A review on methods and software for fuzzy cognitive maps
Fuzzy cognitive maps (FCMs) keep growing in popularity within the scientific community.
However, despite substantial advances in the theory and applications of FCMs, there is a …
However, despite substantial advances in the theory and applications of FCMs, there is a …
Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification
Q Zheng, P Zhao, Y Li, H Wang, Y Yang - Neural Computing and …, 2021 - Springer
Automatic modulation classification is an essential and challenging topic in the development
of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation …
of cognitive radios, and it is the cornerstone of adaptive modulation and demodulation …
Fuzzy cognitive maps based models for pattern classification: Advances and challenges
Abstract Fuzzy Cognitive Maps (FCMs) have proven to be a suitable methodology for the
design of knowledge-based systems. By combining both uncertainty depiction and cognitive …
design of knowledge-based systems. By combining both uncertainty depiction and cognitive …
How active learning and process mining can act as Continuous Auditing catalyst
M Jans, M Hosseinpour - International Journal of Accounting Information …, 2019 - Elsevier
In the context of Continuous Auditing, different approaches have been proposed to
incorporate data analytics to accomplish a continuous audit environment. Some work …
incorporate data analytics to accomplish a continuous audit environment. Some work …
Classification and feature transformation with fuzzy cognitive maps
P Szwed - Applied Soft Computing, 2021 - Elsevier
Abstract Fuzzy Cognitive Maps (FCMs) are considered a soft computing technique
combining elements of fuzzy logic and recurrent neural networks. They found multiple …
combining elements of fuzzy logic and recurrent neural networks. They found multiple …
[HTML][HTML] A three-way decision ensemble method for imbalanced data oversampling
Abstract Synthetic Minority Over-sampling Technique (SMOTE) is an effective method for
imbalanced data classification. Many variants of SMOTE have been proposed in the past …
imbalanced data classification. Many variants of SMOTE have been proposed in the past …
Fuzzy-rough cognitive networks
Abstract Rough Cognitive Networks (RCNs) are a kind of granular neural network that
augments the reasoning rule present in Fuzzy Cognitive Maps with crisp information …
augments the reasoning rule present in Fuzzy Cognitive Maps with crisp information …
Fuzzy-rough cognitive networks: Theoretical analysis and simpler models
Fuzzy-rough cognitive networks (FRCNs) are recurrent neural networks (RNNs) intended for
structured classification purposes in which the problem is described by an explicit set of …
structured classification purposes in which the problem is described by an explicit set of …
Granular structure-based incremental updating for multi-label classification
Y Zhang, D Miao, W Pedrycz, T Zhao, J Xu… - Knowledge-Based …, 2020 - Elsevier
Incremental learning is an efficient computational paradigm of acquiring approximate
knowledge of data in dynamic environment. Most of the research focuses on knowledge …
knowledge of data in dynamic environment. Most of the research focuses on knowledge …
[HTML][HTML] Cartography and Neural Networks: A Scientometric Analysis Based on CiteSpace
S Cheng, J Zhang, G Wang, Z Zhou, J Du… - … International Journal of …, 2024 - mdpi.com
Propelled by emerging technologies such as artificial intelligence and deep learning, the
essence and scope of cartography have significantly expanded. The rapid progress in …
essence and scope of cartography have significantly expanded. The rapid progress in …