[HTML][HTML] Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013–2023)
Uncertainty estimation in healthcare involves quantifying and understanding the inherent
uncertainty or variability associated with medical predictions, diagnoses, and treatment …
uncertainty or variability associated with medical predictions, diagnoses, and treatment …
Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991–2020)
Understanding the data and reaching accurate conclusions are of paramount importance in
the present era of big data. Machine learning and probability theory methods have been …
the present era of big data. Machine learning and probability theory methods have been …
Feature-specific mutual information variation for multi-label feature selection
Recent years has witnessed urgent needs for addressing the curse of dimensionality
regarding multi-label data, which attracts wide attention for feature selection. Feature …
regarding multi-label data, which attracts wide attention for feature selection. Feature …
MBGAN: An improved generative adversarial network with multi-head self-attention and bidirectional RNN for time series imputation
Q Ni, X Cao - Engineering Applications of Artificial Intelligence, 2022 - Elsevier
Time series data is of great value in data mining and analysis, but it often comes with the
problem of data partly missing in many fields. So it is necessary to impute missing values …
problem of data partly missing in many fields. So it is necessary to impute missing values …
Bio-signals in medical applications and challenges using artificial intelligence
Artificial Intelligence (AI) has broadly connected the medical field at various levels of
diagnosis based on the congruous data generated. Different types of bio-signal can be used …
diagnosis based on the congruous data generated. Different types of bio-signal can be used …
R-HEFS: Rough set based heterogeneous ensemble feature selection method for medical data classification
Feature selection is one of the trustworthy processes of dimensionality reduction technique
to select a subset of relevant and non-redundant features from large datasets. Ensemble …
to select a subset of relevant and non-redundant features from large datasets. Ensemble …
Missing value imputation through shorter interval selection driven by Fuzzy C-Means clustering
The presence of missing data is a common and pivotal issue, which generally leads to a
serious decrease of data quality and thus indicates the necessity to effectively handle …
serious decrease of data quality and thus indicates the necessity to effectively handle …
Fusing attribute reduction accelerators
Y Chen, X Yang, J Li, P Wang, Y Qian - Information Sciences, 2022 - Elsevier
In the fields of rough set and machine learning, attribute reduction has been demonstrated to
be effective in removing redundant attributes with clear explanations. Therefore, not only the …
be effective in removing redundant attributes with clear explanations. Therefore, not only the …
Data classification using rough set and bioinspired computing in healthcare applications-an extensive review
N Kumari, DP Acharjya - Multimedia Tools and Applications, 2023 - Springer
The change in living standard made people to think on their physical health. Accordingly,
healthcare organizations are concentrating more on physical health of people in terms of …
healthcare organizations are concentrating more on physical health of people in terms of …
Chiller system optimization using k nearest neighbour regression
This study applies the k nearest neighbour (kNN) regression to ascertain optimal operating
strategies for a chiller system, hence lowering its carbon emissions. First, 19 operating …
strategies for a chiller system, hence lowering its carbon emissions. First, 19 operating …