8.473 hasil untuk machine learning
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Identification of pear tissue mechanical parameters by integrating machine learning and optimization methods
…combines experimental analysis, finite element analysis, and a hybrid machine learning model to describe the viscoelastic behaviour of pear tissue. To obtain a rapid mapping from the intrinsic material constants to mechanical behavior, we develop a feasible surrogate architecture. This framework…
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Predicting student academic performance using TabPFN and SHAP: An interpretable machine learning approach
…with SHapley Additive exPlanations (SHAP) to construct an interpretable machine learning framework for predicting student achievement. Utilizing a publicly available dataset of 4,000 Spanish high school students (SAP-4000), we employed 10-fold cross-validation to train and compare eight machine…
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Predicting under-five child mortality in Zambia: Machine learning versus parametric survival models
…with C-indices ranging from 0.745 (Cox PH) to 0.750 (Gradient Boosting Machine) and Integrated Brier Scores ranging from 0.095 to 0.096; pairwise comparisons across models were not statistically significant (DeLong’s test: all p > 0.05). The Bayesian Joint Model revealed a significant association…
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Vaping and smoking as risk factors for arthritis: Evidence from double machine learning
…the associations of vaping and smoking with arthritis using Double Machine Learning (DML), and present illustrative economic scenarios projecting the potential cost implications of arthritis cases associated with these behaviors. Data from the Behavioral Risk Factor Surveillance System (BRFSS) of…
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Machine learning analysis based on deep learning for fatigue diagnostics in carbon fiber reinforced polymers
…health monitoring (SHM) applications. In this study, a hybrid deep learning framework is proposed for fatigue state classification of CFRP composites using sensor-based monitoring data. The framework integrates a one-dimensional Convolutional Neural Network (1D-CNN) to extract spatial degradation…
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Classification of mathematical test questions using machine learning on datasets of learning management system questions
…Owing to advances in technology and artificial intelligence, the Learning Management System (LMS) has become a popular application to conduct online learning for students. The LMS can store multiple pieces of information on students through an online database, enabling it to recommend appropriate…
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A Machine Learning Approach for Identifying Novel Cell Type–Specific Transcriptional Regulators of Myogenesis
…role in the cellular specificity exhibited by such enhancers. In summary, machine learning combined with evolutionary sequence analysis is useful for recognizing novel TFBSs and for facilitating the identification of cognate TFs that coordinate cell type–specific developmental gene expression…
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Combining Phylogenetic Profiling-Based and Machine Learning-Based Techniques to Predict Functional Related Proteins
…functional linkages, which successfully enhances a PP-based method with machine learning. The experimental results show that the proposed two-stage framework achieved the best overall performance in comparison with three PP-based methods.
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A Tale of Two “Forests”: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping
…as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified…
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Designing Focused Chemical Libraries Enriched in Protein-Protein Interaction Inhibitors using Machine-Learning Methods
…compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels…
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Bow-tie signaling in c-di-GMP: Machine learning in a simple biochemical network
…propose a mathematical model where the c-di-GMP network is analogous to a machine learning classifier. The analogy immediately suggests a mechanism for learning through evolution: adaptation though incremental changes in c-di-GMP network proteins acquires knowledge from past experiences and enables…
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Machine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review
…and public entities in dealing with this disorder. Thus, health data, machine learning and microsimulation techniques could be employed in developing prognostic estimates for dementia. Objective: The goal of this paper is to present evidence on the state of the art of studies investigating and the…
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Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach
…study, we demonstrate a novel method using relatively popular and robust machine learning methods to simulate the risk of terrorist attacks at a global scale based on multiple resources, long time series and globally distributed datasets. Historical data from 1970 to 2015 was adopted to train and…
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Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening
…between molecules and are further integrated with a variety of machine learning algorithms, including k-nearest neighbors, ensemble of trees, and deep convolutional neural networks, to manifest their descriptive and predictive powers for protein-ligand binding analysis and virtual screening of…
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Membrane protein contact and structure prediction using co-evolution in conjunction with machine learning
…arising from spurious transitive effects. Here, we show that applying machine learning approaches and custom descriptors improves evolutionary contact prediction accuracy, resulting in improvement of average precision by 6 percentage points for the top 1L non-local contacts. Further, we demonstrate…
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Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning
…indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive…
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A Machine Learning Approach to Identify Clinical Trials Involving Nanodrugs and Nanodevices from ClinicalTrials.gov
…nanomedical experiments and results. Methods: We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as…
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Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery
Integrated computational approaches for Mycobacterium tuberculosis (Mtb) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined…
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Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools
…prediction models with informatics modeling tools. Five supervised machine learning methods (support vector machine, random forests, artificial neural network, naïve Bayes classifier, K nearest neighbor) and partial least squares regression are used for building the prediction models. Binary and…
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Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza
Traditional methods for monitoring influenza are haphazard and lack fine-grained details regarding the spatial and temporal dynamics of outbreaks. Twitter gives researchers and public health officials an opportunity to examine the spread of influenza in real-time and at multiple geographical…
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