[HTML][HTML] Privacy-preserving artificial intelligence in healthcare: Techniques and applications
There has been an increasing interest in translating artificial intelligence (AI) research into
clinically-validated applications to improve the performance, capacity, and efficacy of …
clinically-validated applications to improve the performance, capacity, and efficacy of …
Developing future human-centered smart cities: Critical analysis of smart city security, Data management, and Ethical challenges
As the globally increasing population drives rapid urbanization in various parts of the world,
there is a great need to deliberate on the future of the cities worth living. In particular, as …
there is a great need to deliberate on the future of the cities worth living. In particular, as …
Collaborative federated learning for healthcare: Multi-modal covid-19 diagnosis at the edge
Despite significant improvements over the last few years, cloud-based healthcare
applications continue to suffer from poor adoption due to their limitations in meeting stringent …
applications continue to suffer from poor adoption due to their limitations in meeting stringent …
Security and privacy of internet of medical things: A contemporary review in the age of surveillance, botnets, and adversarial ML
Abstract Internet of Medical Things (IoMT) supports traditional healthcare systems by
providing enhanced scalability, efficiency, reliability, and accuracy of healthcare services. It …
providing enhanced scalability, efficiency, reliability, and accuracy of healthcare services. It …
[HTML][HTML] Explainable, trustworthy, and ethical machine learning for healthcare: A survey
With the advent of machine learning (ML) and deep learning (DL) empowered applications
for critical applications like healthcare, the questions about liability, trust, and interpretability …
for critical applications like healthcare, the questions about liability, trust, and interpretability …
[HTML][HTML] Quantum computing for healthcare: A review
In recent years, the interdisciplinary field of quantum computing has rapidly developed and
garnered substantial interest from both academia and industry due to its ability to process …
garnered substantial interest from both academia and industry due to its ability to process …
A systematic review of federated learning incentive mechanisms and associated security challenges
In response to various privacy risks, researchers and practitioners have been exploring
different paradigms that can leverage the increased computational capabilities of consumer …
different paradigms that can leverage the increased computational capabilities of consumer …
Making federated learning robust to adversarial attacks by learning data and model association
One of the key challenges in federated learning (FL) is the detection of malicious parameter
updates. In a typical FL setup, the presence of malicious client (s) can potentially demolish …
updates. In a typical FL setup, the presence of malicious client (s) can potentially demolish …
Secure and trustworthy artificial intelligence-extended reality (AI-XR) for metaverses
Metaverse is expected to emerge as a new paradigm for the next-generation Internet,
providing fully immersive and personalised experiences to socialize, work, and play in self …
providing fully immersive and personalised experiences to socialize, work, and play in self …
[HTML][HTML] Optimized convolutional neural network architectures for efficient on-device vision-based object detection
I Rodriguez-Conde, C Campos… - Neural Computing and …, 2022 - Springer
Convolutional neural networks have pushed forward image analysis research and computer
vision over the last decade, constituting a state-of-the-art approach in object detection today …
vision over the last decade, constituting a state-of-the-art approach in object detection today …