Applying the Convolutional Neural Network Deep Learning Technology to Behavioural Recognition in Intelligent Video

Abstract Convolutional neural network is a very important research direction in deep learning technology. According to the current development of convolutional network, in this paper, convolutional neural networks are induced. Firstly, this paper induces the development process of convolutional neural network; then it introduces the structure of convolutional neural network and some typical convolutional neural networks. Finally, several examples of the application of deep learning is introduced.

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Spam Detection on Social Media Using Semantic Convolutional Neural Network

Deep Learning and Neural Networks ◽

10.4018/978-1-7998-0414-7.ch039 ◽

2020 ◽

pp. 704-719

Author(s):

Gauri Jain ◽

Manisha Sharma ◽

Basant Agarwal

Keyword(s):

Neural Network ◽

Social Media ◽

Deep Learning ◽

Convolutional Neural Network ◽

Spam Detection ◽

Learning Technology ◽

The Social ◽

Social Media Text ◽

Current Article ◽

Semantic Layer

This article describes how spam detection in the social media text is becoming increasing important because of the exponential increase in the spam volume over the network. It is challenging, especially in case of text within the limited number of characters. Effective spam detection requires more number of efficient features to be learned. In the current article, the use of a deep learning technology known as a convolutional neural network (CNN) is proposed for spam detection with an added semantic layer on the top of it. The resultant model is known as a semantic convolutional neural network (SCNN). A semantic layer is composed of training the random word vectors with the help of Word2vec to get the semantically enriched word embedding. WordNet and ConceptNet are used to find the word similar to a given word, in case it is missing in the word2vec. The architecture is evaluated on two corpora: SMS Spam dataset (UCI repository) and Twitter dataset (Tweets scrapped from public live tweets). The authors' approach outperforms the-state-of-the-art results with 98.65% accuracy on SMS spam dataset and 94.40% accuracy on Twitter dataset.

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Experimental Discussion on Fire Image Recognition Based on Deep Learning

Journal of Physics Conference Series ◽

10.1088/1742-6596/2066/1/012071 ◽

2021 ◽

Vol 2066 (1) ◽

pp. 012071

Author(s):

Yongyi Cui ◽

Fang Qu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Image Recognition ◽

Rapid Development ◽

Fire Detection ◽

Learning Technology ◽

Fire Recognition

Abstract Fire detection technology based on video images is an emerging technology that has its own unique advantages in many aspects. With the rapid development of deep learning technology, Convolutional Neural Networks based on deep learning theory show unique advantages in many image recognition fields. This paper uses Convolutional Neural Networks to try to identify fire in video surveillance images. This paper introduces the main processing flow of Convolutional Neural Networks when completing image recognition tasks, and elaborates the basic principles and ideas of each stage of image recognition in detail. The Pytorch deep learning framework is used to build a Convolutional Neural Network for training, verification and testing for fire recognition. In view of the lack of a standard and authoritative fire recognition training set, we have conducted experiments on fires with various interference sources under various environmental conditions using a variety of fuels in the laboratory, and recorded videos. Finally, the Convolutional Neural Network was trained, verified and tested by using experimental videos, fire videos on the Internet as well as other interference source videos that may be misjudged as fires.

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Deep Learning Based Coronary Angiography in Diagnosis of Myocardial Ischemia

Scientific Programming ◽

10.1155/2021/8491976 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yiwen Shu ◽

Xiwen Wu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Coronary Angiography ◽

Myocardial Ischemia ◽

Convolutional Neural Network ◽

Learning Algorithm ◽

Control Group ◽

Learning Technology ◽

Observation Group ◽

Deep Learning Algorithm

Objective. This study was to explore the diagnostic effect of the coronary angiography (CAG) based on the fully convolutional neural network (FCNN) algorithm for patients with coronary heart disease (CHD) and suspected (not diagnosed) myocardial ischemia. Methods. In this study, 150 patients with undiagnosed CHD with myocardial ischemia in hospital were selected as the research objects. They were divided into an observation group and a control group by random number method. The patients in observation group were examined with CAG with the assistance of convolutional neural network (CNN) algorithm, while patients in the control group received conventional CAG. Results. The Dice coefficient of the segmentation effect evaluation index was 0.89, which showed that the image processing effect of the algorithm was good. There was no statistical difference in positive rates of single/double-vessel lesions between the two groups ( P > 0.05 ), and the positive rates of multivessel lesions and total lesions in the observation group were higher than those in the control group, showing statistically obvious difference ( P < 0.05 ). The examination sensitivity, specificity, accuracy, and Kappa value of the observation group were −90.9%, −60%, −82.7%, and −0.72, which were all higher in contrast to those of the control group. The proportion of positive myocardial ischemia and coronary artery stenosis (CAS) (82%) was higher than other cases (18%), and the comparison was statistically significant ( P < 0.05 ). Conclusion. CAG based on the deep learning algorithm showed a good detection effect and can better display the coronary lesions and reflect the good development prospects of deep learning technology in medical imaging.

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Intelligent Digital Twin to make Robot Learn the Assembly process through Deep Learning

Lahore Garrison University Research Journal of Computer Science and Information Technology ◽

10.54692/lgurjcsit.2021.0503219 ◽

2021 ◽

Vol 5 (3) ◽

pp. 65-72

Author(s):

Bilal Ahmad

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Industrial Robots ◽

Assembly System ◽

Learning Technology ◽

Assembly Process ◽

Digital Twin ◽

New Form ◽

Collaborative Robot

The objective of this paper is to utilize deep learning technology to develop an intelligent digital twin for the operational support of a human-robot assembly station. Digital twin, as a virtual portrayal, is used to design, simulate, and optimize the complexity of the assembly system. For testing purposes, a convolutional neural network (CNN) is integrated with a digital twin. It is used for the application of a collaborative robot for an assembly application. Collaborative robots are a new form of industrial robots that are safe for humans and can work alongside humans and have received ample attraction in recent years for automation of simple to complex tasks.

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Bearing Fault Diagnosis Based on Multiscale Convolutional Neural Network Using Data Augmentation

Journal of Sensors ◽

10.1155/2021/6699637 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Seungmin Han ◽

Seokju Oh ◽

Jongpil Jeong

Keyword(s):

Neural Network ◽

Deep Learning ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Feature Learning ◽

Learning Technology ◽

Financial Loss ◽

Training Time ◽

Imbalance Problem

Bearings are one of the most important parts of a rotating machine. Bearing failure can lead to mechanical failure, financial loss, and even personal injury. In recent years, various deep learning techniques have been used to diagnose bearing faults in rotating machines. However, deep learning technology has a data imbalance problem because it requires huge amounts of data. To solve this problem, we used data augmentation techniques. In addition, Convolutional Neural Network, one of the deep learning models, is a method capable of performing feature learning without prior knowledge. However, since conventional fault diagnosis based on CNN can only extract single-scale features, not only useful information may be lost but also domain shift problems may occur. In this paper, we proposed a Multiscale Convolutional Neural Network (MSCNN) to extract more powerful and differentiated features from raw signals. MSCNN can learn more powerful feature expression than conventional CNN through multiscale convolution operation and reduce the number of parameters and training time. The proposed model proved better results and validated the effectiveness of the model compared to 2D-CNN and 1D-CNN.

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Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method

Journal of Personalized Medicine ◽

10.3390/jpm11080786 ◽

2021 ◽

Vol 11 (8) ◽

pp. 786

Author(s):

Chaoyue Chen ◽

Yisong Cheng ◽

Jianfeng Xu ◽

Ting Zhang ◽

Xin Shu ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

External Validation ◽

Area Under The Curve ◽

Visual Assessment ◽

Assessment System ◽

Validation Dataset ◽

Low Grade ◽

Learning Technology

The purpose of this study was to determine whether a deep-learning-based assessment system could facilitate preoperative grading of meningioma. This was a retrospective study conducted at two institutions covering 643 patients. The system, designed with a cascade network structure, was developed using deep-learning technology for automatic tumor detection, visual assessment, and grading prediction. Specifically, a modified U-Net convolutional neural network was first established to segment tumor images. Subsequently, the segmentations were introduced into rendering algorithms for spatial reconstruction and another DenseNet convolutional neural network for grading prediction. The trained models were integrated as a system, and the robustness was tested based on its performance on an external dataset from the second institution involving different magnetic resonance imaging platforms. The results showed that the segment model represented a noteworthy performance with dice coefficients of 0.920 ± 0.009 in the validation group. With accurate segmented tumor images, the rendering model delicately reconstructed the tumor body and clearly displayed the important intracranial vessels. The DenseNet model also achieved high accuracy with an area under the curve of 0.918 ± 0.006 and accuracy of 0.901 ± 0.039 when classifying tumors into low-grade and high-grade meningiomas. Moreover, the system exhibited good performance on the external validation dataset.

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Data Augmentation using Adversarial Networks for Tea Diseases Detection

Jurnal Elektronika dan Telekomunikasi ◽

10.14203/jet.v20.29-35 ◽

2020 ◽

Vol 20 (1) ◽

pp. 29

Author(s):

R. Sandra Yuwana ◽

Fani Fauziah ◽

Ana Heryana ◽

Dikdik Krisnandi ◽

R. Budiarianto Suryo Kusumo ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Original Data ◽

The Other ◽

Generative Adversarial Networks ◽

Learning Technology ◽

Limited Data ◽

Adversarial Networks

Deep learning technology has a better result when trained using an abundant amount of data. However, collecting such data is expensive and time consuming. On the other hand, limited data often be the inevitable condition. To increase the number of data, data augmentation is usually implemented. By using it, the original data are transformed, by rotating, shifting, or both, to generate new data artificially. In this paper, generative adversarial networks (GAN) and deep convolutional GAN (DCGAN) are used for data augmentation. Both approaches are applied for diseases detection. The performance of the tea diseases detection on the augmented data is evaluated using various deep convolutional neural network (DCNN) including AlexNet, DenseNet, ResNet, and Xception. The experimental results indicate that the highest GAN accuracy is obtained by DenseNet architecture, which is 88.84%, baselines accuracy on the same architecture is 86.30%. The results of DCGAN accuracy on the use of the same architecture show a similar trend, which is 88.86%.

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Design and exploration of neural network microsystem based on SiP

SN Applied Sciences ◽

10.1007/s42452-021-04766-3 ◽

2021 ◽

Vol 3 (9) ◽

Author(s):

Hao Lv ◽

Shengbing Zhang ◽

Bao Deng ◽

Jia Wang ◽

Desheng Jing ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Power Consumption ◽

Convolutional Neural Network ◽

Research Field ◽

Structure Design ◽

Learning Technology ◽

Balance Performance ◽

Fpga Design ◽

Data Set

AbstractIn recent years, microelectronics technology has entered the era of nanoelectronics/integrated microsystems. System in package (SiP) and system on chip (SoC) are two important technical approaches for the realization of microsystems. Deep learning technology based on neural networks is used in graphics and images. Computer vision and target recognition are widely used. The deep learning technology of convolutional neural network is an important research field in the miniaturization and miniaturization of embedded platforms. How to combine the lightweight neural network with the microsystem to achieve the optimal balance of performance, size, and power consumption is a difficult point. This article introduces a micro-system implementation scheme that combines SiP technology and FPGA-based convolutional neural network. It uses Zynq SoC and FLASH and DDR3 memory as the main components, and uses SiP high-density system packaging technology to integrate. PL end (FPGA) design Convolutional Neural Network, convolutional neural network accelerator, adopt the method of convolution multi-dimensional division and cyclic block to design the accelerator structure, design multiple multiplication and addition parallel computing units to provide the computing power of the system. Improving and accelerating perform on the YOLOv2_Tiny model. The test uses the COCO data set as the training and test samples. The microsystem can accurately identify the target. The volume is only 30 × 30 × 1.2 mm. The performance reaches 22.09GOPs and the power consumption is only 0.81 W under the working frequency of 150 MHz. Multi-objective balance (performance, size and power consumption) of lightweight neural network Microsystems has realized.

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