Nikhil R Pal
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Explore the profile of Nikhil R Pal including associated specialties, affiliations and a list of published articles.
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37
Citations
118
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Recent Articles
1.
Hu Z, Wang J, Zhang K, Pedrycz W, Pal N
IEEE Trans Neural Netw Learn Syst
. 2024 Jun;
PP.
PMID: 38896511
Unsupervised feature selection (UFS) aims to learn an indicator matrix relying on some characteristics of the high-dimensional data to identify the features to be selected. However, traditional unsupervised methods perform...
2.
Tran X, Do T, Pal N, Jung T, Lin C
Sci Rep
. 2024 Jun;
14(1):13217.
PMID: 38851836
Anticipating human decisions while performing complex tasks remains a formidable challenge. This study proposes a multimodal machine-learning approach that leverages image features and electroencephalography (EEG) data to predict human response...
3.
Wang J, Wu S, Zhang H, Yuan B, Dai C, Pal N
IEEE Trans Neural Netw Learn Syst
. 2024 Apr;
36(3):5586-5600.
PMID: 38568758
Approximation ability is one of the most important topics in the field of neural networks (NNs). Feedforward NNs, activated by rectified linear units and some of their specific smoothed versions,...
4.
Wang Y, Wang W, Pal N
IEEE Trans Neural Netw Learn Syst
. 2022 Oct;
35(5):6878-6892.
PMID: 36306292
As a crucial part of machine learning and pattern recognition, feature selection aims at selecting a subset of the most informative features from the set of all available features. In...
5.
Wu S, Wang J, Sun H, Zhang K, Pal N
IEEE Trans Cybern
. 2022 May;
54(2):811-824.
PMID: 35622792
Approximation ability is of much importance for neural networks. The broad learning system (BLS) (Chen and Liu, 2018), widely used in the industry with good performance, has been proved to...
6.
Gong X, Yu L, Wang J, Zhang K, Bai X, Pal N
Neural Netw
. 2022 Mar;
150:87-101.
PMID: 35306463
Unsupervised feature selection is one of the efficient approaches to reduce the dimension of unlabeled high-dimensional data. We present a novel adaptive autoencoder with redundancy control (AARC) as an unsupervised...
7.
Chang Y, Wang Y, Pal N, Lin C
IEEE Trans Neural Syst Rehabil Eng
. 2021 Nov;
29:2464-2473.
PMID: 34748496
Human brain inherently exhibits latent mental processes which are likely to change rapidly over time. A framework that adopts a fuzzy inference system is proposed to model the dynamics of...
8.
Pal N
Front Robot AI
. 2021 Jan;
7:76.
PMID: 33501243
At present we are witnessing a tremendous interest in Artificial Intelligence (AI), particularly in Deep Learning (DL)/Deep Neural Networks (DNNs). One of the reasons appears to be the unmatched performance...
9.
Wang J, Zhang H, Wang J, Pu Y, Pal N
IEEE Trans Neural Netw Learn Syst
. 2020 May;
32(3):1110-1123.
PMID: 32396104
We propose a neural network-based feature selection (FS) scheme that can control the level of redundancy in the selected features by integrating two penalties into a single objective function. The...
10.
Xie X, Zhang H, Wang J, Chang Q, Wang J, Pal N
IEEE Trans Cybern
. 2019 Nov;
50(3):1333-1346.
PMID: 31765323
We propose three different methods to determine the optimal number of hidden nodes based on L regularization for a multilayer perceptron network. The first two methods, respectively, use a set...