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Research on Identification of Cryptographic Algorithms based on BERT Model and BPNN 4

Research on Identification of Cryptographic Algorithms Based...

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4th IEEE international conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Wu, Lijin Chen, Bojian He, Jindong Lin, Chenxiang Lei, Shanshan Ye, Huimin Xie, Xinzhi State Grid Fujian Electric Power Co. Anxi350007 China

ISBN: (纸本)9798350363586

With the widespread adoption of internet technology and advancements in computer application technology, information security issues in large-scale information networks, such as power networks, have become increasingly prevalent. To address the challenges posed by extensive user privacy data introduced by IoT terminals in power networks, this study focuses on the effective identification of passwords for power network information system terminals. By integrating the random forest algorithm with two machine learning algorithms, a novel feature recognition model for grouped passwords in power information systems is proposed. Experimental results indicate that the proposed grouped ciphertext feature recognition model achieves the highest accuracy in image classification and recognition, followed by normal text, with the lowest accuracy for random text. Nevertheless, the model's classification accuracy for random text also exceeds 60%. Compared to the unimproved machine learning model, classification accuracy increased by 13% to 25%. Compared with other popular ciphertext feature recognition models, the proposed model demonstrates the best key sensitivity performance, with a maximum key sensitivity value of 1.6 and a minimum value of -0.9. Consequently, it is evident that the ciphertext feature recognition model developed in this research exhibits excellent key extraction and recognition performance, which can effectively enhance the information security of power network information systems. © 2024 IEEE.

关键词： Ciphertext

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All-optical tunable integrator based on microring resonator assisted by GST superlattice 2

All-optical tunable integrator based on microring resonator ...

引用

2nd international conference on Optical Communication and Optical Information Processing, OCOIP 2024

作者： Zhong, Wentao Dong, Yida Shen, Mei Lei, Lei Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Shenzhen518060 China China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China Academy for Advanced Interdisciplinary Studies Department of Materials Science and Engineering Southern University of Science and Technology Shenzhen518000 China

ISBN: (数字)9781510688995

ISBN: (纸本)9781510688988

This paper presents the design and simulation analysis of a tunable optical integrator based on ZnS-SiO2/Ge2Sb2Te5 superlattice (GST-SL) phase change material (PCM). The transfer function of the integrator is realized through a silicon microring resonator cladded with a three-layer GST-SL (GST-SL3-MRR). The integration of the PCM into the microring resonator enables a reconfigurable Q-factor, allowing for tunable integration orders between 0.5th and 1st. Through laser-induced phase transitions between amorphous and crystalline states, the transmission spectrum of the MRR can be dynamically adjusted. Simulation results show that the integration time window reaches 300 ps for the first-order integration, and the tunable integration results closely align with the ideal mathematical predictions. demonstrating the device's highly tunable performance. Further simulations of applications for quickly solving ordinary differential equations highlight the potential applications of our device in areas such as optical signal processing and image recognition. © 2025 SPIE.

关键词： Zinc Selenide

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Research on the Implementation of Real-Time Intelligent Detection for Illegal Messages based on Artificial Intelligence technology 11

Research on the Implementation of Real-Time Intelligent Dete...

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11th international conference on Communications, Circuits and systems (ICCCAS)

作者： Zeng, Ranran Lin, Yue Li, Xiaoyu Wang, Lei Yang, Jie Zhao, Dexin Su, Minglan China Telecom Corp Res Inst Dept Big Data & Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781665469890

In recent years, the detection of illegal and harmful messages which plays an significant role in internet service is highly valued by the government and society. Although artificial intelligence technology is increasingly applied to actual operating systems, it is still a big challenge to be applied to systems that require high real-time performance. This paper provides a real-time detection system solution based on artificial intelligence technology. We first introduce the background of real-time detection of illegal and harmful messages. Second, we propose a complete set of intelligent detection system schemes for real-time detection, and conduct technical exploration and innovation in the media classification process including detection model optimization, traffic monitoring and automatic configuration algorithm. Finally, we carry out corresponding performance verification.

关键词： artificial intelligence illegal and harmful messages real-time detection text detection audio detection image detection video detection

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international conference on Innovative Technologies in Intelligent systems and Industrial Applications, CITISIA 2022

International Conference on Innovative Technologies in Intel...

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international conference on Innovative Technologies in Intelligent systems and Industrial Applications, CITISIA 2022

ISBN: (纸本)9783031290770

The proceedings contain 86 papers. The special focus in this conference is on international conference on Innovative Technologies in Intelligent systems and Industrial Applications. The topics include: Analysing the Impact of Support Plans on Telehealth Services Users with Complex Needs;trend and Behaviour Changes in Young People Using the New Zealand Mental Health Services;securing Cloud Storage Data Using Audit-based Blockchain technology—A Review;Data Security in Hybrid Cloud Computing Using AES Encryption for Health Sector Organization;cyber Warfare: Challenges Posed in a Digitally Connected World: A Review;surveilling systems Used to Detect Lingering Threats on Dark Web;early Attack Detection and Resolution in Sensor Nodes to Improve IoT Security;exploring Cyber Security Challenges of Multi-cloud Environments in the Public Sector;Histogram of Oriented Gradients (HOG) and Haar Cascade with Convolutional Neural Network (CNN) Performance Comparison in the Application of Edge Home Security System;data Security Risk Mitigation in the Cloud Through Virtual Machine Monitoring;analysis of Knocking Potential based on Vibration on a Gasoline Engine for Pertalite and Pertamax Turbo Using signal Processing Methods;AI-based Video Analysis for Driver Fatigue Detection: A Literature Review on Underlying Datasets, Labelling, and Alertness Level Classification;a Study of Information and Communications technology Students e-Platform Choices as Technopreneur;occupational Safety and Health Training in Virtual Reality Considering Human Factors;block Chain technology and internet of Thing Model on Land Transportation to Reduce Traffic Jam in Big Cities;a Marketing Strategy for Architects Using a Virtual Tour Portfolio to Enhance Client Understanding.

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Design of Microhabitat Monitoring System for Spodoptera Frugiperda based on internet of Things Architecture 8

Design of Microhabitat Monitoring System for Spodoptera Frug...

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8th international conference on signal and image Processing, ICSIP 2023

作者： Chang, Jumei Xu, Quanyuan Lu, Ying Zhang, Hanrui Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9798350397932

Temperature, humidity, light, rainfall, wind speed, and wind direction are the main environmental factors influencing the occurrence and spread of the Spodoptera frugiperda. At present, data on the environmental factors of Spodoptera frugiperda life in China are mainly obtained from meteorological data from county-level and higher meteorological stations, and the field microhabitat data collection of Spodoptera frugiperda occurrence is rarely mentioned in the literature. based on the above problems, this design constructs an IoT architecture and designs a field microhabitat environmental factor data collection and remote transmission system for Spodoptera frugiperda habitats. This design uses temperature and humidity sensors, light sensors, rain sensors, wind speed and wind direction sensors to collect data and send it to a single-chip microcomputer, display each data on the liquid crystal screen in real-time and transmit the data to the Alibaba Cloud service. The researchers were able to remotely monitor the Spodoptera frugiperda's living environment's temperature and humidity, light intensity, rainfall, and wind speed and direction way in time. This system can be used to monitor various factors of the mirohabitat environment of Spodoptera frugiperda in real-time, which is vital for further elucidating the suitable environment of Spodoptera frugiperda in my country and developing scientific monitoring and early warning plans as well as prevention and control strategies. © 2023 IEEE.

关键词： Data acquisition

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A Transfer Learning based Intrusion Detection System for internet of Vehicles 15

A Transfer Learning Based Intrusion Detection System for Int...

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15th international conference on Developments in esystems Engineering, DeSE 2023

作者： Haddaji, Achref Ayed, Samiha Fourati, Lamia Chaari LIST3N-ERA University of Technology of Troyes France National School of Electronics and Telecommunications of Sfax Sfax Tunisia Tunisia

ISBN: (纸本)9798350335149

With the fast expansion of the internet of vehicles (IoV) and the emergence of new types of threats, the traditional machine learning-based intrusion detection systems must be updated to meet the security requirements of the current environment. Recently, deep learning has shown exceptional performance in IoV intrusion detection. However, deep learning-based intrusion detection system (DL-IDS) models are more fixated and dependent on the training dataset. In addition, the behavior changes with the occurrence of attacks. They pose a real problem for the DL-IDS and make their detection more complicate. In this paper, we present a deep transfer learning based intrusion detection in-vehicle (TRLID) model for IoV using the CAN bus protocol. In our proposed model, a data preparation approach is proposed to clean up bus data and convert it to an image for usage as input to the deep learning model. Indeed, we used transfer learning characteristics because they enable us to transfer the source task's knowledge to the target task. Therefore, we trained our model using different dataset including different attacks. The experimental results show that our proposed TRLID achieved good results where the intelligence integration of transfer learning was efficient for attacks detection. © 2023 IEEE.

关键词： Intrusion detection

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international conference on Advanced Engineering, technology and Applications, ICAETA 2024

International Conference on Advanced Engineering, Technology...

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international conference on Advanced Engineering, technology and Applications, ICAETA 2024

ISBN: (纸本)9783031709234

The proceedings contain 68 papers. The special focus in this conference is on Advanced Engineering, technology and Applications. The topics include: Breaking New Ground in HAR with Enhanced Weighted k-NN Algorithm;unleashing Student Triumph: Unraveling the Dynamic Link Between State Funding, Academic Excellence, and Financial Prosperity in the Digital Era;a Multifaceted Approach for Identifying Propaganda on Social Networks;utilizing Transfer Learning for Enhanced Classification of Skin Lesions Through Deep Learning Approaches;Design and Analysis of Artificial IVD by 3D Printing technology Using Functionally Graded Materials;microservice Architecture DevOps Integration Challenges: A Qualitative Study;The Effect of Chlorophyll on the Mechanical Properties of Natural Rubber (NR) and the Penetration of X-ray;enhancing the Heart Disease Classification Using Multi-level Perceptron and Principal Component Analysis;a Comparative Analysis of Cloud Load Balancing Algorithms Using CloudSim Simulations;transformers Architecture Oriented Intrusion Detection systems: A Systematic Review;Performance Analysis of Existing SDN Load Balancing Controller Using Mininet;a New Mathematical Models or Estimation the Radon Concentrations in Medical Herbs by Using Numerical Methods;strategic Price Optimization: Machine Learning's Role in E-Commerce Dynamics;hybridization of the Analytic Banach’s Contraction Method Using the Particle Swarm Optimization Algorithm for Fractional System Solving Drinfeld-Sokolov-Wilson;A UAV Configuration Capable of Object Detection, Instant Communication and Real-Time Data Transmission;exploring Deep Learning Architectures for Multiple Apple Leaf Disease Classification;Analysis of Real Measurement for EMG signal based on Surface Traditional Sensors;feature Selection based on Binary Tree Growth Algorithm Using Opposition-based Learning;synthesis and Characterization of Silver Nanoparticles-Coated Molybdenum Oxide Thin Films;turkish Document image Classification.

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Gesture Recognition and Control based on Visible Light Communication 43

Gesture Recognition and Control based on Visible Light Commu...

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43rd Chinese Control conference, CCC 2024

作者： Liang, Xiaojun Li, Jiarong Xu, Chihan Xie, Zixuan Zhang, Chaobo Ding, Wenbo Gui, Weihua Peng Cheng Laboratory Department of Strategic and Advanced Interdisciplinary Research Shenzhen518055 China Tsinghua University Shenzhen International Graduate School Shenzhen518055 China RISC-V International Open Source Laboratory Shenzhen518055 China Central South University School of Automation Changsha410083 China

ISBN: (纸本)9789887581581

The evolution of human-computer interaction (HCI), particularly in the era of the internet of Things (IoT), demands innovative solutions that transcend traditional interfaces like keyboards and touchscreens. This study introduces a visible light communication (VLC) based sensing system for non-contact gesture recognition in HCI. The system utilizes a cost-effective VLC setup optimized for indoor environments, ensuring precise signal transmission and reception. The data acquisition and processing board can accurately capture and analyze light-based hand gesture signals. This process is supported by a comprehensive signal processing framework that includes data preprocessing steps and robust machine learning algorithms. These algorithms are designed to detect subtle light pattern variations due to gestures, ensuring an accurate and responsive recognition process. The system's effectiveness is confirmed by its high gesture recognition accuracy of 95.7%. The study's key contributions include the development of a VLC-based system for gesture recognition and control, the integration of diverse signal processing techniques suitable for VLC-based data, and the successful demonstration of the system's practical application through extensive experimental evaluations. These evaluations have validated the system's efficacy in real-world scenarios, marking an advancement in VLC-enabled HCI. The research highlights VLC's potential as a user-friendly and efficient interface in HCI, offering more intuitive and responsive interactions in smart environments. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Gesture recognition

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Optimized Adaptive based Method for MR image Denoising 16

Optimized Adaptive Based Method for MR Image Denoising

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16th IEEE international conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Golilarz, Noorbakhsh Amiri Dehzangi, Iman Rahimi, Keyan Mississippi State University Department of Computer Science and Engineering Mississippi StateMS United States Rutgers University Department of Computer Science CamdenNJ United States Brown University Department of Computer Science ProvidenceRI United States

ISBN: (纸本)9798331505264

In this paper, we aim to utilize the orca optimization algorithm (OOA) as a computational intelligence method combined with a non-linear data-driven function in the optimization process for brain image de-noising. This meta-heuristic population-based optimizer has recently been proposed to solve complex optimization problems and has achieved promising results in various domains. Here, it is used to obtain the optimum threshold value and other parameters for the activation function. We investigate the impact of this novel algorithm compared to other state-of-the-art optimizers and alternative approaches such as Cuckoo Search (CS), Particle Swarm Optimization (PSO), Differential Evolution (DE), Thresholding Neural Network (TNN), Neighboring Wavelet Coefficients (Neishrink), Hard and Soft thresholds. Peak signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are also used to measure the performance analysis of various de-noising approaches. The results show that the proposed method performs better than other alternative optimization techniques. © 2024 IEEE.

关键词： Mean square error

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DSP-based Industrial Defect Detection for Intelligent Manufacturing 26

DSP-Based Industrial Defect Detection for Intelligent Manufa...

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26th international conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yue, Han Wang, Rucen Gao, Yi Xia, Ailing Zhang, Jianhua Tianjin University of Technology School of Computer Science and Engineering Tianjin China Zhejiang University of Technology College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350331684

internet of Things (IoT) based industrial defect detection has attracted more and more attention. As a key component of intelligent manufacturing, defect detection is very important. Although deep learning (DL) can reduce the cost of traditional manual inspection and improve accuracy and efficiency, it requires huge computing resources and cannot be simply deployed on IoT devices. Digital signal processor (DSP) is an important IoT device with the characteristics of small size, strong performance and low energy consumption, and has been widely used in intelligent manufacturing. In order to achieve accurate defect detection on DSP, we proposed a variety of optimization strategies, and then extended the model to run on multi-core using a parallel scheme, and further quantified the implementation of the model. We evaluated it on three datasets, i.e. NEUSDD, MTDD and RSDD. Experimental results show that our method achieves a faster speed than running the same CNN model on a mainstream desktop CPU, with slightly accuracy loss. © 2023 IEEE.

关键词： internet of things

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