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Research on automatic news image matching based on BP neural network 4

Research on automatic news image matching based on BP neural...

引用

4th international conference on Artificial Intelligence and computer Engineering, ICAICE 2023

作者： Wang, Junhong School of Marxism Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9798400708831

With the current iterative upgrading of Internet technology and the significant increase in the scale of new media users, traditional paper media has been replaced by digital platforms. this paper proposes a new news content processing method - automatic news picture matching based on BP neural network, which overcomes the problems existing in traditional computer vision processing such as low processing efficiency, long processing cycle, matching not in place, etc., and further improves the computer's graphic matching *** neural network with the help of its own BP neural network with its own powerful deep learning ability and nonlinear mapping ability, constantly optimize the prediction results of the model, and ultimately achieve more efficient news automatically recommend the matching of pictures, which greatly promotes the innovation and development of the "machine + artificial"news information production mode. © 2023 ACM.

关键词： Mapping

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the Socface Project: Large-Scale Collection, processing, and Analysis of a Century of French Censuses 18th

The Socface Project: Large-Scale Collection, Processing, and...

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18th international conference on Document Analysis and recognition (ICDAR)

作者： Boillet, Melodie Tarride, Solene Schneider, Yoann Abadie, Bastien Kesztenbaum, Lionel Kermorvant, Christopher TEKLIA Paris France Inst Natl Etud Demog INED Paris France Paris Sch Econ PSE Paris France

ISBN: (纸本)9783031705427;9783031705434

this paper presents a complete processing workflow for extracting information from French census lists from 1836 to 1936. these lists contain information about individuals living in France and their households. We aim at extracting all the information contained in these tables using automatic handwritten table recognition. At the end of the Socface project, in which our work is taking place, the extracted information will be redistributed to the departmental archives, and the nominative lists will be freely available to the public, allowing anyone to browse hundreds of millions of records. the extracted data will be used by demographers to analyze social change over time, significantly improving our understanding of French economic and social structures. For this project, we developed a complete processing workflow: large-scale data collection from French departmental archives, collaborative annotation of documents, training of handwritten table text and structure recognition models, and mass processing of millions of images. We present the tools we have developed to easily collect and process millions of pages. We also show that it is possible to process such a wide variety of tables with a single table recognition model that uses the image of the entire page to recognize information about individuals, categorize them and automatically group them into households. the entire process has been successfully used to process the documents of a departmental archive, representing more than 450,000 images.

关键词： Handwritten table recognition Large-scale data collection Collaborative annotation

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vision Transformer based ResNet Model for Pneumonia Prediction 4

Vision Transformer based ResNet Model for Pneumonia Predicti...

引用

4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Sowmya, thatikonda Sai Narasimhulu, thumma Sunitha, Gurram Manikanta, thupakula Venkatesh, thirupathi Sree Vidyanikethan Engineering College Department of CSE A.P Tirupati India Mohan Babu University School of Computing Department of Ai & Ml A.P Tirupati India

ISBN: (纸本)9798350300093

this research study aims to develop a pneumonia detection system using vision transformers. Pneumonia is a very serious respiratory illness that may result in severe health issues, and early detection is essential for effective treatment. Deep learning-based computer vision algorithms have yielded encouraging results in medical image analysis in recent years, and vision transformers have emerged as a potent tool for processing visual data. the proposed system uses a vision transformer to process chest x-ray images and extract visual traits, which can be utilized for classification. the proposed model 'Vit_base_resnet50_224_in21k' is trained on a vast and diverse dataset of annotated chest x-ray images to understand the patterns and characteristics of pneumonia. the system's performance is estimated using standard evaluation parameters - accuracy, loss, specificity, sensitivity, F1-score and ROC curve. the proposed model has achieved a performance accuracy of 92.96% for pneumonia detection. the results demonstrate the potential of vision transformers in chest x-ray image analysis and contribute to the development of more accurate and efficient tools for pneumonia detection. this system has the potential to assist healthcare professionals in making faster and more accurate diagnosis, which can ultimately improve outcomes and save lives. © 2023 IEEE.

关键词： image analysis

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Multimodal Fusion Intelligent Driving Fatigue Monitoring and Warning in Police Traffic Scenarios 4

Multimodal Fusion Intelligent Driving Fatigue Monitoring and...

引用

4th international Signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Chen, Guancheng Chen, Long Tan, Kaiwen Xie, Pinxian School of Criminal Investigation People’s Public Security University of China Beijing China School of Computer Science and Engineering Wuhan University of Engineering Wuhan China School of Traffic Management People’s Public Security University of China Beijing China

ISBN: (纸本)9798331528676

the study presents a multimodal fusion intelligent driving fatigue monitoring and warning system tailored for police traffic scenarios. this system integrates diverse data modalities, including facial expressions, body posture, heart rate, and speech, to achieve comprehensive and precise fatigue detection. Leveraging edge-cloud collaborative computing and infrared image recognition technology, the system operates efficiently across various environmental conditions, enhancing its reliability and stability. the system's adaptability is further demonstrated through its compatibility with different levels of autonomous driving (L1 to L5), offering customized solutions that range from assisted to fully autonomous driving. Specially designed for police operations, the system provides features such as fleet management, equipment integration, and real-time connectivity to command centers, which collectively improve the safety and effectiveness of law enforcement tasks. Compared to traditional single-modality systems, this multimodal approach significantly enhances detection accuracy, stability, and functional diversity, positioning it as a competitive solution for widespread application in both general and specialized driving contexts. ©2024 IEEE.

关键词： Fleet operations

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Bio-inspired visual neural network for stop-and-go wave perception in high-density crowd 4

Bio-inspired visual neural network for stop-and-go wave perc...

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4th international conference on computer vision, Application, and Algorithm, CVAA 2024

作者： Jiang, Wei Hu, Bin State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Department of Computer Science College of Computer Science & Technology Guizhou University Guiyang550025 China Artificial Intelligence Research Institute of Guizhou University Guizhou University Guiyang550025 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

In high-density crowd, a unique visual motion effect called stop-and-go wave occurs, which could evolve to trampling and compression incidents. However, few computational models have been reported for stop-and-go wave perception form the angle of computer vision. On the bases of the neural structures of the locusts’ vision systems and the frequency resonance properties of the interneuronal (INs) neurons in locust’s brains, this paper investigates a bio-inspired visual neural network (sgWPNN) for stop-and-go wave perception in high-density crowd scenes. the proposed sgWPNN firstly visually stabilizes the image frames;and then converts the spatial intensity visual information to the frequency domain for extracting crowd’s motion cues;finally, sgWPNN output membrane potential impulses to identify the occurring of stop-and-go wave in the field of view. Videos filmed high-density crowd activities in two scenarios demonstrate the effectiveness of sgWPNN in the perception against to the unique stop-and-go wave effect in moving crowd scenes. this work discusses the biological inspired processing of dynamic visual information in crowd activity perception, which can provide some new ideas for crowd activity detection and behavior analysis. © 2025 SPIE.

关键词： Frequency domain analysis

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Leveraging Semi-Supervised Learning to Enhance Data Mining for image Classification under Limited Labeled Data 4

Leveraging Semi-Supervised Learning to Enhance Data Mining f...

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4th international conference on Electronic Information Engineering and computer Communication, EIECC 2024

作者： Shen, Aoran Dai, Minghao Hu, Jiacheng Liang, Yingbin Wang, Shiru Du, Junliang University of Michigan Ann Arbor United States Columbia University New York United States Tulane University New Orleans United States Northeastern University Seattle United States Dartmouth College Hanover United States Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798331534622

In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, their performance is greatly limited. this study optimizes data mining algorithms by introducing semi-supervised learning methods, aiming to improve the algorithm's ability to utilize unlabeled data, thereby achieving more accurate data analysis and pattern recognition under limited labeled data conditions. Specifically, we adopt a self-training method and combine it with a convolutional neural network (CNN) for image feature extraction and classification, and continuously improve the model prediction performance through an iterative process. the experimental results demonstrate that the proposed method significantly outperforms traditional machine learning techniques such as Support Vector Machine (SVM), XGBoost, and Multi-Layer Perceptron (MLP) on the CIFAR-10 image classification dataset. Notable improvements were observed in key performance metrics, including accuracy, recall, and F1 score. Furthermore, the robustness and noise-resistance capabilities of the semi-supervised CNN model were validated through experiments under varying noise levels, confirming its practical applicability in real-world scenarios. © 2024 IEEE.

关键词： Self-supervised learning

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Research on X-ray weld seam image quality evaluation methods 4

Research on X-ray weld seam image quality evaluation methods

引用

4th international conference on Signal processing and computer Science, SPCS 2023

作者： Wang, Ruifeng Gao, Weixin Deng, Guohao Xiao, Yang Li, Yuqing School of Electronic Engineering Xi'an Shiyou University Shaanxi Xi'an710065 China

ISBN: (纸本)9781510672505

the automatic assessment of X-ray weld image quality is an important basis for the automatic assessment of weld image defects. For ring weld X-ray welds, inspectors judge the image quality through human vision and physical blackness meter, which can easily lead to staff fatigue and thus misjudgment, to achieve an automatic assessment of X-ray weld image quality, this paper combines the subjective evaluation results with objective evaluation algorithms to propose a digital blackness meter model suitable for automatic assessment calculations, and gives the ring weld quality assessment algorithm the results show that the sensitivity of the proposed method can reach 98%. the results show that the proposed method has a sensitivity of 98. 5% and a specificity of 100%. © 2023 SPIE.

关键词： Densitometers

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image Super-Resolution with Multi-scale Hybrid Attention 27th

Image Super-Resolution with Multi-scale Hybrid Attention

引用

27th international conference on pattern recognition, ICPR 2024

作者： Wang, Ningzhi Shi, Hanyi Ruan, Wenna Zeng, Lingbin School of Artificial Intelligence Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Field Engineering College Army Engineering University of PLA Nanjing China National University of Defense Technology Changsha China

ISBN: (纸本)9783031784972

Super-resolution reconstruction stands as a critical task within the domain of computer vision. To enhance texture information extraction and improve visual perception, we introduce the Multi-Scale Hybrid Attention Network (MSHA), a novel single-image super-resolution model engineered to augment image global processing capabilities while accentuating detail reconstruction. the MSHA architecture seamlessly integrates a Multi-Scale Feature Block (MFB) for comprehensive feature extraction and a Parallel Hybrid Attention Module (PHA) for refining detail reconstruction capabilities. through extensive experimentation and comparative analyses against state-of-the-art models, we substantiate the superior performance of the MSHA network in producing high-quality super-resolution images. Our methodology effectively addresses the shortcomings of existing approaches by emphasizing multi-scale feature extraction and detail reconstruction, thereby significantly advancing the field of image super-resolution. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multi-scale Feature Extraction Parallel Hybrid Attention Single image Super-Resolution

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Agility of in Deep Learning Algorithms in Developing Conflicting Attacks Protections and to Secure Vulnerabilities 4

Agility of in Deep Learning Algorithms in Developing Conflic...

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4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Asaad, Renas Rajab Abdulrahman, Saman Mohammed Hani, Ahmed Alaa Ahmad, Hawar Bahzad Sallow, Amira Bibo Zeebaree, Subhi R. M. Nawroz University Department of Computer Science Duhok Iraq Nawroz University College of Science Department of Computer Science KRG Duhok Iraq Duhok Polytechnic University Technical College of Engineering Energy Engineering Department Duhok Iraq

ISBN: (纸本)9798350360165

Deep learning methods have brought revolution to a variety of applications: from image recognition to natural language processing, and many others. However, their most important vulnerability, when used in adversarial conditions, is a matter of concern that remains up to today. Most importantly, adversarial attacks insinuate the design of input in a way to be capable of bringing errors in the model, hence exposing significant vulnerabilities. this paper investigates the nature of such vulnerabilities and proposes the enhancement of model robustness through comprehensive methodologies. First of all, adversaries would be considered under the threat landscape and classified according to their complexity and impact, should one materialize. the second section would detail all available defense mechanisms, again classified based on strengths and limitations. We propose using the ensemble of adversaries, regularization methods, and dynamic network architecture. We validate this using the empirical testing of the technique over several datasets and models, subjected to a battery of rigorous tests. © 2024 IEEE.

关键词： Adversarial machine learning

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Plant image Disease Detection Using Deep Learning 4

Plant Image Disease Detection Using Deep Learning

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Mishra, Devansh Pandey, Alok Deepanshu Sharma, Vivek Galgotias University Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9798350300888

Crop diseases present a significant challenge to food security, but the lack of infrastructure in many regions makes timely diagnosis difficult. However, the widespread adoption of smartphones and recent advancements in computer vision, powered by deep learning, have opened up new possibilities for disease detection with the help of smartphones. through the utilization of a deep convolutional neural network, trained on a publicly available dataset of 54,306 images featuring damaged and healthy plant leaves obtained under controlled conditions, it becomes possible to recognize 14 crop species and identify 26 different illnesses or determine if the plant is healthy. this deep learning model, boasting an impressive accuracy rate of 98.59%, proves to be the most effective artificial neural network topology for detecting plant diseases. By following a strategy that involves developing deep learning models on progressively larger image datasets and making them accessible to the public, the path is paved for widespread smartphone-assisted crop disease diagnosis on a global scale. Deep Convolutional Neural Networks (CNNs) have shown remarkable performance in image recognition tasks, including plant disease detection. these models are trained on large datasets of labeled images to learn patterns and features indicative of different diseases. © 2023 IEEE.

关键词： Smartphones

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