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Research on Low-Resolution Face Image Recognition Based on Convolutional Neural Network

Research on Low-Resolution Face Image Recognition Based on C...

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作者： Tang, Hailin Qi, Yongjun Zhao, Ziguang Faculty of Megadata and Computing Guangdong Baiyun University Guangdong Guangzhou510450 China Mongolian University of Science and Technology Bayanzurkh District Ulaanbaatar13341 Mongolia

Face recognition is a commonly used biometric identification method, but in a complex environment, due to the low quality of imaging devices, the obtained face image has low resolution and contains little information. In this paper, an adaptive learning algorithm based on convolutional neural network is used. This algorithm uses each layer of convolutional neural network to effectively integrate local and global features, and at the same time reduces the problem of gradient correlation decline caused by the increase of network layers. In this paper, a SuperResolution Network (SRN) method is used to up-sample the trained image, and Bicubic sampling is used to improve the quality of the trained image. After the reconstruction of SRN, the recognition rate of ResNet (Residual Network) is obviously improved under the condition of low resolution, which proves that the method used in this paper is effective. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Construction of Zero Expansion Patent Data Analysis System Based on Genetic Algorithm

Construction of Zero Expansion Patent Data Analysis System B...

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作者： Qi, Yongjun Tang, Hailin Luo, Zihao Zhao, Ziguang Faculty of Megadata and Computing Guangdong Baiyun University Guangdong Guangzhou510450 China Mongolian University of Science and Technology Bayanzurkh District Ulaanbaatar13341 Mongolia

The identification of patent information has gradually become the focus of global attention. Patent information plays a very important role in the implementation of intellectual property strategy, the construction of an innovative country and the development of strategic emerging industries. In this paper, a zero-expansion patent data processing method based on genetic algorithm is adopted, which can effectively overcome the "zero distance" phenomenon that may appear in patent data and analyze patent data more accurately, thus effectively solving the error problem caused by too many zeros. The test of zero expansion patent data analysis system shows that the prediction of the number of patent applications for zero expansion model based on genetic algorithm shows that in the 19th month, the predicted data is 257 and the historical data is 248, the difference between them is 9, and the prediction difference is very low. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data accuracy

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Data Security Protection System in Human-Machine-Object Fusion Intelligent Demand Engineering System

Data Security Protection System in Human-Machine-Object Fusi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Yongjun Qi Chenggao Li Hailin Tang Junxuan Shen Faculty of Megadata and Computing Guangdong Baiyun University Guangzhou Guangdong China School of Information and Communication Technology Mongolian University of Science and Technology Ulaanbaatar Mongolia

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In the engineering system of human-machine physical intelligence requirements, the multi-source and high complexity of data pose significant security risks. Therefore, this article designs a comprehensive data security protection system. Firstly, this article chooses a dynamic encryption strategy based on role-based access control. Then, this article combines differential privacy algorithm to construct a privacy protection model to ensure the privacy of data during sharing and processing. Subsequently, this article proposes a trusted data transmission protocol. Finally, this article introduces federated learning techniques to further improve the security and robustness of data in distributed scenarios. The experimental results show that the system performs well in terms of data security, privacy protection, and processing efficiency. Especially under high-intensity attacks, the system successfully detected and prevented approximately 98% of tampering. In the above data conclusions, the research method has significant advantages in encryption performance, achieving a better balance between privacy protection and data accuracy, demonstrating strong application prospects.

关键词： Differential privacy Protocols Federated learning Data security Heuristic algorithms Robustness Encryption Data communication Requirements engineering Protection

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Instruction Ability Intelligent Evaluation System(ES) on Account of Neural Network Algorithm (NNA) 2

Instruction Ability Intelligent Evaluation System(ES) on Acc...

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2nd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2022

作者： Qi, Yongjun Tang, Hailin Guangdong Baiyun University Faculty of Megadata and Computing Guangdong Guangzhou China

ISBN: (纸本)9781665491112

For instruction, the teacher team is the most fundamental component. These resources are the main part of innovation and a sign of the excellence of the school. Over the years, many schools have reformed their appointment platform, phasing out the buildment and replacing it with the appointment platform. This, to a huge extent, broke the original 'big pot' platform. In view of this situation, the appraisal of tutors can occasionally do fair, reasonable has become an important subject. It is very necessary to build a logical and efficient harmony evaluation system(ES) of instruction ability or teacher appraisal model. This paper studies the instruction ability harmony ES on account of NNA, introduces the function and tenet of the instruction ability harmony ES, and explains the concept and tenet of the NNA. The test shows that the harmony instruction ability ES on account of NNA has a more efficient mark in the harmony instruction ability appraisal. © 2022 IEEE.

关键词： evaluation system harmony appraisal instruction ability neural network algorithm

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Analysis on the application of deep neural network model in the improvement of traditional PHP source code auditing tools

Analysis on the application of deep neural network model in ...

引用

2024 International Conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2024

作者： Ouyang, Xibiao Xu, Baolin Jiang, Jin Faculty of Megadata and Computing Guangdong Baiyun University Guangzhou510450 China

ISBN: (纸本)9781510685840

This study mainly discusses the application of deep neural network model in improving the traditional source code auditing tool of hypertext preprocessor (PHP). Under the traditional static and dynamic conditions, there are still many restrictions on the use of PHP vulnerability mining technology. This study will build a deep neural network model on the basis of improving the traditional PHP source code auditing tools, and take the improved S-ASTNN deep neural network as the starting point to fundamentally improve the PHP vulnerability mining method. This study will use the special structure of PHP abstract syntax tree to adjust it. It is found that the semantic information contained in the improved traditional ASTNN deep neural network can be effectively preserved and the model efficiency is improved. After analyzing the experimental results, it is found that the improved S-ASTNN deep neural network model has higher accuracy and recall rate than the traditional vulnerability mining method, and the improved S-ASTNN deep neural network can play a more significant role in the field of PHP language vulnerability mining. © 2024 SPIE.

关键词： Deep neural networks

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Identification and Analysis of Wind Turbine Blade Cracks Based on Multi-scale Fusion of Mobile Information Systems

Identification and Analysis of Wind Turbine Blade Cracks Bas...

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作者： Qi, Yongjun Tang, Hailin Faculty of Megadata and Computing Guangdong Baiyun University Guangdong Guangzhou510450 China

With the development of industry and the consumption of resources, environmental and energy issues have gradually become factors restricting the progress of human civilization. In recent years, people have been continuously researching and trying in the field of exploring and developing new energy. With the gradual consumption of coal, oil and other energy sources, wind energy, which is rich in reserves and has a long history of development, has gradually been developed and utilized by various countries. Wind energy can convert mechanical energy into electrical energy, contributing to the development of large-scale design of wind power blades in my country, and for the design of wind power blades. This paper mainly studies the crack detection problem of wind turbine blades based on the multi-scale fusion technology of mobile information system. By improving the detection efficiency of wind turbine blades, the hidden risks caused by the loss of generator blades can be reduced, and the safety performance of wind power generation can be improved. Multiscale fusion techniques for mobile information systems facilitate crack detection and analysis by analyzing relevant mathematical framework models and concepts such as invariant scale and invariant displacement based on statistical significance. The final result of the study shows that the total number of transverse cracks is 98, the number of identification is 95, the accuracy rate is 96.94%, the total number of longitudinal cracks is 86, the number of identification is 81, the accuracy rate is 94.17%, the total number of transverse cracks is 94.17%, and the total number of longitudinal cracks is 86. It is 84, the number of identifications is 79, and the accuracy rate is 94.05%. It can be seen from the experimental data that the identification accuracy rate of transverse cracks is the highest. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Wind power

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Research on Foreground Extraction and Target Detection and Tracking Algorithm of Surveillance Video Based on Deep Learning 2

Research on Foreground Extraction and Target Detection and T...

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2nd International Conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Ye, Peilei Guangdong Baiyun University Faculty of Megadata and Computing Guangdong Guangzhou510450 China

ISBN: (纸本)9798350323818

With the popularization of intelligent monitoring system, it is more and more important to detect remnants and crowd anomalies. At present, most of the anomaly detection methods of left-behind objects rely on tracking the object carriers, while the anomaly detection methods of crowds mostly judge the anomaly by detecting and tracking the trajectory of pedestrian targets and then analyzing whether their behaviors conform to the normal state. How to efficiently analyze the video data acquired by the video surveillance network by artificial intelligence is a frontier topic in the field of computer vision recently. Moreover, with the rapid development of computer science and technology and video surveillance hardware, the industry has higher and higher requirements for intelligent surveillance technology. Therefore, the quality standard of video target tracking and detection technology based on deep learning directly affects the quality of video target tracking based on deep learning. This paper mainly introduces three common methods of video target tracking and detection based on deep learning, and makes a comparative study of different methods, analyzing their advantages and disadvantages, hoping to provide some help for the further research of video tracking and detection algorithm. © 2023 IEEE.

关键词： Target tracking

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Plant Leaf Classification and Recognition Algorithm Based on Neural Network Technology 2

Plant Leaf Classification and Recognition Algorithm Based on...

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2nd International Conference on Distributed computing and Optimization Techniques, ICDCOT 2024

作者： Qi, Yongjun Tang, Hailin Zhao, Ziguang Guangdong Baiyun University Faculty of Megadata and Computing Guangdong Guangzhou510450 China Mongolian University of Science and Technology Ulaanbaatar Bayanzurkh Mongolia Guangdong Baiyun University Faculty of Megadata and Computing Guangdong Guangzhou China

ISBN: (纸本)9798350382952

Traditional methods for plant leaf classification often rely on manually designed feature extraction, leading to limited distinguishability and challenges in fully expressing the intricate structure and texture details of leaves. To overcome these limitations, this study proposes an enhancement to the Xception model, aiming to elevate the performance of plant leaf classification and recognition. The research leverages the Plant Village public dataset, focusing on tomato leaf images. Prior to model training, a meticulous preprocessing of the dataset is performed, ensuring a balanced representation of various leaf types. The dataset is then divided in a 3:1 format for training and testing purposes. The key improvement to the Xception model involves the incorporation of multi-scale convolution and global maximum pooling techniques. These modifications enhance the model's ability to capture the most significant features of leaves, both at intricate and comprehensive levels. As a result, the average accuracy of the refined Xception model achieves an impressive 98.6%. In conclusion, the study demonstrates that enhancing the Xception model significantly boosts the accuracy of tomato leaf classification. This advancement holds promise for broader applications in plant classification and underscores the importance of leveraging advanced deep learning techniques for intricate tasks in botanical research. © 2024 IEEE.

关键词： Textures

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Double Attention Mechanism Text Detection and Recognition Based on Neural Network Algorithm 6th

Double Attention Mechanism Text Detection and Recognition Ba...

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6th International Conference on Innovative computing, IC 2023

作者： Qi, Yongjun Tang, Hailin Huang, Li Faculty of Megadata and Computing Guangdong Baiyun University Guangdong Guangzhou510450 China Mongolian University of Science and Technology Bayanzurkh District Ulaanbaatar13341 Mongolia

ISBN: (纸本)9789819920914

How to effectively identify these signals and data has become an urgent topic. The neural network model is a stochastic system composed of nonlinear neurons. Therefore, it has strong self adaptability and controllability. This paper proposes a method based on training samples. It classifies the original continuous text through the artificial neural network algorithm. This paper mainly uses experimental method and comparative method to analyze the accuracy, precision, recall rate, F value and its trend in training and the results under different models. The experimental results show that good results have been achieved on the IMDB comment dataset, and the accuracy rate is close to 89.4%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Stochastic systems

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A Text Detection and Recognition System Based on Dual-Attention Mechanism with Artificial Intelligence Technology 6th

A Text Detection and Recognition System Based on Dual-Attent...

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6th International Conference on Innovative computing, IC 2023

作者： Qi, Yongjun Li, Chenggao Huang, Li Faculty of Megadata and Computing Guangdong Baiyun University Guangdong Guangzhou510450 China Mongolian University of Science and Technology Bayanzurkh District Ulaanbaatar13341 Mongolia

ISBN: (纸本)9789819920914

With the rapid development of Internet and artificial intelligence technology (AIT), a large number of social media tools such as Facebook, Twitter and Instagram have emerged, which not only provide a wide communication platform for Internet users, but also generate tens of thousands of text information with rich emotions. In this paper, we study and analyze the text detection (TD) and recognition system (RS) based on the dual-attention mechanism (DAM) under AIT, and discuss the text localization method, the generation of candidate text regions, and the key points of text extraction and recognition;we introduce the DAM to train the model by two different types of feature maps to improve the TD and recognition performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Character recognition

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