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GitHub5d
Machine learning: Neural network profiler
This code example demonstrates how to run through the ModusToolbox™ machine learning (ModusToolbox™-ML ... The following figure shows the communication sequence diagram between the tool and the device ...
SciELO12d
Property prediction of AZ80 magnesium alloy: an extreme learning machine model optimized by a new improved sparrow search algorithm
algorithm and the extreme learning machine (ELM), is proposed in this study, and the stresses of AZ80 magnesium alloy are predicted by the model through a 812-record dataset. The predicting results ...
IEEE17d
Teaching Machine Learning in K–12 Classroom: Pedagogical and Technological Trajectories for Artificial Intelligence Education
Abstract: Over the past decades, numerous practical applications of machine learning techniques have shown the potential of AI-driven and data-driven approaches in a large number of computing fields.
Frontiers18d
Machine Learning-Based Prediction of 6-Month Functional Recovery in Hypertensive Cerebral Hemorrhage: Insights from XGBoost and SHAP Analysis
This study compared the predictive efficacy of multiple machine learning models to identify the optimal model ... and Decision Curve Analysis (DCA) across XGBoost, Random Forest (RF), Logistic ...
IEEE21d
Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning
The NN component extracts high-level features, while XGBoost uses gradient-boosted decision trees for accurate predictions, combining the strengths of deep learning and boosting ... Support vector ...
BMJ23d
Prediction of zero-dose children using supervised machine learning algorithm in Tanzania: evidence from the recent 2022 Tanzania Demographic and Health Survey
Objectives This study aimed to employ machine learning ... instance-based learning algorithm that classifies data points based on the classes of their nearest neighbours, making it inherently simple ...
Frontiers26d
Machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarction
Flow chart of the study ... (28) demonstrated the potential of machine learning in predicting HF after AMI. Li et al. compared seven ML algorithms and identified XGBoost as the best-performing model ...