, and algorithm choice in determining the specificity and prediction accuracy of an algorithm. This review discusses limitations…
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Familial Hypercholesterolemia Detection Through Machine Learning Algorithms
[Figure: see text]
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