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检索条件"任意字段=2023 IEEE 3rd International Conference on Data Science and Computer Application, ICDSCA 2023"

共 3023 条记录，以下是541-550 订阅

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Research on Intrusion Detection Method Based on Machine Learning Algorithm and Big data Technology 3

Research on Intrusion Detection Method Based on Machine Lear...

引用

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

作者： Li, Junhua Geely University of China Sichuan Province Chengdu city641423 China

ISBN: (纸本)9798350319125

With the popularization and application of Internet technology, people are easy to steal valuable information by cyber attackers in the process of using computer networks and electronic products. In order to maintain a good network environment and protect people's interests, network security issues need to be solved. The role of intrusion detection technology in big data platform and feature selection is particularly important, but there is a problem of inaccurate result evaluation. With the rapid development of network technology, the problem of network security has become increasingly prominent. As an important part of network security, intrusion detection plays an important role in preventing network attacks and maintaining system stability. In recent years, the application of machine learning algorithm and big data technology in the field of network intrusion detection has gradually attracted wide attention. This paper mainly discusses the role of these technologies in the process of intrusion detection and its impact on detection results. © 2023 ieee.

关键词： Big data platform cybersecurity Machine learning algorithms neural network algorithms

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Segment anything model for subgrade distress detection with GPR

Segment anything model for subgrade distress detection with ...

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data science and computer application (icdsca), ieee international conference on

作者： Kangjie Ning Chunpeng Zhou Zhi Yu School of Software Technology Zhejiang University Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China School of Computer Science Zhejiang University Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China

The detection of distresses with ground-penetrating radar (GPR) images is a highly challenging task due to their complex structure, blurred boundaries, and varying scales. The Segment Anything Model (SAM) has demonstrated impressive performance in natural image segmentation tasks with its zero-shot segmentation capability and high efficiency, which effectively reduces annotation time. However, the applicability of SAM for GPR image analysis remains till now. To this end, we construct a GPR image dataset comprising 395 samples with segmention, which are jointly confirmed and annotated by multiple domain experts. We comprehensively evaluate the performance of various mainstream deep learning image segmentation models and different SAM models on this dataset, and analyze the results in detail. Our experiments confirm that SAM's performance in detecting underground radar distresses is limited or even completely inadequate. These findings demonstrate that SAM's current zero-shot segmentation capability is insufficient for direct application in subgrade distress with GPR data.

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Hybrid Beamforming Design Based Oversampling for 1-Bit Quantized Massive MIMO Systems 3

Hybrid Beamforming Design Based Oversampling for 1-Bit Quant...

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3rd ieee international conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Yu, Yingying Chen, Wenxun He, Qijun Yan, Junjie Yin, Zengzuo Qin, Huafeng Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou China Liuzhou Vocational & Technical College Liuzhou China Guangxi University of Science and Technology School of Electrical and Electronic Engineering Liuzhou China

ISBN: (纸本)9798350398410

In massive multiple-input multiple-output (MIMO) systems, data converters with low resolution are appropriate because of their low power consumption and cost. Moreover, oversampling for such systems has benefits for the achievable rate, with less increase of power consumption than high-resolution data converters. In this paper, we consider hybrid beamforming for oversampled and 1-bit quantized massive MIMO systems. Our purpose is to prove that this architecture can achieve the performance toward fully digital schemes. Numerical results also show the performance of the proposed architecture. © 2023 ieee.

关键词： Electric power utilization

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MSTP Network data Traffic Anomaly Optimization Detection Algorithm 3

MSTP Network Data Traffic Anomaly Optimization Detection Alg...

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3rd international Symposium on computer Technology and Information science, ISCTIS 2023

作者： Gong, Haoquan Liu, Chao Gao, Wenpeng Wang, Lidan Wang, Xuyang State Grid Gansu Electric Power Company Information and Communication Company Gansu Lanzhou730050 China

ISBN: (纸本)9798350325386

The current conventional network data traffic anomaly optimization detection algorithm mainly achieves the extraction of anomaly features by constructing a feature attribute matrix, which leads to poor detection results due to the lack of evaluation of feature importance. In this regard, the MSTP network data traffic anomaly optimization detection algorithm is proposed. By analyzing the mapping relationship between key values and query values, a self-attentive mechanism is constructed to extract network data traffic features. And by calculating the importance change value of feature data before and after adding random noise, feature importance evaluation is realized and anomaly optimization detection model is constructed. In the experiment, the designed model was tested for detection effectiveness. The final results can prove that the algorithm has a high AUC value and a more desirable detection performance when the proposed method is used for anomaly detection of network data traffic. © 2023 ieee.

关键词： Anomaly detection

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application of Deep Learning Based Longitudinal Tear Detection in Intelligent Main Transportation 3

Application of Deep Learning Based Longitudinal Tear Detecti...

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3rd international conference on computer science, Electronic Information Engineering and Intelligent Control Technology, CEI 2023

作者： Yu, Zhiqiang Automation Co. Ltd. Changzhou213003 China Middling Coal Science and Engineering Group Changzhou Research Institute Co. Ltd. Changzhou213003 China

ISBN: (纸本)9798350372052

The current traditional device for detecting longitudinal tearing of belts has a low level of intelligence, and the triggering mechanism is inherently prone to missed and false detections. This paper designs a longitudinal tear detection system based on deep learning, which takes YOLOv5s model as the deep learning model and low power consumption edge computing module as the carrier. The industrial intrinsic safety camera is used to obtain the image, which is transferred to the edge computing module for tear feature detection. The longitudinal tear length is calculated by fusing the belt speed sensor data, and only the external detection results are finally output. When the detected longitudinal tear length exceeds a certain threshold, the belt conveyor collaborative controller will control the sound and light alarm to give an alarm, and at the same time issue a shutdown command to the specific single controller. In addition, the edge computing module uploads the video stream and alarm data after image processing to the control platform for reference. This system deepens the intelligence level of longitudinal tear detection for the main conveyor belt in coal mines, ensures the safety of the coal production process, improves production efficiency, and reduces economic losses. © 2023 ieee.

关键词： Belt conveyors

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Real-Time Synchronized Positioning and Dense Map Construction from Feature-Based ORB-SLAM3 3

Real-Time Synchronized Positioning and Dense Map Constructio...

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3rd international conference on Artificial Intelligence and computer Information Technology, AICIT 2024

作者： Ye, Weijia He, Ning Liu, Yong Yuan, XiaoZhongLing Wu, Lie Yang Tan, Jing He, Donglin R and D Center The Second Research Institute of CAAC Chengdu China Civil Aviation Chengdu Electronic Technology Co. Ltd Chengdu China Jiangxi Traffic Monitoring Command Center Nanchang China

ISBN: (纸本)9798350365443

With the development of SLAM technology, many excellent SLAM algorithms have emerged, categorized into visual and laser SLAM, among others, based on the application scenario. This paper adopts the ORB-SLAM3 algorithm framework from visual SLAM. It optimizes the sparse point cloud data output by this algorithm, integrating reconstruction algorithms to ultimately produce visualized 3D dense point cloud data. The implementation primarily utilizes ORB-SLAM3's RGB-D mode. The semantic maps generated from further processing of this data can be applied in the field of augmented reality. © 2024 ieee.

关键词： SLAM robotics

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A Review of Adversarial Machine Learning for Cyber security in Intelligent Systems using Image Classification & Signal Classification Analysis 3

A Review of Adversarial Machine Learning for Cyber security ...

引用

3rd ieee international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Jahan, Tasneem Lnct University Department of Computer Science & Engineering Bhopal India

ISBN: (纸本)9798350343274

Machine learning models with adversarial examples must be trained to differentiate between genuine and invading data. Cyber computing technology and tools face huge intrusion attacks and malicious programs. Tampered QR code for online payments, malicious face and biometric detection, incorrect facial recognition, object and sign detection are the adversarial examples in real-life scenarios. Convolutional neural networks often fail to image classification tasks due to high vulnerability. The Intelligent systems comprising computer vision, self-driving cars, radio signals, IoT Devices, industrial production and automation are prone to cyber attack with adversarial data in machine learning models. The goal of this paper is to summarize the various Convolutional Neural Network models and Recurrent Neural Network approaches used in machine learning for malicious image classification. The analysis aims to identify the area of attacks using adversarial machine learning in cyber physical systems and intrusion detection systems. © 2023 ieee.

关键词： Cybersecurity

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Optimizing Drug Dosage and Medication Management for Geriatric Patients with Artificial Neural Networks 3

Optimizing Drug Dosage and Medication Management for Geriatr...

引用

3rd ieee international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Beemkumar, N. Singh, Daljeet Pal Ahmed, Zahid Faculty of Engineering and Technology Department of Mechanical Engineering Bagalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350343274

In this paper, we utilize ANNs to investigate how medicine dosing and management might be improved for the elderly population. The suggested strategy calls for the use of patient-specific data as inputs for the ANNs, such as age, renal and hepatic function, body mass index, and preexisting prescription profiles. The neural network model dynamically changes drug doses using the combination of the Multilayer Perceptron (MLP) algorithm, Backpropagation, and the Stochastic Gradient Descent (SGD) algorithm to maximize therapeutic benefits and reduce the likelihood of adverse drug events. The research highlights the improved accuracy, sensitivity, specificity, precision, and F1 score of the suggested technique compared to numerous conventional methods. As a result, the suggested system has great potential to revolutionize medication management for the elderly population since it is more personalized, flexible, patient-safe, effective, and efficient in its use of resources. © 2023 ieee.

关键词： Optimization

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Face Detection and Classification using Convolutional Neural Network with Channel Attention Module 3

Face Detection and Classification using Convolutional Neural...

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3rd ieee international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Almoussawi, Zainab Abed Kumar, K Sudheer Veena, T. Adnan, Myasar Mundher Jabar, Muhammed Basim College of Islamic Science Ahl Al Bayt University Karbala Zainb Iraq School of Computer Science & Artificial Intelligence SR University Telangana Warangal India S.A. Engineering College Department of Information Technology Chennai India The Islamic University Najaf Iraq National University of Science and Technology Dhi Qar Iraq

ISBN: (纸本)9798350317022

Automatic face detection is one of the most challenging in computer vision, FER is a wide range of application in human- computer interaction, behavior, and human expression. However, most of these related researches use general image classification network, which lead inadaptability while applying to face detection. This paper proposed a deep learning using Convolution Neural Network (CNN) with Channel Attention Module. The FER 2013 dataset is utilized in this research for effective classification of face detection. The data Augmentation is used in this experiment for data pre-processing and feature extraction using high feature generation pyramid (HFGP) and low feature generation pyramid (LFGP). Face detector using Single-Short multi biox detector (SSD) and ResNet 10 based face detection. Then, face detector are given imput to channel attention module classifer which utlized Deep Learning (DL) for effective classification. The obtained result show that the proosed DL using CNN model achieves better accuracy of 99.90% on FER 2013 dataset which ensure accurate classification compared to other existing methods like Engagement Index, Deep Neural Net and Zoning based model. © 2023 ieee.

关键词： Channel Attention module Convolution Neural Network Deep Learning ResNet 10 and Single- Short multi box Detector

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A Method for Automatic Registration of Realistic 3D Models without Control Points

A Method for Automatic Registration of Realistic 3D Models w...

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data science and computer application (icdsca), ieee international conference on

作者： Haixing Shang Wenyu Huang Sihong Li Xing Wen Northwest Engineering Corporation Limited Power China Group Xi’an China Xi’an Key Laboratory of Clean Energy Digital Technology Xi’an China The Tibet Electric Power Co. Ltd of CHN Energy Group Linzhi China

In order to realize the periodic surface monitoring and early warning of the multi-terrain realistic 3D model of the landslide, it is for the unmanned aerial vehicle to use multiphase optical images to measure the ground control points during each phase of image acquisition, and proposes an automatic registration algorithm for the terrain-real 3D model, to automatically establish a realistic 3D model of multiple time series with a unified spatial reference. The experimental results show that the average spatial difference of the realistic 3D model after registration is as low as 0.01m. This method is suitable for periodic dynamic surface monitoring and change detection scenarios of landslides and natural high and steep slopes, and has technical advantages such as high precision, low safety risk, low cost, and non-contact surface monitoring.

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