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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311-320 订阅

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data Sharing Scheme Based on Local Differential Privacy and Attribute Encryption in Cloud Environment 3

Data Sharing Scheme Based on Local Differential Privacy and ...

引用

3rd international conference on Electronic Information Engineering and computer science, EIECS 2023

作者： Li, Qiang Li, Yahong Li, Zhewei School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China

ISBN: (纸本)9798350316674

Aiming at the problem that the cloud environment data sharing scheme relies on trusted third parties and user identity privacy protection, a data sharing scheme based on localized differential privacy and attribute encryption is designed, which combines blockchain technology and cloud storage to realize safe and reliable data sharing. Firstly, in order to protect user identity privacy, localized differential privacy is used to preprocess data. Secondly, it uses attribute encryption and searchable encryption technology, supports multi-keyword search, has fine-grained access control, and supports outsourcing decryption between users and cloud servers to improve decryption efficiency. Finally, through functional analysis and experimental comparison, the scheme has high efficiency, and has practical value in the data sharing scenario. © 2023 ieee.

关键词： attribute-based encryption blockchain cloud storage component privacy protection Searchable encryption

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Improved Models Based on Deep Learning for Person Re-Identification: A Survey 3

Improved Models Based on Deep Learning for Person Re-Identif...

引用

3rd ieee international conference on Social sciences and Intelligence Management, SSIM 2023

作者： Qiu, Ziqiao Yang, Lei Chen, Jing School of Computer Science Guangdong University of Science and Technology Dongguan China School of Computer Science Guangdong University of Science and Technology Dongguan Kunpeng Key Laboratory of Computing Dongguan China

ISBN: (纸本)9798350327786

Person re-identification technology continually tracks particular targets via video image data from cameras at various angles. This technology is used for monitoring in security fields. Incorporating deep learning into person reidentification has increased its importance in computer vision technology. Various common recognition methods in recent years were analyzed in this article focusing on three fundamental deep learning models for person re-identification. Suggestions for further research in person re-identification were made, too. © 2023 ieee.

关键词： Deep learning

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Exploring Deep Learning-Based Visual Localization Techniques

Exploring Deep Learning-Based Visual Localization Techniques

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

作者： Zhuoyi Yu Maynooth University International Engineering College Fuzhou University Fuzhou China

This comprehensive review paper explores the state of the art in visual localization and navigation, drawing on the principles and methodologies of three significant deep learning networks: Visual Localization Network (VLocNet), Deep Fusion Network (DFNet), and Hybrid Frontend Network (HFNet). Each of these networks demonstrates the application of deep learning to spatial awareness and navigation tasks in unique and significant ways. Rather than an exhaustive dissection of these networks, the paper provides an encompassing overview, illuminating their underlying principles, architectural design, and their relative performance within the field. Additionally, the paper delves into the practical implications of these networks, examining their applications in diverse real-world scenarios. It underlines this examination with a comprehensive analysis of existing literature and experimental results, intended to impart a profound understanding of these networks' strengths, limitations, and potential application areas. Ultimately, this review aims to present a valuable compass to researchers navigating the evolving landscape of advancements in visual localization and navigation, thereby fostering enriched understanding and facilitating future exploration and development in this compelling field.

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Price prediction model for different types of sailboats based on XGBoost algorithm

Price prediction model for different types of sailboats base...

引用

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

作者： Zixuan Wei Haodong Ren Honghao Bao Yi Lu Bo’An Qu School of Economics and Management Beijing University of Chemical Technology Beijing China School of International Education Beijing University of Chemical Technology Beijing China School of Information Science and Technology Beijing University of Chemical Technology Beijing China School of Electrical and Mechanical Engineering Beijing University of Chemical Technology Beijing China

Sailboats are a popular type of luxury goods whose values vary depending on a range of factors, such as their age, condition, and market conditions. Further, monohull and catamaran sailboats are two types of sailboats. The characteristics of these two types of sailboats are often different. Due to their more features, the process of predicting their prices is more complicated. For this reason, this paper builds a full-flow model based on XGBoost from data pre-processing to data prediction in order to predict sailboat prices in a comprehensive and integrated way. Experiments show that the model designed in this paper has the best MSE for both single and twin boats in the comparison algorithm. And the accuracy gap is the smallest and the robustness is the best when predicting for single and double boats.

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Design and Analysis of a Pseudo-Random Number Generator (PRNG) Using 2D Chaotic Mapping for Enhanced Randomness 3

Design and Analysis of a Pseudo-Random Number Generator (PRN...

引用

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

作者： Hu, Jiashuo YiLi Normal University Yining China

ISBN: (纸本)9798350372052

This paper introduces a novel two-dimensional chaotic mapping and leverages its properties to design an efficient Pseudo-Random Number Generator (PRNG). Chaotic mappings find widespread applications in cryptography, simulation, and random algorithms, playing a crucial role in enhancing randomness and ensuring data security. The proposed two-dimensional chaotic mapping combines two independent chaotic equations, achieving a more complex and diversified generation of random sequences. The paper provides a detailed description of the mathematical model of this mapping and analyzes its performance and statistical characteristics. Building upon the two-dimensional chaotic mapping, we design and implement a high-quality PRNG. This PRNG demonstrates significant improvements in randomness, uniformity, and periodicity compared to traditional PRNGs. We conduct thorough performance evaluations, including tests for randomness, distribution analysis, and periodicity detection. Experimental results indicate superior performance across various metrics compared to conventional PRNGs. We also discuss potential application areas for the proposed PRNG, such as encrypted communication, simulation experiments, and Monte Carlo simulations. Our research offers new insights into enhancing the quality and security of PRNGs, contributing significantly to the application of chaos theory in information security and computing. © 2023 ieee.

关键词： Security of data

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Design of Mobile Agricultural data Collection Equipment Based on Arduino 3

Design of Mobile Agricultural Data Collection Equipment Base...

引用

3rd ieee international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2023

作者： Hua, Houqiang Institute of Electronic and Electrical Engineering Civil Aviation Flight University of China Guanghan China

ISBN: (纸本)9781665490788

The development from traditional agriculture to intelligent agriculture needs to be driven by data. Acquiring agricultural environmental data through sensors and using advanced means such as wireless data transmission and IoT technology make agricultural data collection more refined and intelligent. In this paper, we design a prototype of Arduino-based mobile agricultural data collection device. The ESP8266-Node MCU development board is connected to a DC stepper motor driver board, a tri-color LED module and a buzzer. Using the driver module to drive two DC motors, the Arduino Nano development board is connected to a temperature and humidity sensor, a photoresistor sensor and a gas sensor. After compiling in the Arduino IDE environment and networking using the WiFi module of the ESP8266-NodeMCU development board, the device is controlled through a software application to receive and view the collected data. The four SG90 servos are controlled by connecting the breadboard power module and the Bluetooth module through the Arduino UNO R3 development board. After the Bluetooth connection, the four servos are controlled by the software application to achieve the robotic arm grasping function. The test experiment results show that the device runs smoothly, the light illumination, alarm and robotic arm grasping functions can be realized, and the movable collection of specific data about the environment where the crops are located, such as temperature, humidity, light level and gas can be smoothly realized, and the collected datas meet the objective regular changes. © 2023 ieee.

关键词： Robotic arms

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IoT & Vision Based Environment Monitoring System 3

IoT & Vision Based Environment Monitoring System

引用

3rd international conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Jonnala, Yoga Bhavagna Talluru, Laasya Sree Kapuganti, Saketh Joshi, Jetendra Niit University Computer Science Department Neemrana Alwar Rajasthan India Niit University India

ISBN: (纸本)9781665453578

Implementing intelligence into machines and devices in the field of science is efficient, and they complete tasks without any involvement of human minds. IoT is a combination of things, intelligence, and networks. It is simply a connected system of material things and humans. In this paper, we propose and demonstrate IoT -based smart environment monitoring systems, which undertake research in the vision-based and IoT fields. This work helps in the monitoring and surveillance of the public in uncomfortable places with gases and warns them about the use of facemasks. The real test case study for this application was taken in a hideous environment created due to presence of harmful gases in an indoor location at NIIT University. © 2023 ieee.

关键词： Air quality

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Research and Design of Cloud-Based Digital Three-Dimensional Scanning System for Denture Digitization

Research and Design of Cloud-Based Digital Three-Dimensional...

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

作者： Bangyuan Wu Wuxi Boston International School Wuxi China

This study delves into the design of a cloud-based digital three-dimensional scanning system for denture digitization. Addressing the requirements of digital denture manufacturing, this article presents a multi-tiered system architecture comprising a user interface, cloud servers, data transmission module, data processing module, and data storage module. Through this system, patients can upload oral model scanning data via the user interface, achieving precise three-dimensional reconstruction and personalized denture design facilitated by cloud server processing and storage. The article also outlines data transmission protocols and security measures, ensuring secure data transmission and privacy protection. Employing a comprehensive array of techniques, including data encryption, end-to-end encryption, data anonymization, and access control, the system safeguards patient privacy and sensitive data. This design furnishes patients with enhanced denture solutions while providing medical professionals with an efficient platform for data sharing and collaboration.

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XAI for Securing Cyber Physical Systems 3

XAI for Securing Cyber Physical Systems

引用

3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Patil, Amar Prakash Devarakonda, Joshitha Singuru, Manognya Tilak, Smriti Jadon, Shruti Pes University Computer Science Department Bangalore India

ISBN: (纸本)9798350300710

Machine Learning (ML) is heavily leveraged in various fields, specially in Cybersecurity to detect attacks and anomalies. Although these models are capable of achieving high prediction accuracies, they are not interpretable. In fields like medical sciences and cybersecurity, it is essential for the model's predictions to be understandable so that informed decisions can be taken accordingly. A Cyber Physical System (CPS) is controlled or monitored by computer-based algorithms. A CPS is divided into three layers, application control layer, data transmission layer and perception execution layer. Attacks can be performed in any of these layers. Network attacks, cryptographic attacks, physical threats and malicious software injection are some of the possible attacks. In the present work, we used a dataset containing Network Attacks and Physical Threats. The aim was to detect these attacks using ML models and interpret the decisions using Explainable Artificial Intelligence (XAI). By extracting explanations from the model, XAI helps build trust on the model outcome. © 2023 ieee.

关键词： Cyber Physical System

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application of Gray Support Vector Machine Combination Model in Energy Prediction

Application of Gray Support Vector Machine Combination Model...

引用

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

作者： Jie Zheng Jinze Chen Di He Faculty of Information Engineering College of Arts and Sciences Kunming Kunming China Faculty of Engineering China University of Geosciences Wuhan China Faculty of Science and Engineering University of Nottingham Ningbo China

Energy is very important for the world, so we use the grey support vector machine combination model to predict energy has great research significance. In order to improve the accuracy and scope of the application of grey prediction, we combine the grey prediction model with the support vector machine, and use the support vector machine to calculate the values of parameters a and b of the grey prediction model, the final result of parameter a and b is 0.43528 and 6.9644, so that the grey prediction model has the intelligent effect, which is suitable for large-scale data and reduces prediction error, the range of our prediction results is between [207.4237, 255.2898], and the range of relative error we predict is just between [0.001715, 0.009801].

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