Ransomware: Recent advances, analysis, challenges and future research directions
C Beaman, A Barkworth, TD Akande, S Hakak… - Computers & …, 2021 - Elsevier
The COVID-19 pandemic has witnessed a huge surge in the number of ransomware attacks.
Different institutions such as healthcare, financial, and government have been targeted …
Different institutions such as healthcare, financial, and government have been targeted …
Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions
With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
When the curious abandon honesty: Federated learning is not private
In federated learning (FL), data does not leave personal devices when they are jointly
training a machine learning model. Instead, these devices share gradients, parameters, or …
training a machine learning model. Instead, these devices share gradients, parameters, or …
Handling privacy-sensitive medical data with federated learning: challenges and future directions
Recent medical applications are largely dominated by the application of Machine Learning
(ML) models to assist expert decisions, leading to disruptive innovations in radiology …
(ML) models to assist expert decisions, leading to disruptive innovations in radiology …
A review of privacy enhancement methods for federated learning in healthcare systems
Federated learning (FL) provides a distributed machine learning system that enables
participants to train using local data to create a shared model by eliminating the requirement …
participants to train using local data to create a shared model by eliminating the requirement …
Adoption of federated learning for healthcare informatics: Emerging applications and future directions
The smart healthcare system has improved the patients quality of life (QoL), where the
records are being analyzed remotely by distributed stakeholders. It requires a voluminous …
records are being analyzed remotely by distributed stakeholders. It requires a voluminous …
Machine-learning-based IoT–edge computing healthcare solutions
AK Alnaim, AM Alwakeel - Electronics, 2023 - mdpi.com
The data that medical sensors collect can be overwhelming, making it challenging to glean
the most relevant insights. An algorithm for a body sensor network is needed for the purpose …
the most relevant insights. An algorithm for a body sensor network is needed for the purpose …
Detecting and mitigating the dissemination of fake news: Challenges and future research opportunities
Fake news is a major threat to democracy (eg, influencing public opinion), and its impact
cannot be understated particularly in our current socially and digitally connected society …
cannot be understated particularly in our current socially and digitally connected society …
Federated learning approach for early detection of chest lesion caused by COVID-19 infection using particle swarm optimization
DR Kandati, TR Gadekallu - Electronics, 2023 - mdpi.com
The chest lesion caused by COVID-19 infection pandemic is threatening the lives and well-
being of people all over the world. Artificial intelligence (AI)-based strategies are efficient …
being of people all over the world. Artificial intelligence (AI)-based strategies are efficient …
Federated learning for big data: A survey on opportunities, applications, and future directions
Big data has remarkably evolved over the last few years to realize an enormous volume of
data generated from newly emerging services and applications and a massive number of …
data generated from newly emerging services and applications and a massive number of …