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 4 publications

(1 citation statement)

References 20 publications

(22 reference statements)

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

Babar,

Qureshi,

Koubaa

2024

PLoS ONE

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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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Performance Monitoring of Hybrid All-Optical Fiber/FSO Communication Systems

Esmail

2023

Applied Sciences

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The demand for network capacity has increased due to the introduction of new digital applications and services, which rely heavily on optical communication networks. While fiber networks serve as the optical networks’ backbone, deploying fiber in certain scenarios is not feasible, making it necessary to use other technologies conjointly. A hybrid all-optical fiber/free space optic (FSO) link is proposed to avoid such a challenge. The all-optical system avoids using electronics that have limited bandwidth. Hence, it supports high-capacity communication. However, the all-optical system comes with challenges arising from fiber and FSO channel impairments. To monitor the amount and type of distortion in the optical channel, machine learning (ML) techniques are exploited. In this work, Gaussian process regression (GPR) is utilized as an ML technique to predict three main channel impairments that arise in the hybrid all-optical fiber/FSO channels, which are turbulence, optical signal-to-noise ratio (OSNR), and chromatic dispersion (CD). The model’s performance is evaluated using boxplot graphs, root mean square error (RMSE) metric, and R-squared metric. The results indicate that the model can predict the various impairments with high accuracy, except under strong amplified spontaneous emission (ASE) noise, where the model demonstrated lower accuracy in predicting light turbulence parameters. The proposed approach provides a self-aware and self-adaptive communication system and can optimize network resources in the future.

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

Cited by 4 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

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

Performance Monitoring of Hybrid All-Optical Fiber/FSO Communication Systems

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