scholarly journals The Realization and Optimization Technology of Recognition Algorithm Based on Tensorflow Deep Learning Mechanism

2021 ◽  
Vol 2066 (1) ◽  
pp. 012002
Author(s):  
Wencai Xu
Keyword(s):  
Deep Learning ◽  
Sequence Analysis ◽  
Video Sequence ◽  
Recognition Algorithm ◽  
Machine Intelligence ◽  
Software Library ◽  
Learning Mechanism ◽  
Learning Framework ◽  
Recognition Algorithms ◽  
Video Sequence Analysis

Abstract With the rapid development of today’s technological society, recognition algorithms have received more and more attention. In addition, in recent years, deep learning algorithms have developed rapidly at the theoretical level, and related new technologies have also been applied to various industries. TensorFlow is a deep learning framework that performs well in all aspects. The purpose of this article is to study the realization of recognition algorithms based on TensorFlow’s deep learning mechanism and their optimization techniques. The target detection algorithm used in the system in this paper combines deep learning technology to replace the traditional method based on convolutional filtering. The paper is based on the TensorFlow deep learning framework. TensorFlow is an open source software library for machine intelligence. The learning software library of the network learning framework. This article uses a semi-automatic labeling method combined with an incremental learning algorithm to label the data set. After labeling the data, the parameters are set, the model is trained, and the model is finally trained and applied to the detection system. Studies have shown that: in the recognition algorithm, only the single sub-analysis stream is considered, and the short video sequence analysis stream can get the most excellent accuracy. Compared with the second best long video sequence analysis stream, it can also increase by about 3%.

A Competitive Neural Network for Multiple Object Tracking in Video Sequence Analysis

2012 ◽  
Vol 37 (1) ◽  
pp. 47-67 ◽  
Author(s):  
Rafael M. Luque-Baena ◽  
Juan M. Ortiz-de-Lazcano-Lobato ◽  
Ezequiel López-Rubio ◽  
Enrique Domínguez ◽  
Esteban J. Palomo
Keyword(s):  
Neural Network ◽  
Sequence Analysis ◽  
Object Tracking ◽  
Video Sequence ◽  
Multiple Object Tracking ◽  
Competitive Neural Network ◽  
Multiple Object ◽  
Video Sequence Analysis

scholarly journals News Sentiment Analysis By Using Deep Learning Framework

2020 ◽  
Author(s):  
Sweta Kaman
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
News Article ◽  
Learning Mechanism ◽  
Attention Model ◽  
Learning Framework ◽  
News Sentiment ◽  
Document Level

Attention is a deep learning mechanism which has been proved very helpful in the field of artificial intelligence and solving various AI problems, in order to bend the various intelligent tasks positively in the direction to its actual goal i.e AI. In this paper, I have used Attention Model to perform the task of sentiment analysis in any news article. After extracting the news article from a scraper and preprocessing the data, it will be fed into a sentiment analyser which will predict the sentiment of the news article at sentence and document level.

scholarly journals Implementation of Object Recognition Algorithm to enhance Manufacturing and Maintenance Tasks on an Aircraft

2021 ◽  
Author(s):  
Abhinav Sundar
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Recognition Algorithm ◽  
Recognition Algorithms ◽  
Aerospace Assembly

The objective of this thesis was to evaluate the viability of implementation of an object recognition algorithm driven by deep learning for aerospace manufacturing, maintenance and assembly tasks. Comparison research has found that current computer vision methods such as, spatial mapping was limited to macro-object recognition because of its nodal wireframe analysis. An optical object recognition algorithm was trained to learn complex geometric and chromatic characteristics, therefore allowing for micro-object recognition, such as cables and other critical components. This thesis investigated the use of a convolutional neural network with object recognition algorithms. The viability of two categories of object recognition algorithms were analyzed: image prediction and object detection. Due to a viral epidemic, this thesis was limited in analytical consistency as resources were not readily available. The prediction-class algorithm was analyzed using a custom dataset comprised of 15 552 images of the MaxFlight V2002 Full Motion Simulator’s inverter system, and a model was created by transfer-learning that dataset onto the InceptionV3 convolutional neural network (CNN). The detection-class algorithm was analyzed using a custom dataset comprised of 100 images of two SUVs of different brand and style, and a model was created by transfer-learning that dataset onto the YOLOv3 deep learning architecture. The tests showed that the object recognition algorithms successfully identified the components with good accuracy, 99.97% mAP for prediction-class and 89.54% mAP. For detection-class. The accuracies and data collected with literature review found that object detection algorithms are accuracy, created for live -feed analysis and were suitable for the significant applications of AVI and aircraft assembly. In the future, a larger dataset needs to be complied to increase reliability and a custom convolutional neural network and deep learning algorithm needs to be developed specifically for aerospace assembly, maintenance and manufacturing applications.

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