Systematic review of advanced AI methods for improving healthcare data quality in post COVID-19 Era
At the beginning of the COVID-19 pandemic, there was significant hype about the potential
impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or …
impact of artificial intelligence (AI) tools in combatting COVID-19 on diagnosis, prognosis, or …
Improving the performance of the intrusion detection systems by the machine learning explainability
QV Dang - International Journal of Web Information Systems, 2021 - emerald.com
Purpose This study aims to explain the state-of-the-art machine learning models that are
used in the intrusion detection problem for human-being understandable and study the …
used in the intrusion detection problem for human-being understandable and study the …
[HTML][HTML] Towards a deep learning-based outlier detection approach in the context of streaming data
Uncommon observations that significantly vary from the norm are referred to as outliers.
Outlier detection, which aims to detect unexpected behavior, is a critical topic that has …
Outlier detection, which aims to detect unexpected behavior, is a critical topic that has …
Gaussian projection deep extreme clustering and chebyshev reflective correlation based outlier detection
S Rajalakshmi, P Madhubala - International Journal of …, 2022 - search.proquest.com
Outlier detection or simply the task of point detection that are noticeably distinct and different
from data sample is a predominant issue in deep learning. When a framework is …
from data sample is a predominant issue in deep learning. When a framework is …