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p-Index From 2021 - 2026
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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Indonesian Journal of Electrical Engineering and Computer Science
R, Prabhu
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Collaborative Healthcare Data Management Framework using Parallel Computing and the Internet of Things D, Shamia; M, Ephin; Yalagi, Pratibha C. Kaladeep; Chowdhury , Rini; Prashant Kumar; R, Prabhu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.611

Abstract

Healthcare data management has become a critical research area, primarily driven by the widespread adoption of personal health monitoring systems and applications. These systems generate an immense volume of data, necessitating efficient and reliable management solutions for lossless sharing. This article introduces a Collaborative Data Management Framework (CDMF) that leverages the combined strengths of parallel computing and federated learning. The proposed CDMF is designed to achieve two primary objectives: reducing computational complexity in data handling and ensuring high sharing accuracy, regardless of the data generation rate. The framework employs parallel computing to streamline the scheduling and processing of data acquired at various intervals. This approach minimizes processing delays by operating on a less complex scheduling algorithm, making it suitable for handling high-frequency data generation. Federated learning, on the other hand, plays a pivotal role in verifying data distribution and maintaining sharing accuracy. By enabling decentralized learning, federated learning ensures that data remains on local devices while sharing only the necessary model updates. This approach enhances privacy and security, a critical consideration in healthcare data management. It ensures that data distribution and sharing are verified based on appropriate requests while avoiding latency issues. By decentralizing the learning process, federated learning enhances privacy and security, as raw data does not leave the local systems. This cooperative interaction between parallel computing and federated learning operates in a cyclic manner, allowing the framework to adapt dynamically to increasing monitoring intervals and varying data rates. The performance of the CDMF is validated through improvements in two key metrics. First, the framework achieves a 15.08% enhancement in sharing accuracy, which is vital for maintaining data integrity and reliability during transfers. Second, it reduces computation complexity by 9.48%, even when handling maximum data rates. These results highlight the framework’s potential to revolutionize healthcare data management by addressing the dual challenges of scalability and accuracy.
An investigation of different low-power circuits and enhanced energy efficiency in medical applications R, Prabhu; Rajagopal, Sivakumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 2: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i2.pp478-493

Abstract

This research investigates the application of low-power circuits in medical devices and imaging systems. The primary goal is to address the growing demand for energy-efficient solutions in medical applications. There is an increasing need for energy-efficient solutions due to the development of medical technologies, particularly implanted and battery-operated medical devices. This paper explores the integration of adiabatic logic as a critical enabler for achieving low power consumption in medical applications. The study looks into different low-power circuit designs and technologies that optimize power usage without sacrificing performance. Adiabatic circuits offer a promising substitute for conventional circuitry in low-energy design. The research examines several low-power circuit designs and technologies that maximize power efficiency without compromising functionality. In low-energy design, adiabatic circuits present a possible alternative to traditional circuitry. Adiabatic logic aims to create energy-efficient digital circuits that consume significantly less power than conventional complementary metal-oxide-semiconductor (CMOS) circuits. We accomplish this by recovering and recycling energy that would otherwise be lost as heat and carefully controlling energy flows during switching events. Adiabatic logic is precious in battery-operated and energy-constrained devices.
Co-Authors Chowdhury , Rini D, Shamia M, Ephin Prashant Kumar Rajagopal, Sivakumar Yalagi, Pratibha C. Kaladeep