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GMG-LDefmamba-YOLO: An Improved YOLOv11 Algorithm Based on Gear-Shaped Convolution and a Linear-Deformable Mamba Model for Small Object Detection in UAV Images. [PDF]

Sensors (Basel)
Yang Y, Yan L, Wang J, Liu J, Tang X.
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The Programmable Nature of Drug-Polymer Systems and Its Implications. [PDF]

Polymers (Basel)
Ghizdovat V, Nica I, Ochiuz L, Popa O, Vasincu D, Rusu DI, Agop M, Trofin AM.   +7 more
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Bidirectional recurrent neural network approach for predicting cervical cancer recurrence and survival. [PDF]

Sci Rep
Geeitha S, Prabha KPR, Cho J, Easwaramoorthy SV.   +3 more
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A deep neural network for tactile perception in open scenes. [PDF]

iScience
Fang H, Yang Q, Liu K, Huang X, Xie Y.
europepmc   +1 more source

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Discernibility Matrix-Based Ensemble Learning

2018 24th International Conference on Pattern Recognition (ICPR), 2018
Ensemble learning is admittedly one main paradigm in machine learning, where multiple individual learners are combined together to obtain better performance by making use of the significant diversity among the models. The source of diversity, however, is included in either samples or attributes in some ensemble methods.
Shuaichao Gao, Jianhua Dai, Hong Shi
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Multilabel Feature Selection Based on Relative Discernibility Pair Matrix

IEEE Transactions on Fuzzy Systems, 2022
In multi-label learning, the curse of dimensionality is one of major challenges. Existing single-label feature selection methods cannot be directly applied to multi-label data, and multi-label feature selections have thus been widely studied. As an effective granular computing tool, rough set theory has been applied to multi-label feature selections ...
Erliang Yao, Deyu Li, Yanhui Zhai, Chao Zhang   +3 more
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Discernibility matrix based dimensionality reduction for EEG signal

2016 IEEE Region 10 Conference (TENCON), 2016
Classification of EEG signals is an important task in Brain Computer Interface (BCI) research. However, the large number of attributes of EEG data is regarded as a curse for classifiers. This paper aims at dimensionality reduction of EEG signals. We use rough set theory to reduce the dimensions of EEG data.
Rajdeep Chatterjee, Dibyajyoti Guha, Debarshi Kumar Sanyal, Sachi Nandan Mohanty   +3 more
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Discernibility matrix based incremental attribute reduction for dynamic data

Knowledge-Based Systems, 2018
Abstract Dynamic data, in which the values of objects vary over time, are ubiquitous in real applications. Although researchers have developed a few incremental attribute reduction algorithms to process dynamic data, the reducts obtained by these algorithms are usually not optimal.
Wei Wei, Xiaoying Wu, Jiye Liang, Junbiao Cui, Yijun Sun   +4 more
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