113.116 hasil untuk machine learning
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Sediment subduction in Hadean revealed by machine learning
…on these signatures has not been reached. To address this, we developed a machine learning classifier capable of deciphering the geochemical fingerprints of zircon. This allowed us to identify the oldest detrital zircon originating from sedimentary-derived “S-type” granites. Our results indicate…
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Cardiac disease risk prediction using machine learning algorithms
…expenses for treatment. Predicting the occurrence of heart diseases by machine learning (ML) algorithms has become significant work in healthcare industry. This study aims to create a such system that is used for predicting whether a patient is likely to develop heart attacks, by analysing various…
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Machine Learning-Driven Methods for Nanobody Affinity Prediction
…high-affinity Nbs using experimental methods. In the current study, 12 machine learning algorithms were compared in parallel to explore the potential patterns between Nb–ligand affinity and eight noncovalent interactions. After model comparison and optimization, four optimized models (SVMrB, RotFB…
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Machine Learning Descriptors for CO(2) Capture Materials
The influence of machine learning (ML) on scientific domains continues to grow, and the number of publications at the intersection of ML, CO(2) capture, and material science is growing rapidly. Approaches for building ML models vary in both objectives and the methods through which materials are…
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Machine learning potential predictor of idiopathic pulmonary fibrosis
INTRODUCTION: Idiopathic pulmonary fibrosis (IPF) is a severe chronic respiratory disease characterized by treatment challenges and poor prognosis. Identifying relevant biomarkers for effective early-stage risk prediction is therefore of critical importance. METHODS: In this study, we obtained gene…
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Machine learning-optimized targeted detection of alternative splicing
…events of interest. Primers are designed using Optimal Prime, a novel machine learning algorithm trained on the performance of thousands of primer sequences. In experimental benchmarks, LSV-seq achieves high on-target capture rates and concordance with RNA-seq, while requiring significantly lower…
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Machine learning-based analyzing earthquake-induced slope displacement
…to seismic activity. This study evaluates the capabilities of various machine learning models, including artificial neural network (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) in analyzing earthquake-induced slope displacement. A dataset of 45…
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Machine learning for accelerating screening in evidence reviews
Evidence reviews are important for informing decision‐making and primary research, but they can be time‐consuming and costly. With the advent of artificial intelligence, including machine learning, there is an opportunity to accelerate the review process at many stages, with study screening…
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Machine learning enhanced evaluation of semiconductor quantum dots
…and expedite this process, in this paper, we propose a data-driven machine-learning-based method of evaluating the applicability of a semiconductor quantum dot as single photon source. For this, first, a minimally redundant, but maximally relevant feature representation for quantum dot emission…
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Entry point of machine learning in axial spondyloarthritis
…struggle to meet the demands of personalised treatment. Recently, machine learning (ML) has seen rapid advancements in the medical field. By integrating large-scale data with diverse algorithms and using multidimensional data, such as patient medical records, laboratory examinations, radiological…
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Evaluation of Arabica Coffee Fermentation Using Machine Learning
…dimensions. Data analysis and predictive modeling were performed using machine learning techniques. This study found that NC exhibited a higher production of acids (citric, malic, succinic, and lactic) compared to PC, resulting in distinct chemical and sensory profiles. The decision tree showed…
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Design of Modified Polymer Membranes Using Machine Learning
…tackle this challenge is the application of data-driven methods such as machine learning. In this study, we applied machine learning methods to data sets containing the performance parameters of modified membranes. The resulting machine learning models were used to predict performance parameters…
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Machine learning in cardiac surgery: a narrative review
BACKGROUND AND OBJECTIVE: Machine learning (ML) is increasingly being utilized to provide data driven solutions to challenges in medicine. Within the field of cardiac surgery, ML methods have been employed as risk stratification tools to predict a variety of operative outcomes. However, the…
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Machine learning for predicting elective fertility preservation outcomes
This retrospective study applied machine-learning models to predict treatment outcomes of women undergoing elective fertility preservation. Two-hundred-fifty women who underwent elective fertility preservation at a tertiary center, 2019–2022 were included. Primary outcome was the number of…
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Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?
…cardiovascular changes in septic shock. We discuss the potential for machine learning systems to personalize cardiovascular resuscitation in sepsis. While the literature is replete with proofs of concept, the technological readiness of current systems is low, with a paucity of clinical trials and…
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Interpretable machine learning for dementia: A systematic review
INTRODUCTION: Machine learning research into automated dementia diagnosis is becoming increasingly popular but so far has had limited clinical impact. A key challenge is building robust and generalizable models that generate decisions that can be reliably explained. Some models are designed to be…
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Prediction of DNA i-motifs via machine learning
…the folding status and strength of iM structures. Here, we introduce a machine learning pipeline, iM-Seeker, to predict both folding status and structural stability of DNA iMs. The programme iM-Seeker incorporates a Balanced Random Forest classifier trained on genome-wide iMab antibody-based…
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