Abstract: Agricultural remote sensing community is increasingly focusing on enhancing crop mapping accuracy by improving data-driven machine-learning model structures, yet ignoring impact of ...
Quantum machine learning is a hybrid approach that combines classical data with quantum computing methods. In classical computing, data is stored in bits encoded as a 0 or 1. Quantum computers use ...
Abstract: Federated Learning (FL) in symbiotic IoT networks is a promising collaborative paradigm that utilizes IoT devices to co-train machine learning models, promising to accelerate edge ...
ABSTRACT: Machine learning (ML) has revolutionized risk management by enabling organizations to make data-driven decisions with higher accuracy and speed. However, as machine learning models grow more ...
Optimizing public health management with predictive analytics: leveraging the power of random forest
Community health outcomes significantly impact older populations' wellbeing and quality of life. Traditional analytical methods often struggle to accurately predict health risks at the community level ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. In early-stage drug design, machine learning models often rely on compressed ...
Background: Sepsis associated encephalopathy (SAE) is prevalent among elderly patients in the ICU and significantly affects patient prognosis. Due to the symptom similarity with other neurological ...
Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. To the best of our knowledge, no published literature has ...
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