Machine Learning Based Healthcare Service Dissemination Using Social Internet of Things and Cloud Architecture in Smart Cities

Kaliappan, Vishnu Kumar; Gnanamurthy, Sundharamurthy; Yahya, Abid; Samikannu, Ravi; Babar, Muhammad Ali; Qureshi, Basit; Koubâa, Anis

doi:10.3390/su15065457

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…In [35], a proposed model known as ICBS (Information Centric Dissemination Scheme) for smart cities to provide smart health is discussed. Jyothi Peta and Srinivas Koppu [36] introduced SPWO (Student Psychology Whale Optimization)-based deep max out network.…”

Section: Related Workmentioning

confidence: 99%

Secure Healthcare Model Using Multi-Step Deep Q Learning Network in Internet of Things

Roy,

Teju,

Kandula

et al. 2024

Electronics

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Internet of Things (IoT) is an emerging networking technology that connects both living and non-living objects globally. In an era where IoT is increasingly integrated into various industries, including healthcare, it plays a pivotal role in simplifying the process of monitoring and identifying diseases for patients and healthcare professionals. In IoT-based systems, safeguarding healthcare data is of the utmost importance, to prevent unauthorized access and intermediary assaults. The motivation for this research lies in addressing the growing security concerns within healthcare IoT. In this proposed paper, we combine the Multi-Step Deep Q Learning Network (MSDQN) with the Deep Learning Network (DLN) to enhance the privacy and security of healthcare data. The DLN is employed in the authentication process to identify authenticated IoT devices and prevent intermediate attacks between them. The MSDQN, on the other hand, is harnessed to detect and counteract malware attacks and Distributed Denial of Service (DDoS) attacks during data transmission between various locations. Our proposed method’s performance is assessed based on such parameters as energy consumption, throughput, lifetime, accuracy, and Mean Square Error (MSE). Further, we have compared the effectiveness of our approach with an existing method, specifically, Learning-based Deep Q Network (LDQN).

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Section: Related Workmentioning

confidence: 99%

Secure Healthcare Model Using Multi-Step Deep Q Learning Network in Internet of Things

Roy,

Teju,

Kandula

et al. 2024

Electronics

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“…Based on the empirical analysis, although the recent monetary easing policy led to the devaluation of the JPY and imported inflation, it contributed much to the export economy while the domestic consumer prices were kept low [50,[65][66][67][68][69]. Second, information technologies such as 5G, big data analytic engines, remote medical consultation, and applications of Artificial Intelligence (AI) are expected to suppress the operating costs [70][71][72][73][74][75]. These measures help in downsizing the scale of public hospitals and improve the cost effectiveness [76,77].…”

Section: Main Findings and Implicationsmentioning

confidence: 99%

Analysis of the Forces Driving Public Hospitals’ Operating Costs Using LMDI Decomposition: The Case of Japan

Kou,

Dou,

Arai

2024

Sustainability

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The sustainable management of public hospitals is usually threatened by long-term operating deficit, which was exacerbated during the COVID-19 pandemic. This study aimed to quantitatively decompose the historical changes in the annual operating costs of public hospitals in Japan to identify the main driving forces responsible for a worsening imbalance between operating costs and income over the past two decades. A dataset of the annual operating costs of public hospitals in Japan was compiled, in which influencing factors were redefined to make the data amenable to the application of a decomposition method referred to as the Logarithmic Mean Divisia Index (LMDI). Using the LMDI method, the contribution of each influencing factor to the changes in public hospital operating costs was quantitatively determined. The results indicate that, on average, there is an annual reduction in operating costs by JPY 9 million per hospital, arising out of the national reform of public hospitals, but the rapid increase in the prices and worsened structure of costs in recent years resulted in an annual increment of JPY 127 million per hospital to the increasing operating costs. The pandemic revealed damage to the financial balance of public hospitals, but epidemic prevention policies brought an offset to the increased operating cost. A more resilient domestic medical supply chain, the introduction of new technologies, and continuous endeavors in system reform and pricing policies are required to achieve financial sustainability in public hospitals in Japan.

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“…Also, to efficiently handle large data and deliver timely predictions, a network connection with high bandwidth and low latency is mandatory. An alternative approach is to use each client's data to train the ML model and then distribute copies of the trained model to each participant [20][21][22][23]. This way, data doesn't need to be moved when new insights are gained, as each owner has their model.…”

Section: Challengesmentioning

confidence: 99%

Investigating the impact of data heterogeneity on the performance of federated learning algorithm using medical imaging

Babar,

Qureshi,

Koubaa

2024

PLoS ONE

Self Cite

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In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.

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Machine Learning Based Healthcare Service Dissemination Using Social Internet of Things and Cloud Architecture in Smart Cities

Cited by 5 publications

References 20 publications

Secure Healthcare Model Using Multi-Step Deep Q Learning Network in Internet of Things

Secure Healthcare Model Using Multi-Step Deep Q Learning Network in Internet of Things

Analysis of the Forces Driving Public Hospitals’ Operating Costs Using LMDI Decomposition: The Case of Japan

Investigating the impact of data heterogeneity on the performance of federated learning algorithm using medical imaging

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