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检索条件"任意字段=2022 International Conference on Algorithms, Microchips and Network Applications"

共 1155 条记录，以下是61-70 订阅

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Facenet: A Cloud-Based Application Framework for Social network

Facenet: A Cloud-Based Application Framework for Social Netw...

引用

IEEE international conference on Electrical Engineering, Big Data and algorithms (EEBDA)

作者： Hu, Yiteng Hangzhou Dianzi Univ Sch Honor Hangzhou 31000 Peoples R China

ISBN: (纸本)9781665416061

With the fast development of social media, the social relationship between people becomes more and more complex, and the social network becomes bloated. In this work, motivated by the six degrees of separation theory and with the help of cloud database, a social network model called facenet is built, which clearly represents the social relationship between people. First, a mathematical model is designed to estimate the diameter of one specific social network, leading to a random sparse graph. On this basis, a unique social platform is established. It has cutting-edge augmented reality (AR) functionality, allowing users to easily find the shortest social connection between themselves and any stranger in the world. Furthermore, an application framework is designed to illustrate the software architecture of the facenet and the relationship between various modules. Users can create their own cloudbased AR applications through this framework. Experimental results in the real world show that the proposed face recognition model achieves a recognition accuracy of 91.2% and great potential and extensibility.

关键词： social network augmented reality six degrees of separation convolutional neural network

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Intrusion Detection Systems Based on Stacking Ensemble Learning in VANET 5

Intrusion Detection Systems Based on Stacking Ensemble Learn...

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5th international conference on Communications, Signal Processing, and their applications (ICCSPA)

作者： Behravan, Mahshid Zhang, Ning Jaekel, Arunita Kneppers, Marc Univ Windsor Windsor ON Canada Telus Communications Inc Edmonton AB Canada

ISBN: (纸本)9781665482370

Vehicular ad-hoc network (VANET) will play an important role in improving driving safety and efficiency in transport systems. As various attacks arise in VANET, it is essential to design mechanisms that can detect these attacks and then mitigate them. In this paper, we make an effort to detect five different position falsification attacks in VANET, including constant attack, constant offset attack, random attack, random offset attack, and eventual stop attack. Two detection systems based on ensemble machine learning algorithms, including stacking ensemble learning algorithms for classification and stacking ensemble learning for neural network, are proposed. We extracted the most important features by performing feature importance techniques. Then, we train the proposed learning algorithms on VeReMi dataset which includes five different position falsification attacks with three traffic densities and three attacker densities. Extensive experimental results are provided to evaluate the proposed solutions' effectiveness. Based on our results, stacking ensemble learning for classification algorithm can achieve the best performance in terms of accuracy and recall. In low density traffic, accuracy and recall of stacking ensemble learning for classification algorithms are 1 for the constant attack, constant offset attack, and random attack. Accuracy and recall for the random offset attack are 0.999 and 0.996, respectively. For the eventual stop attack, accuracy and recall are 0.995 and 0.985, respectively. In medium density, accuracy and recall of stacking ensemble learning also achieve the best performance.

关键词： Intrusion detection system Stacking Ensemble learning VeReMi dataset Stacking for neural network

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Research on Mobile network User Complaint Warning Method Based on Multimodal Data 8

Research on Mobile Network User Complaint Warning Method Bas...

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IEEE 8th international conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Di, Zixiang Xiao, Tian Li, Bei Peng, Yunfeng Li, Jianfei Cheng, Xinzhou Xu, Lexi Zhu, Xiaomeng Zheng, Hangbo China United Network Commun Corp Res Inst Beijing 100037 Peoples R China Univ Sci & Technol Beijing Beijing 100083 Peoples R China Zhejiang Airport Digital Technol Co Ltd Hangzhou 310051 Peoples R China

ISBN: (纸本)9798350319514

With the rapid development of information and communication technology, the types of service applications for mobile terminals are becoming increasingly diverse. Each application has varying requirements for network performance, and each user's preferences, usage frequency, and sensitivity are different. When the quality of the operator network cannot meet the specific service needs of users and exceeds the threshold that users can tolerate, it will trigger user complaints. This article proposes a method for mobile network user complaint warning based on multimodal data. The method can be used to identify potential complaint users in the network and reduce the number of user complaints. In this method, a multimodal user level application rating feature dataset is constructed based on multi-source data, combined with factors such as user preferences and usage perception. Then, AI algorithms and great network computing power are used to generate complaint prediction models for different service applications at the user level. By integrating multiple application business complaint models at the user level and comprehensively evaluating user complaint expectations, it is used to improve the accuracy of user complaint warning.

关键词： Mobile service applications Artificial intelligence algorithms Complaint warning Multi-source data Multimodal

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Image Style Transfer Using Deep Learning Methods

Image Style Transfer Using Deep Learning Methods

引用

IEEE international conference on Electrical Engineering, Big Data and algorithms (EEBDA)

作者： Ren, Sihan Sheng, Yiwei Nanjing Normal Univ Dept Comp & Elect Informat Nanjing Peoples R China North China Elect Power Univ Dept New Energy Beijing Peoples R China

ISBN: (纸本)9781665416061

Image style transfer is an increasingly popular technology that can learn the style of an existing picture through neural network algorithms and apply this style to another picture. It is widely used in the field of art, such as oil painting, cartoon animation production, image season conversion and text style conversion. Meanwhile, deep learning methods are attracting more and more attention both in research and applications in various areas. In this paper, we give an overview on current research progress and results of image style transfer using deep learning methods. The deep learning methods are categorized into Convolutional Neural networks (CNN) and Generative Adversarial networks (GAN). As for CNN methods, we mainly talk about models based on VGG;and in terms of GAN methods, conditional GAN, Cycle GAN, and cartoon-GAN methods are contained. Finally, we summarized the shortcomings of the current results and the future study direction.

关键词： image style transfer deep learning convolutional neural network generative adversarial network

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Pointer network Solution Pool : Combining Pointer networks and Heuristics to Solve TSP Problems 3

Pointer Network Solution Pool : Combining Pointer Networks a...

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3rd international conference on Computer Vision, Image and Deep Learning and international conference on Computer Engineering and applications, CVIDL and ICCEA 2022

作者： Shi, Chenxu Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665459112

This paper use pointer networks to improve the quality of initial solutions generation to solve slow convergence problems and the tendency to fall into local optimal solutions when solving path planning problems by the heuristic algorithm. The results show that the convergence speed and the optimisation outcome of the optimised algorithm are improved and can be effectively used to improve the application of heuristic algorithms such as VNS to the travelling salesman problem. © 2022 IEEE.

关键词： Heuristic algorithms

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Gas Discrimination Analysis of Neural network algorithms for a Graphene-Based Electronic Nose 10

Gas Discrimination Analysis of Neural Network Algorithms for...

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10th IEEE international conference on Computational Intelligence and Virtual Environments for Measurement Systems and applications (IEEE CIVEMSA)

作者： Schober, Sebastian A. Carbonelli, Cecilia Wille, Robert Johannes Kepler Univ Linz Insitute Integrated Circuits Linz Austria Tech Univ Munich Chair Design Automat Munich Germany SCCH Hagenberg Austria

ISBN: (数字)9781665434454

ISBN: (纸本)9781665434454

Electronic noses, such as chemiresistive gas sensors, are important tools for environmental monitoring, public health and food quality. Throughout the measurement process, pattern recognition algorithms are used to map the electrical sensor measurements to a gas concentration. When analyzing gaseous mixtures with such devices, cross-sensitivities are likely to occur, which are related to the selectivity of the sensor materials. In the prediction process, cross-sensitive sensor signals can lead to false positive predictions and a loss in measurement accuracy. In this work, we thoroughly study the impact of the degree of crosssensitivity on the gas concentration prediction performance of neural networks in graphene-based gas sensor arrays. The study was conducted by using a simulation model of a chemiresistive gas sensor to simulate an array of two sensors with different levels of cross-sensitivity to two different target gases. Subsequently, two neural network algorithms were trained and evaluated on the datasets with the varying cross-sensitivity levels. Our analysis shows that a certain threshold regarding the independence of the array signals is necessary in order to ensure a sufficient gas discrimination and concentration estimation performance of the applied machine learning algorithms. Furthermore, it was also observed that a combination of a highly and poorly selective sensor, as implemented by filtering techniques, can also provide adequate results, when using suitable algorithms.

关键词： electronic nose graphene simulation gas sensor neural networks

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An Investigation into the Methods and applications of Deep Learning in Smart Grid 10

An Investigation into the Methods and Applications of Deep L...

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10th IEEE Jubilee international conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2022

作者： Rituraj, Rituraj Obuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary

ISBN: (数字)9781665481779

ISBN: (纸本)9781665481779

The manuscript represents the state-of-the-art review of the deep learning methods for smart grid applications. This paper reviews novel applications of deep learning algorithms in smart grid. The deep learning based three algorithms i.e., Long-Short Term Memory, recurrent neural network, convolution neural network found to be most useful in smart grid application. These algorithms are found to be most useful for forecasting, cyber-attack, anomaly detection and electricity theft in smart grid. This paper briefly surveys the most usable deep learning algorithms for making the smart is resilience, accurate, and safe. The review result shows that the mentioned deep learning algorithms give an excellent results over other deep learning algorithm. Therefore, these three algorithms are widely acceptable for the evaluation of smart grids. © 2022 IEEE.

关键词： Long short-term memory

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Ancient Character Image Classification Model Training 3

Ancient Character Image Classification Model Training

引用

3rd international conference on Computer Vision, Image and Deep Learning and international conference on Computer Engineering and applications, CVIDL and ICCEA 2022

作者： Lin, Yixuan Fuzhou University Fuzhou350108 China

ISBN: (纸本)9781665459112

Nowadays, various neural network models are updated, and most industries around the world need deep learning algorithms to solve a lot of practical problems. In this paper, we propose the task of image recognition of ancient Chinese characters based on RESNET network model, in order to provide help for students to learn ancient Chinese characters. In the work, the classification of five ancient Chinese characters is completed. The results of RESNET network model are very good, and the accuracy of the final result of the test set is 90%. At the same time, the stability of the model was tested after training, including vertical and horizontal flipping of the image of the test set, and adding noise to the image of the test set. Finally, the RESNET network model is summarized and its applicable environment is described. © 2022 IEEE.

关键词： Neural network models

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network Traffic Prediction Based on a CNN-LSTM with Attention Mechanism 7

Network Traffic Prediction Based on a CNN-LSTM with Attentio...

引用

7th international conference on Computational Intelligence and applications, ICCIA 2022

作者： Wu, Juan Qiu, Tingfeng Tang, Hong Liu, Xiaojun Research Institute of China Telecom Co. Ltd. Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781665495844

With the increase of Internet users and the rapid development of various large traffic network applications such as video and games, the network traffic load increases sharply, and network congestion occurs from time to time. Existing routers cannot solve this problem. network expansion is a costly solution, so intelligent routers should be adopted. Traffic prediction is the precondition of intelligent routing algorithms. Because network traffic has strong nonlinear characteristics, the traditional linear model has great error in predicting network traffic. Machine learning algorithms such as support vector regression (SVR), long-term and short-term memory network (LSTM) and convolutional neural network long-term and short-term memory network (CNN-LSTM) have achieved certain results, but their prediction accuracy still can not meet the requirements of practical application. In this paper, a CNN-LSTM with attention mechanism is used to efficiently extract timing information and other relevant feature information, so as to achieve a more accurate prediction of network traffic. The experiment of this paper used the network traffic data of China Telecom in Guangdong Province as an example to predict. The results show that the performance of the model is satisfactory. © 2022 IEEE.

关键词： Forecasting

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A Comparative Analysis of the Impact of Cryptography in IoT LoRa applications 20

A Comparative Analysis of the Impact of Cryptography in IoT ...

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20th IEEE international conference on Industrial Informatics (INDIN)

作者： Lino, Ines Cecilio, Jose Univ Lisbon Fac Ciencias LASIGE Lisbon Portugal

ISBN: (数字)9781728175683

ISBN: (纸本)9781728175683

The Internet of Things has been applied in different application contexts, ranging from industrial, healthcare or simple home life applications. It allows adding a new level of automation to objects, making them appealing to everyone. Since several applications involve data, and in some cases, it is sensitive, those applications are more vulnerable to be attacked. As a result, researchers are constantly exploring secure mechanisms to keep the data and its owners safe. For the resource-constrained nature of these IoT applications, the design of cryptography solutions becomes a challenging task because IoT nodes should be cheap and require low power, which means lower computational performance. Lightweight cryptography algorithms are attractive solutions to reduce computation complexity, keeping the desired level of security. This work provides an experimental performance analysis of several cryptography solutions targeted for embedded platforms to help choose better algorithms for IoT systems in terms of efficiency. Execution time, code efficiency and communication influences are evaluated for a set of cryptography algorithms. The results of this work intend to help IoT developers to choose a suitable cryptography algorithm for their IoT applications.

关键词： IoT applications Lightweight cryptography Communication security Performance evaluation

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