scholarly journals Forecast Model of TV Show Rating Based on Convolutional Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lingfeng Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Rating Data ◽  
Tv Program ◽  
Neural Network Structure ◽  
Rating Prediction ◽  
Tv Show

The TV show rating analysis and prediction system can collect and transmit information more quickly and quickly upload the information to the database. The convolutional neural network is a multilayer neural network structure that simulates the operating mechanism of biological vision systems. It is a neural network composed of multiple convolutional layers and downsampling layers sequentially connected. It can obtain useful feature descriptions from original data and is an effective method to extract features from data. At present, convolutional neural networks have become a research hotspot in speech recognition, image recognition and classification, natural language processing, and other fields and have been widely and successfully applied in these fields. Therefore, this paper introduces the convolutional neural network structure to predict the TV program rating data. First, it briefly introduces artificial neural networks and deep learning methods and focuses on the algorithm principles of convolutional neural networks and support vector machines. Then, we improve the convolutional neural network to fit the TV program rating data and finally apply the two prediction models to the TV program rating data prediction. We improve the convolutional neural network TV program rating prediction model and combine the advantages of the convolutional neural network to extract effective features and good classification and prediction capabilities to improve the prediction accuracy. Through simulation comparison, we verify the feasibility and effectiveness of the TV program rating prediction model given in this article.

scholarly journals Multi-scale target detection based on morphological shared-weight neural network

2017 ◽  
Author(s):  
◽  
Shuxian Shen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Convolutional Neural Networks ◽  
Network Structure ◽  
Training Data ◽  
Multi Scale ◽  
Neural Network Structure

Convolutional Neural Networks (CNN) are a popular neural network structure for image based applications. This thesis discusses an alternative network, the morphological shared-weight neural network (MSNN) for object detection. In this thesis, three combined network structures are developed for multi-scale object detection. The dataset used for the experiments presented here were created by the author for this thesis study. The convolutional neural network is used as the baseline for judging the performance of the MSNN. Experiments suggest that when training data is limited, the MSNN has a more robust and precise performance as compared with the CNN.

Application of convolution neural network in medical image processing

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Hongbo Zhao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Medical Image ◽  
Sampling Methods ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Data Set ◽  
Convolution Kernels ◽  
Neural Network Structure

BACKGROUND: Convolution neural network is often superior to other similar algorithms in image classification. Convolution layer and sub-sampling layer have the function of extracting sample features, and the feature of sharing weights greatly reduces the training parameters of the network. OBJECTIVE: This paper describes the improved convolution neural network structure, including convolution layer, sub-sampling layer and full connection layer. This paper also introduces five kinds of diseases and normal eye images reflected by the blood filament of the eyeball “yan.mat” data set, convenient to use MATLAB software for calculation. METHODSL: In this paper, we improve the structure of the classical LeNet-5 convolutional neural network, and design a network structure with different convolution kernels, different sub-sampling methods and different classifiers, and use this structure to solve the problem of ocular bloodstream disease recognition. RESULTS: The experimental results show that the improved convolutional neural network structure is ideal for the recognition of eye blood silk data set, which shows that the convolution neural network has the characteristics of strong classification and strong robustness. The improved structure can classify the diseases reflected by eyeball bloodstain well.

scholarly journals Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Shixi Tang ◽  
Jinan Gu ◽  
Keming Tang ◽  
Wei Ding ◽  
Zhengyang Shang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Prediction Model ◽  
Deep Neural Networks ◽  
Complete Solution ◽  
Partial Solution ◽  
Complex Environments ◽  
Solution Theory ◽  
Analysis Model ◽  
Neural Network Structure

The robot dynamic model is often rarely known due to various uncertainties such as parametric uncertainties or modeling errors existing in complex environments. It is a key problem to find the relationship between the changes of neural network structure and the changes of input and output environments and their mutual influences. Firstly, this paper defined the conceptions of neural network solution, neural network eigen solution, neural network complete solution, and neural network partial solution and the conceptions of input environments, output environments, and macrostructure of neural networks. Secondly, an eigen solution theory of general neural networks was proposed and proven including consistent approximation theorem, eigen solution existence theorem, consistency theorem of complete solution, the partial solution, and none solution theorem of neural networks. Lastly, to verify the eigen solution theory of neural networks, the proposed theory was applied to a novel prediction and analysis model of controller parameters of grinding robot in complex environments with deep neural networks and then build prediction model with deep learning neural networks for controller parameters of grinding robot. The morphological subfeature graph with multimoment was constructed to describe the block surface morphology using rugosity, standard deviation, skewness, and kurtosis. The results of theoretical analysis and experimental test show that the output traits have an optional effect with joint action. When the input features functioning in prediction increase, higher predicted accuracy can be obtained. And when the output traits involving in prediction increase, more output traits can be predicted. The proposed prediction and analysis model with deep neural networks can be used to find and predict the inherent laws of the data. Compared with the traditional prediction model, the proposed model can predict output features simultaneously and is more stable.

Copyright Protection for Software 2.0?

2021 ◽  
pp. 323-340
Author(s):  
Hao-Yun Chen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Structure ◽  
Copyright Protection ◽  
Computer Programs ◽  
Copyright Law ◽  
Data Sets ◽  
Copyright Laws ◽  
Neural Network Structure ◽  
Over Time

Traditionally, software programmers write a series of hard-coded rules to instruct a machine, step by step. However, with the ubiquity of neural networks, instead of giving specific instructions, programmers can write a skeleton of code to build a neural network structure, and then feed the machine with data sets, in order to have the machine write code by itself. Software containing the code written in this manner changes and evolves over time as new data sets are input and processed. This characteristic distinguishes it markedly from traditional software, and is partly the reason why it is referred to as ‘software 2.0’. Yet the vagueness of the scope of such software might make it ineligible for protection by copyright law. To properly understand and address this issue, this chapter will first review the current scope of computer program protection under copyright laws, and point out the potential inherent issues arising from the application of copyright law to software 2.0. After identifying related copyright law issues, this chapter will then examine the possible justification for protecting computer programs in the context of software 2.0, aiming to explore whether new exclusivity should be granted or not under copyright law, and if not, what alternatives are available to provide protection for the investment in the creation and maintenance of software 2.0.

Neural Network Structure Analysis Based on Hierarchical Force-Directed Graph Drawing for Multi-Task Learning

2015 ◽  
Vol 19 (2) ◽  
pp. 225-231 ◽  
Author(s):  
Atsushi Shibata ◽  
◽  
Fangyan Dong ◽  
Kaoru Hirota ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Directed Graph ◽  
Network Structure ◽  
Graph Drawing ◽  
Training Data ◽  
Specific Task ◽  
Motion System ◽  
Close Relationship ◽  
Neural Network Structure

A hierarchical force-directed graph drawing is proposed for the analysis of a neural network structure that expresses the relationship between multitask and processes in neural networks represented as neuron clusters. The process revealed by our proposal indicates the neurons that are related to each task and the number of neurons or learning epochs that are sufficient. Our proposal is evaluated by visualizing neural networks learned on the Mixed National Institute of Standards and Technology (MNIST) database of handwritten digits, and the results show that inactive neurons, namely those that do not have a close relationship with any tasks, are located on the periphery part of the visualized network, and that cutting half of the training data on one specific task (out of ten) causes a 15% increase in the variance of neurons in clusters that react to the specific task compared to the reaction to all tasks. The proposal aims to be developed in order to support the design process of neural networks that consider multitasking of different categories, for example, one neural network for both the vision and motion system of a robot.

Genetic Programming for Letters Identification Based on Neural Network

2015 ◽  
Vol 734 ◽  
pp. 642-645
Author(s):  
Yan Hui Liu ◽  
Zhi Peng Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Genetic Programming ◽  
Network Structure ◽  
High Accuracy ◽  
Identification System ◽  
Neural Network Structure ◽  
Hidden Layer

According to the problem that the letters identification is not high accuracy using neural networks, in this paper, an optimal neural network structure is designed based on genetic algorithm to optimize the number of hidden layer. The English letters can be identified by optimal neural network. The results obtained in the genetic programming optimizations are very satisfactory. Experiments show that the identification system has higher accuracy and achieved good ideal letters identification effect.

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