Federated Learning for Privacy-Preserving Healthcare Data Analysis in the Age of Cybersecurity Threats DOI
Padala Sravan,

S. Saranya,

N M Deepika

et al.

Published: Dec. 29, 2023

This examination explores joined picking up gathering appraisals, unequivocally United Averaging (FedAvg), Weighted Consolidated (FedAvg-W), Bound together Learning with Adaptable Rate (FedAdapt), and Secure Combination for Brought (SecAgg), inside the space of assertion saving clinical benefits data assessment. The reason organized assessments was to assess their performance in terms accuracy, evidence coverage communication speed. article provides a comparative evaluation help practitioners select most appropriate algorithm reasoning applications. results show that FedAvg-W achieves much higher accuracy than other algorithms especially when used locations varying attributes implying it can adapt changes. In relation this, method called FedAdapt mixes quickly while maintaining high by way dynamically changing learning rate limits respect particular instances distribution information. A secure aggregation framework based on homomorphic encryption guarantees exact compliance. review subtle experiences into space-related works, such as health informatics federated learning. On one hand, SecAgg fulfills basic requirement ensuring preserving medical side, FedAdapt's flexibility concerns anticipated scalability

Language: Английский

The Future of Healthcare with Industry 5.0: Preliminary Interview-Based Qualitative Analysis DOI Creative Commons
Juliana Basulo-Ribeiro, Leonor Teixeira

Future Internet, Journal Year: 2024, Volume and Issue: 16(3), P. 68 - 68

Published: Feb. 22, 2024

With the advent of Industry 5.0 (I5.0), healthcare is undergoing a profound transformation, integrating human capabilities with advanced technologies to promote patient-centered, efficient, and empathetic ecosystem. This study aims examine effects on healthcare, emphasizing synergy between experience technology. To this end, 6 specific objectives were found, which answered in results through an empirical based interviews 11 professionals. article thus outlines strategic policy guidelines for integration I5.0 advocating policy-driven change, contributes literature by offering solid theoretical basis its impact sector.

Language: Английский

Citations

19
Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey DOI
Mohammed A. A. Al‐qaness,

Jie Zhu,

Dalal AL-Alimi

et al.

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(6), P. 3267 - 3301

Published: Feb. 19, 2024

Language: Английский

Citations

12
Federated Learning in Agents Based Cyber-Physical Systems DOI

Domenico Di Sivo,

Palma Errico,

Salvatore Venticinque

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

1
Empowering Dataspace 4.0: Unveiling Promise of Decentralized Data-Sharing DOI Creative Commons
Saeed Hamood Alsamhi, Ammar Hawbani, Santosh Kumar

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 112637 - 112658

Published: Jan. 1, 2024

Language: Английский

Citations

4
A Systematic Survey of Distributed Decision Support Systems in Healthcare DOI Creative Commons
Basem Almadani,

Hunain Kaisar,

Irfan Rashid Thoker

et al.

Systems, Journal Year: 2025, Volume and Issue: 13(3), P. 157 - 157

Published: Feb. 26, 2025

The global Internet of Medical Things (IoMT) market is growing at a Compound Annual Growth Rate (CAGR) 17.8%, testament to the increasing demand for IoMT in health sector. However, more devices mean an increase volume and velocity data received by healthcare decision-makers, leading many develop Distributed Decision Support Systems (DDSSs) help them make accurate timely decisions. This research systematic review DDSSs using Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) framework. study explores how advanced technologies such as Artificial Intelligence (AI), IoMT, blockchain enhance clinical decision-making processes. It highlights key innovations DDSSs, including hybrid imaging techniques comprehensive disease characterization. also examines role Case-Based Reasoning (CBR) frameworks improving personalized treatment strategies chronic diseases like diabetes mellitus. presents challenges applying sector, security privacy, system integration, interoperability issues. Finally, it discusses open issues future directions field structure standardization, alert fatigue workers lack adherence emerging medical regulations.

Language: Английский

Citations

0
State of the art and taxonomy survey on federated learning and blockchain integration in UAV applications DOI

Hela Alaya,

Asma Ben Letaïfa, Abderrezak Rachedi

et al.

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(5)

Published: March 24, 2025

Language: Английский

Citations

0
Artificial intelligence in COVID-19 research: A comprehensive survey of innovations, challenges, and future directions DOI

Richard Annan,

Letu Qingge

Computer Science Review, Journal Year: 2025, Volume and Issue: 57, P. 100751 - 100751

Published: April 4, 2025

Language: Английский

Citations

0
From AI to the Era of Explainable AI in Healthcare 5.0: Current State and Future Outlook DOI
Anichur Rahman,

Dipanjali Kundu,

Tanoy Debnath

et al.

Expert Systems, Journal Year: 2025, Volume and Issue: 42(6)

Published: April 29, 2025

ABSTRACT Artificial intelligence (AI) and explainable artificial (XAI) are advancing rapidly, with the potential to deliver significant benefits modern society. The healthcare sector, in particular, has experienced transformative changes; overall, these technologies helping address numerous challenges, such as cancer cell detection, tumour zone identification animal bodies, predictions of major minor diseases, diagnosis, more. This article provides an in‐depth detailed overview AI XAI, focusing on recent trends their implications for Healthcare 5.0 applications. Initially, study examines key concepts exceptional features AI, 5.0. Additional emphasis is placed state‐of‐the‐art practices currently being implemented healthcare, particularly those involving XAI. Subsequently, it establishes a coherent link between XAI 5.0, grounded contemporary advancements. Based findings, algorithms recommended initial obstacles integrating into framework. Proposals further enhancing performance through integration its unique discussed detail. work also implementation strategies highlights model‐specific within frameworks Particular attention given model settings, emphasising contributions improved patient feedback delivery more sophisticated care. Most importantly, this research support sustainable advancements Finally, issues analysed, open discussion presented future guidelines blending

Language: Английский

Citations

0
Examining medical urgent and patient precedence within the telemedicine landscape with its infrastructure and policies: A comprehensive review DOI Creative Commons
Geetika M. Patel, Upendra Sharma, Sudhir Kumar Gupta

et al.

Multidisciplinary Reviews, Journal Year: 2024, Volume and Issue: 6, P. 2023ss017 - 2023ss017

Published: Jan. 30, 2024

Medical facilities are confronted with grave issues, including a population that is aging and physician shortage. In an effort to address these telehealth remote health monitoring systems (RHMS) aim reduce hospital visits by small amount. RHMS lessens the workload for primary care patients enhances inter-unit communication, which strain on emergency rooms. Due significant advancements in mobile information transfer processing of signal technologies, some healthcare researchers have made efforts employ place provide triage prioritization individuals. Prioritization strategy giving people urgent medical attempt save their lives, while clinical examination determines severity sickness or damage. To emphasize disadvantages present patient screening system over environment, crucial inquiry needed. Based two axes, in-depth crisis assessment videoconferencing setting was provided this research. First, collection, analysis, classification earlier research kind done. Second, variety priorities standards, as well various approaches procedures prioritization, were examined. The subsequent outcomes attained: shortcomings issues current methods highlighted. priority individuals who cardiac conditions not presented. years come, structure based theory evidence, incorporation multiple-layer analytical hierarchy approach method ranking preference due resemblance perfect solutions, can be used prioritize several ongoing cardiovascular disease triaging them emergencies dimensions.

Language: Английский

Citations

0
Federated Learning for Privacy-Preserving Healthcare Data Analysis in the Age of Cybersecurity Threats DOI
Padala Sravan,

S. Saranya,

N M Deepika

et al.

Published: Dec. 29, 2023

This examination explores joined picking up gathering appraisals, unequivocally United Averaging (FedAvg), Weighted Consolidated (FedAvg-W), Bound together Learning with Adaptable Rate (FedAdapt), and Secure Combination for Brought (SecAgg), inside the space of assertion saving clinical benefits data assessment. The reason organized assessments was to assess their performance in terms accuracy, evidence coverage communication speed. article provides a comparative evaluation help practitioners select most appropriate algorithm reasoning applications. results show that FedAvg-W achieves much higher accuracy than other algorithms especially when used locations varying attributes implying it can adapt changes. In relation this, method called FedAdapt mixes quickly while maintaining high by way dynamically changing learning rate limits respect particular instances distribution information. A secure aggregation framework based on homomorphic encryption guarantees exact compliance. review subtle experiences into space-related works, such as health informatics federated learning. On one hand, SecAgg fulfills basic requirement ensuring preserving medical side, FedAdapt's flexibility concerns anticipated scalability

Language: Английский

Citations

0