Application Proficiency
Application proficiency focuses on optimizing the performance and efficiency of algorithms and models across diverse applications, aiming to improve accuracy, speed, and resource utilization. Current research emphasizes developing robust methods for handling model uncertainties and constraints, often employing Bayesian optimization, metaheuristics, and deep learning architectures like convolutional neural networks and transformers. This field is crucial for advancing various domains, from real-time control systems and fraud detection to personalized medicine and environmental monitoring, by enabling the effective deployment of sophisticated computational tools.
900papers
Papers - Page 19
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Comparison of Machine Learning Classification Algorithms and Application to the Framingham Heart Study
Quantum Theory and Application of Contextual Optimal Transport
Towards Spatially-Lucid AI Classification in Non-Euclidean Space: An Application for MxIF Oncology Data
A Bio-Medical Snake Optimizer System Driven by Logarithmic Surviving Global Search for Optimizing Feature Selection and its application for Disorder Recognition
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