Enabling smart data: noise filtering in big data classification
In any knowledge discovery process the value of extracted knowledge is directly related to
the quality of the data used. Big Data problems, generated by massive growth in the scale of …
the quality of the data used. Big Data problems, generated by massive growth in the scale of …
Evidential reasoning approach with multiple kinds of attributes and entropy-based weight assignment
Multiple attribute decision making (MADM) problems often consists of quantitative and
qualitative attributes which can be assessed by numerical values and subjective judgments …
qualitative attributes which can be assessed by numerical values and subjective judgments …
A Deng-entropy-based evidential reasoning approach for multi-expert multi-criterion decision-making with uncertainty
The evidential reasoning (ER) approach has been widely applied to aggregate evaluation
information in multi-expert multicriterion decision-making (MEMCDM) problems with …
information in multi-expert multicriterion decision-making (MEMCDM) problems with …
Investigating random undersampling and feature selection on bioinformatics big data
This paper aims to address a key research issue regarding the ECBDL'14 bioinformatics big
data competition. The ECBDL'14 dataset was the big data target in the competition, and it …
data competition. The ECBDL'14 dataset was the big data target in the competition, and it …
Federated tensor decomposition-based feature extraction approach for industrial IoT
Y Gao, G Zhang, C Zhang, J Wang… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Data in modern industrial applications and data science present multidimensional
progressively, the dimension and the structural complexity of these data are becoming …
progressively, the dimension and the structural complexity of these data are becoming …
Prediction of the bilinear stress-strain curve of aluminum alloys using artificial intelligence and big data
D Merayo Fernández, A Rodríguez-Prieto… - Metals, 2020 - mdpi.com
Aluminum alloys are among the most widely used materials in demanding industries such
as aerospace, automotive or food packaging and, therefore, it is essential to predict the …
as aerospace, automotive or food packaging and, therefore, it is essential to predict the …
[HTML][HTML] An analysis on new hybrid parameter selection model performance over big data set
Parameter selection or attribute selection is one of the crucial tasks in the data analysis
process. Incorrect selection of the important attribute might generate imprecise or event for a …
process. Incorrect selection of the important attribute might generate imprecise or event for a …
Early detection of Alzheimer's disease using patient neuropsychological and cognitive data and machine learning techniques
Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of
dementia in older adults. With no known cures, there is a pressing need to find behavioral …
dementia in older adults. With no known cures, there is a pressing need to find behavioral …
Mini-batch algorithms with online step size
Mini-batch algorithms have been proposed as a way to speed-up stochastic optimization
methods and good results for mini-batch algorithms have been reported previously. A major …
methods and good results for mini-batch algorithms have been reported previously. A major …
An improved intelligent early warning method based on MWSPCA and its application in complex chemical processes
Z Geng, N Chen, Y Han, B Ma - The Canadian Journal of …, 2020 - Wiley Online Library
With the development of industrial automation, the requirement of abnormal early warning in
the industrial production process is getting higher and higher. Facing complex chemical …
the industrial production process is getting higher and higher. Facing complex chemical …