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Deep neural networks (DNNs) have demonstrated remarkable success across a wide range of machine learning tasks. However, determining an effective network architecture, particularly the sizes of the hidden layers, remains a significant challenge and often relies on inefficient trial-and-error experimentation. In this paper, we propose an automated architecture design approach based on structurally adaptive DNNs,…

In healthcare, accurate communication is critical, and medical coding, especially coding using the ICD (International Classification of Diseases) standards, plays a vital role in achieving this accuracy. Traditionally, ICD coding has been a time-consuming manual process performed by trained professionals, involving the assignment of codes to patient records, such as doctor’s notes. In this paper,…

Machine learning-based predictive models often face challenges, particularly biases and a lack of trust in their predictions when deployed by individual agents. Establishing a robust deployment methodology that supports validating the accuracy and fairness of these models is a critical endeavor. In this paper, we introduce a novel approach to deploying predictive models, such as…

Blockchain technology has been successfully applied in many fields for immutable and secure data storage. However, for applications with on-chain big data, blockchain scalability remains to be a main concern. In this paper, we propose a novel scalable storage scheme, called HistoChain, for a consortium blockchain network to manage blockchain data. We use a current…

Blockchain technology is seeing a trend of popularity and adoption in many different application areas. One such area is healthcare, as there is a need to develop a system that can reliably store and share electronic health records (EHRs) among hospital-based health facilities. In this paper, we present a cloud-based hierarchical consortium blockchain framework for…