CAPTCHAs against meddler image identification based on a convolutional neural network

Chithra, P. L.; Sathya, K.

doi:10.1007/s11042-022-11961-w

Cited by 2 publications

(1 citation statement)

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“…fast image recognition and convergence to neural network models. Chithra P L describes a method for comparing recognition methods using a visual object category dataset in the context of image feature extraction, validating the classification accuracy of extracted image features using a probabilistic neural network [3]. Although the existing research on animation image recognition is very rich, the research on the application of neural network animation image recognition relying on time series is still insufficient.…”

Section: Introductionmentioning

confidence: 99%

Stability of Neural Networks Dependent on Time Series in Anime Image Recognition

2021

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Time series and neural networks have profoundly affected the fields of artificial intelligence and machine learning. It can be applied to tasks such as image recognition and target detection with high accuracy. In order to solve the shortcomings of the existing time series neural network animation image recognition stability research, this paper discusses the time series model stationarity, animation image image feature extraction and the functional equation of neural network loss. The dataset and parameter settings for sequential neural network animation image recognition applications are briefly introduced. And the work flow design of the time series neural network animation image recognition system structure model is discussed, and finally the stability of the time series neural network in animation image recognition is compared with the machine learning SVF, FCN model and RFC model. The experimental data show that the average absolute error of the time series neural network model in animation image recognition is small. When the number of iterations is 50-250, the average absolute error of the time series neural network model is less than 2%, while the machine learning SVF, FCN The average absolute error of the model and the RFC model is greater than 10%, so it is verified that the time series neural network model has a faster convergence speed and higher stability in animation image recognition.

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Section: Introductionmentioning

confidence: 99%

Stability of Neural Networks Dependent on Time Series in Anime Image Recognition

2021

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An Adaptive Deep Convolution Neural Network for High Pixel Image Segmentation and Classification

Pellakuri

Hùng

Chakrabarti

2023

2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA)

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CAPTCHAs against meddler image identification based on a convolutional neural network

Cited by 2 publications

References 25 publications

Stability of Neural Networks Dependent on Time Series in Anime Image Recognition

Stability of Neural Networks Dependent on Time Series in Anime Image Recognition

An Adaptive Deep Convolution Neural Network for High Pixel Image Segmentation and Classification

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