Using machine learning for dynamic authentication in telehealth: A tutorial
Telehealth systems have evolved into more prevalent services that can serve people in
remote locations and at their homes via smart devices and 5G systems. Protecting the …
remote locations and at their homes via smart devices and 5G systems. Protecting the …
Automated feature selection: A reinforcement learning perspective
Feature selection is a critical step in machine learning that selects the most important
features for a subsequent prediction task. Effective feature selection can help to reduce …
features for a subsequent prediction task. Effective feature selection can help to reduce …
Application of reinforcement learning for security enhancement in cognitive radio networks
Cognitive radio network (CRN) enables unlicensed users (or secondary users, SUs) to
sense for and opportunistically operate in underutilized licensed channels, which are owned …
sense for and opportunistically operate in underutilized licensed channels, which are owned …
AdaFS: Adaptive feature selection in deep recommender system
Feature selection plays an impactful role in deep recommender systems, which selects a
subset of the most predictive features, so as to boost the recommendation performance and …
subset of the most predictive features, so as to boost the recommendation performance and …
A deep reinforcement learning based multi-criteria decision support system for optimizing textile chemical process
Textile manufacturing is a typical traditional industry involving high complexity in
interconnected processes with limited capacity on the application of modern technologies …
interconnected processes with limited capacity on the application of modern technologies …
A survey of privacy risks and mitigation strategies in the Artificial Intelligence life cycle
Over the decades, Artificial Intelligence (AI) and machine learning has become a
transformative solution in many sectors, services, and technology platforms in a wide range …
transformative solution in many sectors, services, and technology platforms in a wide range …
Multi-objective optimization of the textile manufacturing process using deep-Q-network based multi-agent reinforcement learning
Multi-objective optimization, such as quality, productivity, and cost, of the textile
manufacturing process is increasingly challenging because of the growing complexity …
manufacturing process is increasingly challenging because of the growing complexity …
MAFSIDS: a reinforcement learning-based intrusion detection model for multi-agent feature selection networks
K Ren, Y Zeng, Y Zhong, B Sheng, Y Zhang - Journal of Big Data, 2023 - Springer
Large unbalanced datasets pose challenges for machine learning models, as redundant
and irrelevant features can hinder their effectiveness. Furthermore, the performance of …
and irrelevant features can hinder their effectiveness. Furthermore, the performance of …
Automating feature subspace exploration via multi-agent reinforcement learning
Feature selection is the preprocessing step in machine learning which tries to select the
most relevant features for the subsequent prediction task. Effective feature selection could …
most relevant features for the subsequent prediction task. Effective feature selection could …
Autofs: Automated feature selection via diversity-aware interactive reinforcement learning
In this paper, we study the problem of balancing effectiveness and efficiency in automated
feature selection. Feature selection is to find the optimal feature subset from large-scale …
feature selection. Feature selection is to find the optimal feature subset from large-scale …