The liquor quality recognition using magnetic resonance spectrum based on Kernel principal component analysis and convolutional neural network

2022 ◽  
Vol 6 (1) ◽  
pp. 13-20
Author(s):  
Lin-Lin Cheng ◽  
Lei-Lei Chen ◽  
Qiao-Mei Wang ◽  
Ming-Ju Chen ◽  
Xing-Zhong Xiong
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Resonance Spectrum ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis

scholarly journals PROSES EKSTRAKSI DAN KLASIFIKASI CITRA EMOSI MENGGUNAKAN METODE PCA DAN CNN

JOUTICA ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 484
Author(s):  
Resty Wulanningrum ◽  
Anggi Nur Fadzila ◽  
Danar Putra Pamungkas
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Facial Expression Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Recognition

Manusia secara alami menggunakan ekspresi wajah untuk berkomunikasi dan menunjukan emosi mereka dalam berinteraksi sosial. Ekspresi wajah termasuk kedalam komunikasi non-verbal yang dapat menyampaikan keadaan emosi seseorang kepada orang yang telah mengamatinya. Penelitian ini menggunakan metode Principal Component Analysis (PCA) untuk proses ekstraksi ciri pada citra ekspresi dan metode Convolutional Neural Network (CNN) sebagai prosesi klasifikasi emosi, dengan menggunakan data Facial Expression Recognition-2013 (FER-2013) dilakukan proses training dan testing untuk menghasilkan nilai akurasi dan pengenalan emosi wajah. Hasil pengujian akhir mendapatkan nilai akurasi pada metode PCA sebesar 59,375% dan nilai akurasi pada pengujian metode CNN sebesar 59,386%.

scholarly journals REAL-TIME CLASSIFICATION OF FACIAL EXPRESSIONS USING A PRINCIPAL COMPONENT ANALYSIS AND CONVOLUTIONAL NEURAL NETWORK

SINERGI ◽  
2019 ◽  
Vol 23 (3) ◽  
pp. 239
Author(s):  
Dwi Lydia Zuharah Astuti ◽  
Samsuryadi Samsuryadi ◽  
Dian Palupi Rini
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Convolutional Neural Network ◽  
Customer Service ◽  
Facial Expressions ◽  
Principal Component ◽  
Component Analysis ◽  
Point Of View ◽  
In The Wild

Classification of facial expressions has become an essential part of computer systems and human-computer fast interaction. It is employed in various applications such as digital entertainment, customer service, driver monitoring, and emotional robots. Moreover, it has been studied through several aspects related to the face itself when facial expressions change based on the point of view or perspective. Facial curves such as eyebrows, nose, lips, and mouth will automatically change. Most of the proposed methods have limited frontal Face Expressions Recognition (FER), and their performance decrease when handling non-frontal and multi-view FER cases.  This study combined both methods in the classification of facial expressions, namely the Principal Component Analysis (PCA) and Convolutional Neural Network (CNN) methods. The results of this study proved to be more accurate than that of previous studies. The combination of PCA and CNN methods in the Static Facial Expressions in The Wild (SFEW) 2.0 dataset obtained an accuracy amounting to 70.4%; the CNN method alone only obtained an accuracy amounting to 60.9%.

scholarly journals Principal Component Analysis and Prediction of Students’ Physical Health Standard Test Results Based on Recurrent Convolution Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Kai Hou
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Physical Fitness ◽  
Physical Health ◽  
Convolutional Neural Network ◽  
Deep Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Standing Long Jump ◽  
Long Jump

The recurrent convolutional neural network is an advanced neural network that integrates deep structure and convolution calculation. The feedforward neural network with convolution operation and deep structure is an important method of deep learning. In this paper, the convolutional neural network and the recurrent neural network are combined to establish a recurrent convolutional neural network model composed of anomalies, LSTM (Long Short-Term Memory), and CNN. This study combines the principal component analysis method to predict and analyze the test results of students’ physical fitness standards. The innovation lies in the introduction of the function of the recurrent convolutional network and the use of principal component analysis to conduct qualitative research on seven evaluation indicators that reflect the three aspects of students’ physical health. The results of the study clearly show that there is a strong correlation between some indicators, such as standing long jump and sitting bends which may have a strong correlation. The first principal component eigenvalue has the highest contribution rate, which mainly reflects the five indicators of standing long jump, sitting forward bend, pull-up, 50 m sprint, and 1000 m long-distance running. This shows that the physical fitness indicators have a great impact on the physical health of students, which also reflects the current status of students’ physical fitness problems. The results of principal component analysis are scientific and reasonable.

Classification of Area Flowing Based on KRNN Method

2011 ◽  
Vol 255-260 ◽  
pp. 2855-2859
Author(s):  
Xiang Sun
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Performance ◽  
Kernel Principal Component Analysis ◽  
Analysis Method ◽  
Graduate Employment ◽  
Principal Component Analysis Method ◽  
The Neural Network

It is hard to search the influence variables and to classify the flowing areas of graduate employment due to the complex factor inputs. Recently the neural network method has been successfully employed to solve the problem. However the classification result is not ideal due to the nonlinearity and noise. In this work, by combining Recurrent Neural Network (RNN) with Kernel Principal Component Analysis (KPCA), a KRNN model is presented, based on which, the flowing areas of graduate employment is tried to be classified, and the complex factor problem has been well dealt with. In the model, RNN with Kernel Principal Component Analysis (KPCA) and Principal Component Analysis (PCA) as the feature extraction is introduced in as comparison. And then by an empirical study with actual data, it is shown that the proposed methods can both achieve good classification performance comparing with NN method. And the Kernel Principal Component Analysis method performs better than the Principal Component Analysis method.

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