A comparative study on authorship attribution classification tasks using both neural network and statistical methods

2009 ◽  
Vol 19 (4) ◽  
pp. 573-582 ◽  
Author(s):  
Nikos Tsimboukakis ◽  
George Tambouratzis
Keyword(s):  
Neural Network ◽  
Comparative Study ◽  
Statistical Methods ◽  
Authorship Attribution ◽  
Classification Tasks

scholarly journals Comparative Study of Artificial Neural Network Based Channel Equalization Methods for MmWave Communications

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Diego Fernando Carrera ◽  
Cesar Vargas-Rosales ◽  
Noe M. Yungaicela-Naula ◽  
Leyre Azpilicueta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Comparative Study ◽  
Channel Equalization ◽  
Artificial Neural

scholarly journals Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Jian-ye Yuan ◽  
Xin-yuan Nan ◽  
Cheng-rong Li ◽  
Le-le Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Environmental Science ◽  
Fine Tuning ◽  
Classification Model ◽  
Classification Models ◽  
Data Set ◽  
Classification Tasks ◽  
Accuracy Rates

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification model. Besides, we compared the model with classic image classification models including AlexNet, VGG16, and ResNet18 and lightweight classification models including MobileNetV2 and SuffleNetV2 and found that the model GAF_dense has a higher accuracy rate, fewer Params, and FLOPs. To further check the performance of the model, we tested the CIFAR-10 data set and found the accuracy rates of the model (GAF_dense) are 0.018 and 0.03 higher than ResNet18 and SufflenetV2, respectively. In the ImageNet data set, the accuracy rates of the model (GAF_dense) are 0.225 and 0.146 higher than Resnet18 and SufflenetV2, respectively. Therefore, the garbage classification model proposed in this paper is suitable for garbage classification and other classification tasks to protect the ecological environment, which can be applied to classification tasks such as environmental science, children’s education, and environmental protection.

Selection of Proper Neural Network Sizes and Architectures—A Comparative Study

2012 ◽  
Vol 8 (2) ◽  
pp. 228-240 ◽  
Author(s):  
David Hunter ◽  
Hao Yu ◽  
Michael S. Pukish, III ◽  
Janusz Kolbusz ◽  
Bogdan M. Wilamowski
Keyword(s):  
Neural Network ◽  
Comparative Study ◽  
Selection Of

Based on BP Neural Network Discrete Data Forecast

2011 ◽  
Vol 50-51 ◽  
pp. 977-981 ◽  
Author(s):  
Jing Wang ◽  
Guo Li Wang ◽  
Jian Hui Wu ◽  
Yu Su
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Statistical Methods ◽  
Bp Neural Network ◽  
Model Fitting ◽  
Predictive Ability ◽  
Discrete Data ◽  
Engineering System ◽  
Forecast Performance ◽  
Artificial Neural

Artificial neural network is based on human brain structure and operational mechanism based on knowledge and understanding of its structure and behavior of simulated an engineering system. BP artificial neural network is an important component of neural networks, as it can on the linear or nonlinear multivariable without preconditions in the case of statistical analysis, with the traditional statistical methods, analysis of the variables need to be consistent with certain conditions compared to its own advantage. The BP neural network does not need the precise mathematical model, does not have any supposition request to the material itself. Its processing non-linear problem's ability is stronger than traditional statistical methods. This article uses two groups of data to establish the BP neural network model separately, and carries on the comparison to the model fitting ability and the forecast performance, discovered BP neural network when data distribution relative centralism fits ability, forecasts the stable property. But the predictive ability is unable in the discrete data application to achieve anticipated ideally.

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