Enhancing Security and Privacy in Cloud – Based Healthcare Data Through Machine Learning DOI
Aasheesh Shukla, Hemant Singh Pokhariya, Jacob J. Michaelson

et al.

Published: Dec. 29, 2023

It is becoming more and important for healthcare providers to protect the integrity security of sensitive medical data as they use cloud computing processing storage. This work explores field machine learning algorithms that are secure privacy-preserving when applied information in environments. We investigate sophisticated cryptography, federated learning, differentiating privacy techniques using an interpretive philosophy a method based on deduction. Our results highlight computational expense associated with cryptographic protocols, while also revealing their nuanced performance potential enabling calculations. Federated shown be effective collaborative model training, providing workable approach analysis over-dispersed datasets. Differential systems require careful parameter calibration because demonstrate delicate balance between value preservation.

Language: Английский

OPT-CO: Optimizing pre-trained transformer models for efficient COVID-19 classification with stochastic configuration networks DOI Creative Commons
Ziquan Zhu, Lu Liu, Robert C. Free

et al.

Information Sciences, Journal Year: 2024, Volume and Issue: 680, P. 121141 - 121141

Published: July 8, 2024

Building upon pre-trained ViT models, many advanced methods have achieved significant success in COVID-19 classification. Many scholars pursue better performance by increasing model complexity and parameters. While these can enhance performance, they also require extensive computational resources extended training times. Additionally, the persistent challenge of overfitting, due to limited dataset sizes, remains a hurdle. To address challenges, we proposed novel method optimize transformer models for efficient classification with stochastic configuration networks (SCNs), referred as OPT-CO. We two optimization methods: sequential (SeOp) parallel (PaOp), incorporating optimizers manner, respectively. Our without necessitating parameter expansion. introduced OPT-CO-SCN avoid overfitting problems through adoption random projection head augmentation. The experiments were carried out evaluate our based on publicly available datasets. Based evaluation results, superior, surpassing other state-of-the-art methods.

Language: Английский

Citations

13
CTBViT: A novel ViT for tuberculosis classification with efficient block and randomized classifier DOI Creative Commons
Siyuan Lu, Ziquan Zhu, Yao Tang

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 100, P. 106981 - 106981

Published: Oct. 4, 2024

Language: Английский

Citations

7
Enhancing Data Security and Privacy in Energy Applications: Integrating IoT and Blockchain Technologies DOI Creative Commons
Hari Mohan, Kaustubh Kumar Shukla, Lilia Tightiz

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(19), P. e38917 - e38917

Published: Oct. 1, 2024

Language: Английский

Citations

7
A Federated Learning Approach to Breast Cancer Prediction in a Collaborative Learning Framework DOI Open Access
Maram Fahaad Almufareh, Noshina Tariq, Mamoona Humayun

et al.

Healthcare, Journal Year: 2023, Volume and Issue: 11(24), P. 3185 - 3185

Published: Dec. 17, 2023

Breast cancer continues to pose a substantial worldwide public health concern, necessitating the use of sophisticated diagnostic methods enable timely identification and management. The present research utilizes an iterative methodology for collaborative learning, using Deep Neural Networks (DNN) construct breast detection model with high level accuracy. By leveraging Federated Learning (FL), this framework effectively combined knowledge data assets several healthcare organizations while ensuring protection patient privacy security. described in study showcases significant progress field diagnoses, maximum accuracy rate 97.54%, precision 96.5%, recall 98.0%, by optimum feature selection technique. Data augmentation approaches play crucial role decreasing loss improving performance. Significantly, F1-Score, comprehensive metric evaluating performance, turns out be 97%. This signifies notable advancement screening, fostering hope improved outcomes via increased reliability. highlights potential impact namely, FL, transforming detection. incorporation considerations diverse sources contribute early treatment cancer, hence yielding benefits patients on global scale.

Language: Английский

Citations

12
Information Modeling Technique to Decipher Research Trends of Federated Learning in Healthcare DOI Open Access

Rishu,

Vinay Kukreja, Shanmugasundaram Hariharan

et al.

The Open Neuroimaging Journal, Journal Year: 2025, Volume and Issue: 18(1)

Published: March 12, 2025

Aim The aim of this study is to determine the most prevalent types federated learning, discuss their uses in healthcare, highlight significant issues, and suggest methods for further research. Context When it comes handling distributed data, learning revolutionary, especially sensitive sectors like healthcare. In order improve outcomes growing number healthcare studies, there must be a method safely effectively analyze use enormous data. Objective purpose research large corpus 6,800 studies published between 2000 2024 apply topic modeling using Latent Semantic Analysis (LSA). Methods was analyzed LSA with goal identifying latent themes that capture spirit industry. provide an organized overview subject matter, five-topic solution devised. To guarantee relevance clarity, topics' coherence assessed. Results term frequency inverse document high-loading terms provided five major solutions. score achieved, i.e ., 0.789, indicating high level integration among identified topics. Different (FL), applications FL, key challenges possible associated FL have been analyzed. Conclusion This highlights significance privacy-preserving data analysis field, which may lead development creative solutions complex problems.

Language: Английский

Citations

0
Federated Learning in Real-Time Medical IoT: Optimizing Privacy and Accuracy for Chronic Disease Monitoring DOI Creative Commons

Et al. Dhairyashil Patil

Deleted Journal, Journal Year: 2024, Volume and Issue: 19(3), P. 32 - 42

Published: Jan. 25, 2024

The rising occurrence of long-term illnesses requires inventive and effective healthcare solutions, the incorporation Internet Things (IoT) technologies holds significant potential in revolutionizing conventional medical monitoring. This study presents an innovative method called Adaptive Federated Learning for Chronic Disease Prediction (AFL-CDP), which is specifically designed real-time applications. main objective to enhance both privacy accuracy surveillance chronic diseases. AFL-CDP utilizes federated learning, a decentralized approach machine learning that allows model training on multiple edge devices without need transfer raw data central server. not only mitigates concerns related sensitive but also improves precision predictive models by assimilating information from various sources. adaptability enables ongoing improvement using changing patient data, resulting personalized timely forecasts In order improve IoT with limited resources, integrates utilization SPECK, advanced technique preserving privacy. SPECK secure aggregation encryption mechanisms safeguard throughout process, guaranteeing confidentiality while integrity model. Ensuring security are utmost importance, particularly field IoT. proposed methodology assessed dataset consists purpose monitoring model's performance evaluated Area Under Curve (AUC) metric, achieves impressive AUC 94.37%. showcases efficacy framework capturing fundamental patterns varied data. To summarize, this strong applications, highlighting significance combination offers thorough satisfies strict demands high level precision, establishing basis enhanced results interventions.

Language: Английский

Citations

1
Secure and Privacy-Preserving Federated Learning With Explainable Artificial Intelligence for Smart Healthcare Systems DOI
Rita Komalasari

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 288 - 313

Published: April 19, 2024

With the escalating global population, healthcare sector faces unprecedented challenges, necessitating innovative solutions. Deep learning (DL) and federated (FL) have emerged as pivotal technologies, yet challenges persist in data privacy, security, model interpretability, especially applications. This research addresses these by proposing robust frameworks for secure, privacy-preserving with explainable artificial intelligence smart systems. The objective is to enhance performance, privacy of systems, ensuring their resilience effectiveness real-world scenarios. employs a literature approach. comprehensive approach establishes foundation future development fostering trust, transparency, efficiency decision-making processes.

Language: Английский

Citations

1
A Comparative Analysis of Federated Learning and Privacy-Preserving Techniques in Healthcare AI DOI
Yogesh Kumar Sharma, Deepika Ajalkar, Smitha Nayak

et al.

Advances in healthcare information systems and administration book series, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 14

Published: April 19, 2024

AI might conduct screening and assessment in the event that medical expertise is lacking a setting with limited resources. Because algorithms are involved, even most rapid decisions methodical comparison human decision-making. In this chapter, authors provide thorough literature review on data privacy for healthcare system development. order to facilitate safer translational research, they offer comprehensive of issues owners have when sharing datasets researchers. They go over many forms attacks how jeopardize user privacy. also into detail about several possible ways fix these problems.

Language: Английский

Citations

1
Enhancing the performance of CNN models for pneumonia and skin cancer detection using novel fractional activation function DOI
Meshach Kumar, Utkal Mehta

Applied Soft Computing, Journal Year: 2024, Volume and Issue: unknown, P. 112500 - 112500

Published: Nov. 1, 2024

Language: Английский

Citations

0
A Privacy-Preserving Federated Learning Framework for Financial Crime DOI
Abdul Haseeb, Idongesit Ekerete,

Samuel K. Moore

et al.

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 743 - 754

Published: Jan. 1, 2024

Language: Английский

Citations

0