Fusion of progressive granular neural networks for pattern classification

D. Arun Kumar; Saroj K. Meher; K. Padma Kumari

doi:10.1007/s00500-018-3052-x

Fusion of progressive granular neural networks for pattern classification

Soft Computing ◽

10.1007/s00500-018-3052-x ◽

2018 ◽

Vol 23 (12) ◽

pp. 4051-4064 ◽

Cited By ~ 2

Author(s):

D. Arun Kumar ◽

Saroj K. Meher ◽

K. Padma Kumari

Keyword(s):

Neural Networks ◽

Pattern Classification ◽

Granular Neural Networks

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Unified granular neural networks for pattern classification

Neurocomputing ◽

10.1016/j.neucom.2016.07.034 ◽

2016 ◽

Vol 216 ◽

pp. 109-125 ◽

Cited By ~ 7

Author(s):

D. Arun Kumar ◽

Saroj K. Meher ◽

Debananda Kanhar ◽

K. Padma Kumari

Keyword(s):

Neural Networks ◽

Pattern Classification ◽

Granular Neural Networks

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Neural Networks Pattern Classification For Certainty In Measurement of Position and Momentum With Heisenberg Uncertainty Principle

Journal of Advances and Scholarly Researches in Allied Education ◽

10.29070/15/57935 ◽

2018 ◽

Vol 15 (9) ◽

pp. 114-118

Author(s):

Punam . ◽

O. P. Prakash

Keyword(s):

Neural Networks ◽

Pattern Classification ◽

Uncertainty Principle ◽

Heisenberg Uncertainty Principle ◽

Heisenberg Uncertainty

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Deep learning adapted to differential neural networks used as pattern classification of electrophysiological signals

IEEE Transactions on Pattern Analysis and Machine Intelligence ◽

10.1109/tpami.2021.3066996 ◽

2021 ◽

pp. 1-1

Author(s):

Dusthon Llorente ◽

Mariana Ballesteros ◽

Ivan DE JESUS Salgado Ramos ◽

Jorge Isaac Chairez Oria

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Pattern Classification ◽

Electrophysiological Signals

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Optimization of modular granular neural networks using a firefly algorithm for human recognition

Engineering Applications of Artificial Intelligence ◽

10.1016/j.engappai.2017.06.007 ◽

2017 ◽

Vol 64 ◽

pp. 172-186 ◽

Cited By ~ 67

Author(s):

Daniela Sánchez ◽

Patricia Melin ◽

Oscar Castillo

Keyword(s):

Neural Networks ◽

Firefly Algorithm ◽

Human Recognition ◽

Granular Neural Networks

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Multistrategy learning using genetic algorithms and neural networks for pattern classification

1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929) ◽

10.1109/icsmc.1996.565355 ◽

2002 ◽

Cited By ~ 1

Author(s):

Zhou Yuanhui ◽

Zhang Zhaohui ◽

Lu Yuchang ◽

Shi Chunyi

Keyword(s):

Neural Networks ◽

Genetic Algorithms ◽

Pattern Classification ◽

Multistrategy Learning

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A Framework for Analysis of Granular Neural Networks

Rough Sets - Lecture Notes in Computer Science ◽

10.1007/978-3-319-60837-2_16 ◽

2017 ◽

pp. 199-206

Author(s):

Julian Skirzyński

Keyword(s):

Neural Networks ◽

Granular Neural Networks

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Learning algorithm for pattern classification using cellular neural networks

Electronics Letters ◽

10.1049/el:20001368 ◽

2000 ◽

Vol 36 (23) ◽

pp. 1941 ◽

Cited By ~ 9

Author(s):

G. Grassi ◽

E. Di Sciascio

Keyword(s):

Neural Networks ◽

Pattern Classification ◽

Learning Algorithm ◽

Cellular Neural Networks

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Study on feature weight and feature selection in pattern classification neural networks

1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929) ◽

10.1109/icsmc.1996.565427 ◽

2002 ◽

Author(s):

Rui-Ping Li ◽

M. Mukaidono ◽

I.B. Turksen

Keyword(s):

Neural Networks ◽

Feature Selection ◽

Pattern Classification ◽

Feature Weight

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Stock Pattern Classification from Charts using Deep Learning Algorithms

Academic Perspective Procedia ◽

10.33793/acperpro.03.01.89 ◽

2020 ◽

Vol 3 (1) ◽

pp. 445-454

Author(s):

Celal Buğra Kaya ◽

Alperen Yılmaz ◽

Gizem Nur Uzun ◽

Zeynep Hilal Kilimci

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Pattern Classification ◽

Short Term Memory ◽

Stock Exchange ◽

Learning Techniques ◽

Proposed Model ◽

Istanbul Stock Exchange

Pattern classification is related with the automatic finding of regularities in dataset through the utilization of various learning techniques. Thus, the classification of the objects into a set of categories or classes is provided. This study is undertaken to evaluate deep learning methodologies to the classification of stock patterns. In order to classify patterns that are obtained from stock charts, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory networks (LSTMs) are employed. To demonstrate the efficiency of proposed model in categorizing patterns, hand-crafted image dataset is constructed from stock charts in Istanbul Stock Exchange and NASDAQ Stock Exchange. Experimental results show that the usage of convolutional neural networks exhibits superior classification success in recognizing patterns compared to the other deep learning methodologies.

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Unsupervised pattern classification by neural networks

Mathematics and Computers in Simulation ◽

10.1016/0378-4754(95)00063-1 ◽

1996 ◽

Vol 41 (1-2) ◽

pp. 109-116 ◽

Cited By ~ 5

Author(s):

D. Hamad ◽

C. Firmin ◽

J.-G. Postaire

Keyword(s):

Neural Networks ◽

Pattern Classification

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