Publications by authors named "Anshul Verma"
Quantum computing and machine learning convergence enable powerful new approaches for optimizing mobile edge computing (MEC) networks. This paper uses Lyapunov optimization theory to propose a novel quantum machine learning framework for stabilizing computation offloading in next-generation MEC systems. Our approach leverages hybrid quantum-classical neural networks to learn optimal offloading policies that maximize network performance while ensuring the stability of data queues, even under dynamic and unpredictable network conditions.
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- Neurodegenerative disorders like Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) affect brain function, and advanced MRI techniques are essential for diagnosing these conditions by spotting structural changes.
- This study harnesses the ADNI and OASIS datasets, using deep learning models to differentiate between AD, Mild Cognitive Impairment, and cognitively normal individuals through various classification tests.
- The research highlights the effectiveness of deep learning architectures, especially ResNet-101, which achieved high accuracy rates (98.21% on ADNI and 97.45% on OASIS) in classifying AD and MCI, demonstrating the potential of these models for improving clinical diagnostics.
The temporal trends and preprocedural predictors of emergency coronary artery bypass graft surgery (ECABG) after elective percutaneous coronary intervention (PCI) in the contemporary era are largely unknown. From January 2003 to December 2014 elective hospitalizations with PCI as the primary procedure were extracted from the Nationwide Inpatient Sample. ECABG was identified as CABG within 24 hours of elective PCI.
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