federated learning

Cloud-based solutions for sustainable biomedical data processing and management

The rapid advancement of big data analytics is beginning to significantly impact the progress of medical research and practice, enabling data-driven decision-making and innovation. It has led to the creation of tools for collecting, organizing, processing, and integrating vast amounts of diverse, structured, and unstru...

Multimodal machine learning approaches for heart disease detection and control

Heart disease is one of the major causes of death around the world. This is due to the high and rising number of smokers and other factors. Developing a novel system capable of predicting heart diseases is necessary to control this health hazard. An electrocardiogram (ECG) dataset is utilized to build the machine learn...

Secure Federated Learning Based on the OPTICS Clustering Algorithm and Client Selection

Abstract In Federated Learning (FL), the aggregation servers inevitably face attacks from malicious clients because they cannot directly constrain the behaviour of clients. Effectively detecting and mitigating the impact of these malicious clients on the global model is a critical challenge. This paper proposes a secur...

UAG-MF: Uncertainty-aware generative multimodal fusion for continuous pain estimation in non-verbal patients under clinical occlusions

Official code repository for UAG-MF. Contains training scripts, model implementations, LOSO cross-validation, ablation studies, federated learning simulation, and figure generation code. All results reported in the manuscript are fully reproducible.

Communication-efficient federated learning for edge IoT via combined quantization and sparsification

This paper implements a practical, communication-efficient FL framework that combines update quantization with top-k sparsification with error feedback and evaluates its performance under realistic edge conditions, and provides reproducible MATLAB code and result artifacts.

UAG-MF: Uncertainty-aware generative multimodal fusion for continuous pain estimation in non-verbal patients under clinical occlusions

Official code repository for UAG-MF. Contains training scripts, model implementations, LOSO cross-validation, ablation studies, federated learning simulation, and figure generation code. All results reported in the manuscript are fully reproducible.

Artificial Intelligence–Driven Digital Twins, Multi-Omics and Precision Hepatitis Care: Emerging Innovations, Challenges, and Future Perspectives

Due to persistent diagnostic delays, varied disease progression, and unpredictable therapy responses, viral hepatitis remains a major worldwide health concern despite advances in antiviral medication and vaccine. Precision hepatology is a viable strategy for overcoming these limitations through tailored, data-driven cl...

Artificial Intelligence–Driven Digital Twins, Multi-Omics and Precision Hepatitis Care: Emerging Innovations, Challenges, and Future Perspectives

Due to persistent diagnostic delays, varied disease progression, and unpredictable therapy responses, viral hepatitis remains a major worldwide health concern despite advances in antiviral medication and vaccine. Precision hepatology is a viable strategy for overcoming these limitations through tailored, data-driven cl...

Next-Generation Deep Learning: A Comprehensive Survey on Explainable, Efficient, Privacy-Preserving and Multimodal Artificial Intelligence

The continuous evolution of deep learning has significantly expanded the capabilities of artificial intelligence, enabling intelligent systems to solve increasingly complex problems across healthcare, computer vision, natural language processing, cybersecurity, finance, autonomous systems, and smart environments.Beginn...

FOIN: A Federated Multi-Organoid Intelligence Architecture using Combinatorial Graph Modelling

Open research challenges and future directions for multimodal healthcare security

The swift advancement of multimodal artificial intelligence (AI) has changed the face of integration of diverse data types, such as medical images, texts, biosignals, genomics, wearables, and clinical data in the field of medicine. However, the deployment of reliable, secure, and scaled multimodal healthcare systems is...

AI-driven cybersecurity for industrial internet of things: architectures, challenges, datasets, and future research directions

This review critically analyzes the cybersecurity research published over the past few years on cyber threats across the various layers of the IIoT architecture, publicly available cybersecurity datasets, evaluation practices, and AI-based intrusion detection methods to provide a pathway toward resilient, adaptive, and...