All Outputs (10)

From accuracy to vulnerability: Quantifying the impact of adversarial perturbations on healthcare AI models (2025)
Journal Article

As AI becomes indispensable in healthcare, its vulnerability to adversarial attacks demands serious attention. Even minimal changes to the input data can mislead Deep Learning (DL) models, leading to critical errors in diagnosis and endangering patie... Read More about From accuracy to vulnerability: Quantifying the impact of adversarial perturbations on healthcare AI models.

AI under attack: Metric-driven analysis of cybersecurity threats in deep learning models for healthcare applications (2025)
Journal Article

Incorporating Artificial Intelligence (AI) in healthcare has transformed disease diagnosis and treatment by offering unprecedented benefits. However, it has also revealed critical cybersecurity vulnerabilities in Deep Learning (DL) models, which rais... Read More about AI under attack: Metric-driven analysis of cybersecurity threats in deep learning models for healthcare applications.

Explainable AI in medical imaging: An interpretable and collaborative federated learning model for brain tumor classification (2025)
Journal Article

Introduction: A brain tumor is a collection of abnormal cells in the brain that can become life-threatening due to its ability to spread. Therefore, a prompt and meticulous classification of the brain tumor is an essential element in healthcare care.... Read More about Explainable AI in medical imaging: An interpretable and collaborative federated learning model for brain tumor classification.

Meta-verse assisted healthcare body sensor network architecture (2024)
Presentation / Conference Contribution

These days, the usage of body area network enabled healthcare services has been increasing widely in practice. The objective is to monitor healthcare in real time from different remote services. However, the existing body area network architecture fo... Read More about Meta-verse assisted healthcare body sensor network architecture.

Heart patient health monitoring system using invasive and non-invasive measurement (2024)
Journal Article

The abnormal heart conduction, known as arrhythmia, can contribute to cardiac diseases that carry the risk of fatal consequences. Healthcare professionals typically use electrocardiogram (ECG) signals and certain preliminary tests to identify abnorma... Read More about Heart patient health monitoring system using invasive and non-invasive measurement.

Advancing DDoS attack detection with hybrid deep learning: integrating convolutional neural networks, PCA, and vision transformers (2024)
Journal Article

Distributed denial of service (DDoS) attacks pose a significant security risk, particularly with the increasing reliance on cloud computing and information technology (IT). These attacks not only allow unauthorized users to access services but also d... Read More about Advancing DDoS attack detection with hybrid deep learning: integrating convolutional neural networks, PCA, and vision transformers.

Survey improving usability of the smartphones for elders (2023)
Journal Article

The focal point of this study is the usability of smartphones for elderly individuals. Notably, Android dominates the current smartphone market share at 72.72%. This prevalence is influenced by various factors, including affordability, a diverse arra... Read More about Survey improving usability of the smartphones for elders.

Novel framework for efficient detection of QRS morphology for the cardiac arrhythmia classification (2023)
Journal Article

The abnormal conduction or disturbance in the cardiac activity is called cardiac arrhythmia except for sinus rhythm. Cardiac arrhythmias are placing a significant strain on the healthcare system as a result of the rising mortality rate in the world.... Read More about Novel framework for efficient detection of QRS morphology for the cardiac arrhythmia classification.

A fully automatic model for premature ventricular heartbeat arrhythmia classification using the Internet of Medical Things (2023)
Journal Article

Cardiac arrhythmias are one of the leading causes of increased mortality worldwide and place a heavy burden on the medical environment. Premature ventricular contraction is the disturbance in electrical activity which is the most dangerous arrhythmia... Read More about A fully automatic model for premature ventricular heartbeat arrhythmia classification using the Internet of Medical Things.

Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud (2021)
Journal Article

These days, fog-cloud based healthcare application partitioning techniques have been growing progressively. However, existing static fog-cloud based application partitioning methods are static and cannot adopt dynamic changes in the dynamic environme... Read More about Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud.