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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

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Steel Types Association Analysis in Traffic Construction 3

Steel Types Association Analysis in Traffic Construction

引用

3rd IEEE international conference on Power, Electronics and computer Applications, ICPECA 2023

作者： Yang, Zhiyao Fang, Shaohong Qin, Yuping Zhu, Jingrun Chen, Hao Huazhong Univ. of Sci. & Tech School of Software Engineering Wuhan China Wuhan Teng Road ITS Co. Ltd Wuhan China

ISBN: (纸本)9781665472784

Steel and connection joints are important and large amounts of materials for traffic engineering, research on the relationship between the use of different types of steel and connecting joints in the construction project is helpful for reasonable distribution and combination of them. The Apriori algorithm is used to analyze the association. First, according to the data engineering, some with fewer occurrences and unclear definitions are deleted in the original data, secondly, the association algorithm is used to select the frequent itemset and the maximum frequent itemset with high support, and then the association rules of the high index through the confidence level is found out, and finally, the association rules results with high lift in the association rules selected in the previous step are screened. © 2023 IEEE.

关键词： learning algorithms

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Detection of Cyberbullying Using Modified Dense Framework 10

Detection of Cyberbullying Using Modified Dense Framework

引用

10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Malik, Varun Mittal, Ruchi Singh, Vikram Mittal, Amit Singh, S Vikram Diwvedi, Shashi Prakash Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chaudhary Devilal University Department of Computer Science and Engineering Sirsa India Chitkara Business School Chitkara University Punjab India Amity School of Engineering and Technology Amity University Greater Noida India Lloyd Institute of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350382471

Cyberbullying has emerged as a significant societal concern, with the proliferation of digital communication platforms. Detecting and mitigating cyberbullying is crucial to ensure online safety and promote positive digital interactions. This study proposes an innovative approach for the detection of cyberbullying using a Modified Dense Framework (MDF). The Modified Dense Framework (MDF) introduced in this research is a deep learning architecture specifically tailored for cyberbullying detection. Unlike traditional methods, MDF combines the power of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in a novel way, enhancing the model's ability to capture intricate patterns within textual and multimedia content. The framework is trained on diverse datasets encompassing text, images, and videos, enabling it to effectively identify various forms of cyberbullying across multiple online platforms. The study demonstrates the effectiveness of MDF through rigorous experimentation and evaluation. Comparative analyses with existing state-of-the-art models highlight the superiority of MDF in terms of accuracy, precision, recall, and F1-score. Moreover, MDF exhibits remarkable resilience against adversarial attacks, ensuring robust cyberbullying detection even in challenging scenarios. © 2023 IEEE.

关键词： Cyberbullying Deep learning Detection Modified Dense Framework Robust Detection

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Performance Analysis of Adaptive Cruise Control Using Frequency Modulated Continuous Wave Radar under Rain Clutter 26

Performance Analysis of Adaptive Cruise Control Using Freque...

引用

26th international conference on computer and Information Technology, ICCIT 2023

作者： Shibly, Imam Hossain Zaman, Md. Maniruz Hossain, Md. Selim Hasan, Mia Mohammad Shoaib Rajshahi University of Engineering and Technology Dept. of Electrical and Electronic Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

This paper introduces a simulation platform for Adaptive Cruise Control (ACC) using Frequency Modulated Continuous Wave (FMCW) radar operating at 77 GHz. ACC relies on radar sensors for precise assessment of nearby vehicle distance and velocity. FMCW radar offers superior accuracy and range resolution, crucial for reliable object detection in diverse driving conditions. The results reveal remarkable improvements, such as the ability to detect up to 3 targets within a maximum range of 200 m, and achieving a minimum detectable velocity for moving targets as low as 0.0083 m/s. However, radar performance can suffer due to clutter. The rain clutter scenario is also performed in the model and observed how the signal gets attenuated. © 2023 IEEE.

关键词： Adaptive Cruise Control clutter doppler shift FMCW radar FMSK ghost effect

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Cloud Classification via Satellite Images using Residual Network

Cloud Classification via Satellite Images using Residual Net...

引用

2023 international conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Bark, Meryem Sena Akin, Osman Ostim Technical University Computer Engineering Department Ankara Turkey Ostim Technical University Artificial Intelligence Department Ankara Turkey

ISBN: (纸本)9798350322972

Identifying cloud shapes and predicting the weather based on these shapes is an important phenomenon for many people and businesses. Making a suitable definition for the clouds obtained from satellite images and revealing the cloud shapes will make things easier in weather forecasting. Although the prediction of cloud shapes using artificial intelligence has become widespread recently, the problem has still not been fully resolved due to complex clouds structure. This study addresses a problem that involves multi-label features in a training dataset of 5546 samples using a deep learning method. Clouds have four different types: fish, flowers, sugar, and gravel. In this multi-label classification pattern Resnet50 model, we studied images with 256x256 pixels, using and comparing two different loss functions, namely Binary Cross-Entropy and Weighted Binary Cross-Entropy. With the applied method, a success score of 69.1% was achieved and better results were obtained than the methods at the state-of-the-art level. © 2023 IEEE.

关键词： Weather forecasting

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Research on Abnormal Network Traffic Detection Method based on Machine learning 3

Research on Abnormal Network Traffic Detection Method based ...

引用

3rd international conference on Machine learning and computer Application, ICMLCA 2022

作者： Tan, Xiaobo Jia, Yumei College of Information Science and Engineering Ligong University Liaoning Shenyang China Department of Computer Science and Technology Shenyang Ligong University Liaoning Shenyang China

ISBN: (纸本)9781510664814

Aiming at the problem of high false detection rate and low detection rate of abnormal network traffic detection methods due to uneven data distribution of current network traffic datasets, a network abnormal traffic detection method based on CNN-GRU is proposed. The method firstly performs local feature learning on the data according to the local learning ability unique to the convolutional neural network (CNN), and replaces the fully connected layer with a global average pooling layer to reduce the feature dimension and parameters;secondly, it uses a gated recurrent unit (GRU) learns the time series features of the data to improve the nonlinear representation ability of the method;finally, for the imbalance between the data, the cross entropy loss function is optimized, and the penalty for abnormal class sample detection and normal class sample detection is passed. The adjustment of the weight increases the attention of the model to the abnormal samples, thereby improving the accuracy of the model detection. The simulation results show that the method can effectively improve the classification and detection performance. © 2023 SPIE.

关键词： Convolutional neural networks

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VDH: a dynamic honeynet technology based on game theory 4

VDH: a dynamic honeynet technology based on game theory

引用

4th international conference on Machine learning and computer Application, ICMLCA 2023

作者： Ma, Li Chen, Ming Liu, Liang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

As the complexity of cyber attacks increases, honeypot networks, acting as a proactive security mechanism, have become an important area of research in the field of cybersecurity. Although traditional honeynets offer a degree of defense, their limitations in dealing with complex and persistent cyber attacks, including a lack of flexibility, insufficient allure for attackers, and high maintenance costs, are increasingly exposed. This paper introduces a versatile dynamic honeynet architecture (VDH) to compensate for these deficiencies in existing honeynet technologies. By deploying in parallel with enterprise networks and dynamically generating honeypot nodes, the VDH architecture achieves flexible control and redirection of suspicious traffic, thus enhancing network protection capabilities. The paper further abstracts honeynet attack and defense behaviors into a non-cooperative incomplete information game model, and through precise modeling and analysis of the single-stage attack-defense game process, proposes a novel optimal configuration strategy for dynamic honeynets. Lastly, the paper designs and implements the VDH prototype system based on network function virtualization, and verifies the effectiveness and superiority of the VDH structure and configuration strategy. © 2024 SPIE.

关键词： Network function virtualization

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AI-Driven Deep Structured learning for Cross-Site Scripting Attacks

AI-Driven Deep Structured Learning for Cross-Site Scripting ...

引用

2023 IEEE international conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Abhishek, S. Ravindran, Rahulkrishnan Anjali, T. Shriamrut Amrita School of Computing Department of Computer Science and Engineering Amritapuri India VMware Inc India

ISBN: (纸本)9798350397208

A substantial danger of attacks from numerous sources, with varying intensity and sophistication, is posed by the worldwide nature of online applications. A code injection attack known as cross-site scripting (XSS) occurs on the client side or via web browsers. Cross-Site Scripting (XSS), a severe attack that may be used to change a user's or an organization's data, is typical in online applications. This paper suggests a hybrid convolutional neural network (CNN) and machine learning (ML) framework for quickly and effectively identifying the XSS attacks to enhance the security. According to the simulation findings, the proposed model is more effective than the existing detection techniques. It generates results for XSS attack categorization that are more than 99.9% correct and is exceptionally resistant to XSS attack. © 2023 IEEE.

关键词： Deep learning

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distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation 31

Distance-Adaptive Quaternion Knowledge Graph Embedding with ...

引用

31st international conference on Computational Linguistics, COLING 2025

作者： Wang, Weihua Liang, Qiuyu Bao, Feilong Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798891761964

Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet through either semantic matching or geometric distance scoring functions. However, it appears that semantic matching diminishes the separability of entities, while the distance scoring function weakens the semantics of entities. To address this issue, we propose a novel quaternion knowledge graph embedding model. Our model combines semantic matching with the geometric distance of entities to better measure the plausibility of triplets. Specifically, in the quaternion space, we perform a right rotation on head entity and a reverse rotation on tail entity to learn rich semantic features. We then utilize distance-adaptive translations to learn geometric distance between entities. Furthermore, we provide mathematical proofs to demonstrate our model can handle complex logical relationships. Extensive experimental results and analyses show our model significantly outperforms previous models on well-known knowledge graph completion benchmark datasets. Our code is available at https://***/llqy123/DaBR. © 2025 Association for Computational Linguistics.

关键词： Knowledge graph

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Aadhar UID Mask Detecting Tool Using CNN With Verhoeff Algorithm 9

Aadhar UID Mask Detecting Tool Using CNN With Verhoeff Algor...

引用

9th international conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Syed Musthafa, A. Monisha, B. Roshni, P. Sowmiyha, N. Sri Dharshini, K.R. M.Kumarasamy College of Engineering Department of Information Technology Tamil Nadu Karur India

ISBN: (纸本)9798350397376

Aadhaar UID Mask Detecting Tool is a computer vision system that uses convolutional neural networks (CNNs) and the Verhoeff algorithm to detect and extract masked Aadhaar UID numbers in digital images. The tool is designed to address the growing concern over the protection of personal information and privacy in the era of digital communication. The CNNs are trained to recognize patterns in the Aadhaar UID numbers and the Verhoeff algorithm is used to validate the extracted numbers. This tool can be used in various applications such as document scanning and verification, data protection and privacy, and information security. The use of a combination of machine learning and validation algorithms makes this tool robust and reliable for detecting masked Aadhaar UID numbers in images. © 2023 IEEE.

关键词： learning algorithms

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A Method of Capturing Control Based on Game Theory for Multiple Underactuated Underwater Vehicles

A Method of Capturing Control Based on Game Theory for Multi...

引用

2023 international conference on Artificial Intelligence and Automation Control, AIAC 2023

作者： Qi, Xue Bai, Xueyang Shi, Manhong Dong, Shanshan Pan, Hua Anhui Science and Technology University College of Information and Network Engineering Bengbu233000 China

ISBN: (纸本)9798350383805

This paper address the problem of capturing control of multiple underactuated underwater vehicles (UUVs). The capturing control algorithm is designed based on learning game theory. This algorithm inherits the learning game theory's nature, that is, simplification and convergence. The property of learning makes the algorithm robust and autonomous. At the same time, this algorithm also has collision avoidance and obstacle avoidance capabilities. Based on the algorithm and the line of sight method, the way point tracking controller is designed. Based on the Lyapunov stability theory and adaptive backstepping iterative design method, precise tracking control of the UUV speed, heading and distance is achieved. Finally, simulation results illustrate the validity of the proposed controller. © 2023 IEEE.

关键词： Game theory

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