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Machine learning and data mining in manufacturing
Manufacturing organizations need to use different kinds of techniques and tools in order to
fulfill their foundation goals. In this aspect, using machine learning (ML) and data mining …
fulfill their foundation goals. In this aspect, using machine learning (ML) and data mining …
A state-of-the-art review on the utilization of machine learning in nanofluids, solar energy generation, and the prognosis of solar power
In the contemporary data-driven era, the fields of machine learning, deep learning, big data,
statistics, and data science are essential for forecasting outcomes and getting insights from …
statistics, and data science are essential for forecasting outcomes and getting insights from …
[HTML][HTML] Post-hoc explanation of black-box classifiers using confident itemsets
Abstract Black-box Artificial Intelligence (AI) methods, eg deep neural networks, have been
widely utilized to build predictive models that can extract complex relationships in a dataset …
widely utilized to build predictive models that can extract complex relationships in a dataset …
Proposing a classifier ensemble framework based on classifier selection and decision tree
One of the most important tasks in pattern, machine learning, and data mining is
classification problem. Introducing a general classifier is a challenge for pattern recognition …
classification problem. Introducing a general classifier is a challenge for pattern recognition …
Identification of membrane protein types via multivariate information fusion with Hilbert–Schmidt independence criterion
Membrane proteins perform a variety of functions vital to the survival of organisms, such as
oxidoreductase, transferase or hydrolase. If the type of membrane protein can be detected …
oxidoreductase, transferase or hydrolase. If the type of membrane protein can be detected …
A review of feature set partitioning methods for multi-view ensemble learning
Since the present era is entirely computer and Internet of Things (IoT) oriented, enormous
amounts of data are produced quickly from many sources. Machine learning's primary …
amounts of data are produced quickly from many sources. Machine learning's primary …
Research status of monitoring, detection, and intelligent identification of weathering steel bridges
W Ji, X Li, J He, X Zhang, J Li - Journal of Constructional Steel Research, 2024 - Elsevier
The issue of weathering steel (WS) material and structural component inspection has been
widely discussed in the current scientific research. However, there are few comprehensive …
widely discussed in the current scientific research. However, there are few comprehensive …
Using sub-sampling and ensemble clustering techniques to improve performance of imbalanced classification
Abundant data of the patients is recorded within the health care system. During data mining
process, we can achieve useful knowledge and hidden patterns within the data and …
process, we can achieve useful knowledge and hidden patterns within the data and …
A comprehensive study of clustering ensemble weighting based on cluster quality and diversity
Clustering as a major task in data mining is responsible for discovering hidden patterns in
unlabeled datasets. Finding the best clustering is also considered as one of the most …
unlabeled datasets. Finding the best clustering is also considered as one of the most …
Consensus function based on cluster-wise two level clustering
MR Mahmoudi, H Akbarzadeh, H Parvin… - Artificial Intelligence …, 2021 - Springer
The ensemble clustering tries to aggregate a number of basic clusterings with the aim of
producing a more consistent, robust and well-performing consensus clustering result. The …
producing a more consistent, robust and well-performing consensus clustering result. The …