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共 3282 条记录，以下是581-590 订阅

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Realization of Network Security Knowledge Base with Knowledge Graph 3rd

Realization of Network Security Knowledge Base with Knowledg...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Ouyang, Zepeng Li, Xiangyang Wang, Bing Ji, Zhenyan School of Software Engineering Beijing Jiaotong University No. 3 Shangyuan Village Beijing China Cyberspace Security Department China Railway Information Technology Group Co. Ltd. No. 10 Fuxing Avenue Beijing China

ISBN: (纸本)9789819616978

In the era of digital transformation, the sheer volume of network security data poses significant challenges in terms of organization and retrieval. Traditional search engines fail to provide contextual answers, necessitating the development of more advanced systems. This paper presents the design and implementation of a network security knowledge base enhanced with a knowledge graph. The proposed system aims to improve the efficiency of information management and retrieval for network security professionals. We discuss the multi-layer architecture of the knowledge base, which includes the UI, service, data access, and data storage layers. The knowledge graph is constructed through a four-layer architecture comprising the data, extraction, knowledge, and application layers. Using the Scrapy framework, we efficiently crawl and extract data from various online sources. This data is processed with the Jena tool and stored in Neo4j as structured triples, enabling complex queries and advanced analytic. Our system supports semantic queries, knowledge association discovery, and intelligent question answering, improving user experience and decision-making in network security management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Computer Vision and pattern recognition Technology on Account of Deep Neural Network 4th

Computer Vision and Pattern Recognition Technology on Accoun...

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4th international conference on machine learning, Image Processing, Network Security and data Sciences (MIND)

作者： Ren, Yiming Han, Jiake Chen, Chongyu Xu, Yang Bao, Tingxuan Shenzhen MSU BIT Univ Shenzhen 518172 Guangdong Peoples R China Shanghai Univ Shanghai 201900 Peoples R China Tianjin Univ Sci & Technol 1038 Dagu South Rd Tianjin Hexi Tianjin 300222 Peoples R China

ISBN: (纸本)9783031243660;9783031243677

With the rapid development of computer, computer vision technology is also making rapid progress. In this paper, deep neural network algorithm is used to improve the technology of computer vision, improve the visual effect, and at the same time, innovative algorithm structure, improve the identification of pattern recognition. Computer vision and pattern recognition is a very cutting edge technology that has given people very advanced tools for vision and recognition. At present, the technology can control more accurate image resolution and improve the ability of pattern recognition. This article mainly explains the process of using deep neural network algorithm to improve computer vision and pattern recognition from the internal mechanism, and reveals the working principle and internal mechanism of computer vision and pattern recognition technology application. data analysis proves that the pattern recognition application established by deep neural network algorithm performs very well in the field of vision and pattern recognition.

关键词： Deep neural network algorithm Computer vision pattern recognition Technical application

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AI Voice-Assisted Fitness Coach with Body Pose recognition 3rd

AI Voice-Assisted Fitness Coach with Body Pose Recognition

引用

3rd international conference on data Science, machine learning and Applications, ICDSMLA 2021

作者： Moulya, S. Pragathi, T.R. Kambali, Pandurang S. ISE RVCE Mysore Rd RV Vidyaniketan Post Karnataka Bengaluru560059 India ECE RVCE Mysore Rd RV Vidyaniketan Post Karnataka Bengaluru560059 India

ISBN: (纸本)9789811959356

The aim of this work was to develop an AI-controlled fitness trainer to tend to each user’s needs. It includes an AI-based voice assistant that acts as a virtual fitness trainer to guide the user in performing a certain routine of exercises, which was implemented through the use of NLP to recognize the user’s voice for commands to activate the trainer and body pose recognition to monitor the user’s postures for the workouts in real-time. This work combined two different features and collectively helped the user to workout in a more efficient way. The trainer can currently perform these operations over a set of 10 workouts—7 for muscle workouts and 3 for cardio. Once the workout session is complete, a bar plot consisting of all the exercises performed during that session is constructed and stored on the user’s device. The study below goes into further detail on the major insinuations for future fitness coach design and assessment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Natural language processing systems

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Effective Visualization and Trend Forecasting of Crime data Through Big data Analytics and mining 3

Effective Visualization and Trend Forecasting of Crime Data ...

引用

3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Mahendhiran, Pd Apsara, S.K. Vasudevan, Harshitha Nithish, S. Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Tamil Nadu Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350322842

A crime is an act that merits public condemnation and penalties, which is usually in the form of a fine or jail. It is a widespread social issue that affects a country's life style, economic progress, and credibility. People are at a worrisome point in terms of reducing criminal activity at a greater rate, as criminality rates have risen dramatically. If people can evaluate and predict the crime pattern and implement preventive steps ahead of time, crime rates can be lowered. In this research, the author has present Crime Cast, a system that analyses crime reports and anticipates trends and crime statistics based on a specified data set using deep learning techniques such as LSTM and DNN at a faster rate and with greater accuracy. Within a huge percentage of crime data, Big data Analytics is utilized to analyze and detect different crime patterns, their relationships, and trends. The information contains cases of kidnapping, murder, rape, stealing, and other crimes. With the help of the LSTM model and DNN several operations performed on the data. The expected outcomes will aid police and law enforcement agencies in clearer grasp crime trends, allowing them to track incidences and improve their decision-making processes. These results will allow the law enforcement and regulatory agencies to further immediately spot inappropriate behavior issues and provide information that will allow them to monitor activities and predict the likelihood of limiting the number of events, transporting property securely, and improving the dynamic cycle. © 2023 IEEE.

关键词： Crime

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Secured Face recognition System Based on Blockchain with machine learning 1

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3rd international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2022

作者： Krishnakumar, K. Saravanan, S. Naite-Ali, Amine Department of Multimedia VIT School of Design Vellore Institute of Technology Vellore India Université Paris-Est Créteil Créteil France

ISBN: (数字)9789819926022

ISBN: (纸本)9789819926015

Face recognition system has become an integral part of the security mechanisms used in the IoT applications. Smart cities and industries started deploying face recognition applications for security purposes through surveillance cameras and biometric systems. In computer vision, facial recognition has been a widely studied area and has become mature. Recent developments in hardware and deep learning usage for face recognition techniques improve accuracy and considerably reduce the processing time after training the model. This chapter proposes a face recognition system with blockchain for a secured and tamper-free facial recognition. The face recognition system uses VGG19 to train image data to identify the face in the image or in the video. VGG19 model is utilized to reduce training time and reduce the sample size. This system uses sample image data to train the model for recognizing the face. With blockchain, the data stored in the database for face recognition, is secured, and prevents unauthorized alteration in the data from hackers or any malicious activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Face recognition

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Image recognition Methods Based on Deep learning 1

引用

3rd international conference on 3D Imaging Technologies - Multidimensional Signal Processing and Deep learning (3DIT-MSP and DL)

作者： Zhang, Zehua Univ Calif San Diego 9500 Gilman Dr La Jolla CA 92093 USA

ISBN: (数字)9789811924484

ISBN: (纸本)9789811924484;9789811924477

With the development of science and technology worldwide, computer technology has gradually been widely used in various fields like the military and biomedical domains. In this situation, people's requirements for image recognition are increasing. However, the efficiency of manual image recognition is very low, and image recognition has always been a weak field in computer technology. This paper will introduce various deep learning models such as feedforward neural networks, convolutional neural networks, and recurrent neural networks. We will talk about how these models work and then compare and analyze these different networks. In this paper, we will try to train a convolutional neural network from a data set. The "dogs-vs-cats" data set from Kaggle will be used. In the "dogs-vs-cats" experiment part, some basic tasks in image processing will be discussed, such as feature extraction method, which is one of the most important steps in each link of image processing. Finally, the machine should be able to distinguish between pictures of dogs and cats. Moreover, we will also discuss some preliminary applications of deep learning in image recognition.

关键词： Deep learning Image recognition machine learning

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Third international conference on Digital Signal and Computer Communications, DSCC 2023

Third International Conference on Digital Signal and Compute...

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3rd international conference on Digital Signal and Computer Communications, DSCC 2023

ISBN: (纸本)9781510666559

The proceedings contain 73 papers. The topics discussed include: road traffic status discrimination method based on data fusion of multiple floating vehicles;traffic sign reflectivity detection method based on driver's visual recognition;a belief propagation cooperative localization algorithm in GNSS-loss environments;an integrated algorithm of transparency retrieval;research on 5G differential protection data compression transmission technology;optimization of radiation shielding scheme for Jupiter spacecraft based on genetic algorithm;bidirectional consistency of multi-agent based on data-driven;research on mung bean variety identification algorithm based on machine learning and hyperspectral technology;research and analysis of blockchain system based on computer big data model;and deep clustering model for time-series data based on recurrence plot and variational auto-encoder.

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Leveraging Neural Networks to Enhance Large-Scale data Processing Performance 3

Leveraging Neural Networks to Enhance Large-Scale Data Proce...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Pandey, Megha Vinay Kumar, S.B. Khurana, Jatin Kalpana, B. Pathak, Kishor R. Kumar, Mukesh Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India 1 Electronics and Communication Engineering Faculty of Engineering and Technology Karnataka Bangalore India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350319125

Recent advances in neural networks have enabled them to become powerful tools in data processing for tasks such as pattern recognition and classification. This has enabled neural networks to be applied to large-scale data processing problems, where they have been shown to perform very well. However, due to the large amount of data involved in such tasks, it is essential that the processing performance is also improved. This paper presents several methods to leverage available resources such as distributed computing and improved algorithmic techniques to improve the performance of large-scale data processing tasks using neural networks. Examples of such techniques are given for both supervised and unsupervised learning tasks, which can significantly enhance the performance of large-scale data processing applications. The results of several experiments are presented, demonstrating the effectiveness of the proposed techniques. The authors also provide insights for future work that could further enhance the capabilities of neural networks when applied to large-scale data processing applications. © 2023 IEEE.

关键词： Applications data Networks presented processing

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Surface defect detection of the power machinery based on deep learning 3

Surface defect detection of the power machinery based on dee...

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3rd international conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： Chen, Tao Zhao, Yutong Li, Bo Ding, Tianhong Cui, Mengmeng Shui, Chongyuan Sichuan Institute of Product Quality Supervision Inspection and Testing Chengdu610000 China The Key Laboratory of Power Machinery and Engineering of Education Ministry Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781510668522

With the rapid development of machine learning methods, deep learning models based on big data and computer arithmetic are widely used in various scenarios, including target detection and image recognition. In order to detect surface defects of the power machinery more rapidly and accurately, this paper investigates the performance of various convolutional neural network models in the task of detecting surface defects, identifies relatively excellent detection models based on the confusion matrix, and summarizes the more appropriate detection scenarios for different models. The results indicate that EfficientNet has good comprehensive performance in detection tasks. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Surface defects

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Predicting Student Attention And Engagement Through Eye-Tracking Using machine learning

Predicting Student Attention And Engagement Through Eye-Trac...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), international conference on

作者： Kirubakaran R Suganthi N Lokeshwaran V Akilan V E Shahidha S Department of Computer Science and Engineering Kumaraguru College of Technology Coimbatore India Department of Information Science and Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This paper presents a comprehensive approach to predicting student engagement and learning behavior by analyzing eye-tracking data combined with behavioral analysis using camera-based recognition algorithms. The proposed system leverages WebGazer for eye-tracking to monitor gaze patterns and uses computer vision algorithms to recognize behaviors and emotional states. Feelings like engagement, frustration, or perplexity are identified using facial expression analysis. The system aims to provide real-time, actionable insights to students and educators by relating to emotional and concentration states by combining different modalities, thereby improving personalized learning strategies and educational outcomes.

关键词： Emotion recognition Computer vision Gaze tracking machine learning Educational technology Prediction algorithms Real-time systems Telecommunication computing pattern recognition Monitoring

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