scholarly journals Classification of critical thinking in mathematics using particle swarm optimization based neural network algorithms

2021 ◽  
Vol 1088 (1) ◽  
pp. 012039
Author(s):  
Ade Irma Purnamasari ◽  
Saeful Anwar ◽  
Martanto ◽  
Ahmad Faqih ◽  
Nisa Dienwati Nuris
2020 ◽  
Vol 16 (1) ◽  
pp. 129-134
Author(s):  
Robi Aziz Zuama ◽  
Irwan Agus Sobari

In this study, an automatic diagnosis analysis of the results of pap smear image extraction using neural network algorithms, the analysis included a review of the results of Herlev pap smear extraction level 7 grade, 2 normal and abnormal classes, 3 classes of normal level dysplasia and 4 classes of abnormal dysplasia levels. The problem is that neural networks are very difficult to designate optimal features in diagnosing and difficult to handle class imbalances. This study proposes a combination of particle swarm optimization (PSO) to optimize the features and bagging methods to deal with class imbalances, with the aim that the results of diagnosis using a neural network can increase its accuracy. The results show that using PSO and bagging methods can improve the accuracy of the algorithm of network balance. At level 7 the buffer class increased by 1.64%, 2 classes increased by 0.44%, 3 classes increased by 2.04%, and at level 4 the class increased by 5.47%In this study, an automatic diagnosis analysis of the results of pap smear image extraction using neural network algorithms, the analysis included a review of the results of Herlev pap smear extraction level 7 grade, 2 normal and abnormal classes, 3 classes of normal level dysplasia and 4 classes of abnormal dysplasia levels. The problem is that neural networks are very difficult to designate optimal features in diagnosing and difficult to handle class imbalances. This study proposes a combination of particle swarm optimization (PSO) to optimize the features and bagging methods to deal with class imbalances, with the aim that the results of diagnosis using a neural network can increase its accuracy. The results show that using PSO and bagging methods can improve the accuracy of the algorithm of network balance. At level 7 the buffer class increased by 1.64%, 2 classes increased by 0.44%, 3 classes increased by 2.04%, and at level 4 the class increased by 5.47%


2019 ◽  
Vol 1175 ◽  
pp. 012067
Author(s):  
Mohammad Humam ◽  
Oman Somantri ◽  
Maylane Boni Abdillah ◽  
Syaefani Arif Romadhon ◽  
Mohammad Khambali ◽  
...  

2014 ◽  
Vol 986-987 ◽  
pp. 1431-1434
Author(s):  
Ning Xia Yang ◽  
Mao Fa Gong ◽  
Xiao Fei Wang ◽  
Hui Ting Ge ◽  
Yu Qing Lin ◽  
...  

To improve accuracy and speed of recognising and classifying grid power quality disturbances, this paper presents a new method which combines complex wavelet transform and particle swarm optimization (PSO) neural network to identify and classify the disturbance . This method extract both amplitude-frequency and phase frequency information of the interference signal to make up for the lack of traditional wavelet transform which only extract the amplitude-frequency information. And on this basis, using particle swarm optimization, we seek the optimal solution of neural network weights and thresholds for the identification and classification of power quality. The MATLAB simulation result has verified the accuracy and rapidity of this method compared with the traditional method .


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