WITHDRAWN: An efficient framework for blog recommender system

Muppidi, Satish; Rao, P. Srinivasa; Kirubakaran, S. Stewart

doi:10.1016/j.matpr.2021.02.669

Cited by 2 publications

(2 citation statements)

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“…The testing method was explained in great detail. Uploading datasets, preparing them for CNN training, When compared to the CNN method, the suggested method helps make the OpenCVlibrary [11] in Figure 2 more accurate. The data shows that the 95.5% accuracy of the OpenCV library profile data faces is a very important number.…”

Section: Resultsmentioning

confidence: 99%

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Human Emotions Recognition Using Softmax Classifier and Predict the Error Level Using OpenCV Library

PavanreddyA

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

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The goal of this research project is to compare the accuracy of the CNN method using the Softmax classifier and the OpenCV library by taking out new features and comparing them to the accuracy of the CNN method. To analyse the suggested system, CNN algorithms with softmax classifier and OpenCV library were used with a sample size of 20 to find facial expression recognition by comparing the accuracy and error rate between the techniques. In the proposed study, the dataset was used to collect data on the outcome and accuracy of the OpenCV error rate, and the OpenCV improvement was compared to the CNN softmax classifier technique. The average accuracy of OpenCV over techniques that use convolutional neural networks is 95.5%. The T-Test doesn’t show anything important (p 0.05). Face recognition systems were used to get information from a number of places for the study. Compared to the convolution neural network method, the OpenCV algorithm made the result more accurate and less likely to make mistakes.

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Section: Resultsmentioning

confidence: 99%

“…Associations in the OpenCV library use these attributes to change the way they look. Feature extraction is used by the OpenCV library to classify facial expressions [11].…”

Section: Methodsmentioning

confidence: 99%