Object Detection and Data Classification With Deeplearning Model Using Tensorflow
Year: 2020
Type: article
Abstract: Deep learning is the sub set of machine learning in artificial intelligence. The major role is played in deep
learning is analysis the inner function of the object and make effective decision. Deep learning achieves higher success
results in many applications and can provide faster results in data analytics process. Object has more number of patterns
and geographical movements. Analysing these pattern we need effecting classification method with maximum efficiency
and minimum processing time. In this paper, we used TensorFlow for detecting object from real time video stream data.
This neural network model and create data pattern for each objects. TensorFLow MNIST dataset is used data
classficiation and analytics process. This library files are developed my Google. The multiple actions and classification
techniques are analysed. The experimental results are analysed with liner unit, softpuls, softsign and convolution neural
network. The result shows that more accurate and objects detection using TensorFlow.
Source: Journal of Critical Reviews
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W3046784986