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检索条件"任意字段=2023 3rd International Conference on Electrical Engineering and Computer Technology, ICEECT 2023"

共 11249 条记录，以下是4791-4800 订阅

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Protecting Online Transactions: A Cybersecurity Solution Model 3

Protecting Online Transactions: A Cybersecurity Solution Mod...

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3rd international conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Surya, S. Jagtap, Suvarna Ranjeet Ramnarayan, Ramnarayan Priyadarshini, Mankali Ibrahim, Read Khalid Alazzam, Malik Bader Porur Chennai600116 India Yashwantrao Mohite College Maharashtra Pune411038 India Uttaranchal University Uttaranchal Institute of Technology Uttarakhand Dehradun24800 India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Al-Farahidi University Medical Technical College Baghdad10021 Iraq Ajloun National University Information Technology College Ajloun Jordan

ISBN: (纸本)9798350399264

The increasing reliance on online transactions and e-commerce platforms has increased cybersecurity threats, putting businesses and individuals at risk of data breaches, theft, and other malicious attacks. This study proposes a cybersecurity solution model to protect online transactions and mitigate the risks of cyberattacks. The model emphasizes the importance of OTP verification and password protection as protective measures to secure transactions. The study highlights the different types of cybersecurity threats faced by e-commerce platforms, including debit or credit card fraud, data misuse, and vulnerabilities in electronic payment systems. Human errors can also increase the risk of cyberattacks, making it crucial for businesses to implement security measures and strategies to prevent and mitigate cyber threats. The proposed cybersecurity solution model emphasizes the need for a multi-layered security approach, incorporating technologies such as firewalls, antivirus software, and encryption. Additionally, the model recommends regular security audits and employee training to raise awareness of cybersecurity risks and promote best practices. The study concludes that businesses and individuals must take proactive steps to protect their online transactions and safeguard against cybersecurity threats. By implementing the proposed cybersecurity solution model, businesses can mitigate the risks of cyberattacks and ensure secure online transactions, promoting trust and confidence in e-commerce platforms. © 2023 IEEE.

关键词： Cybersecurity

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Equipment Life Assessment Based on Fault Tree-Bayesian Thinking Under Small Sample Conditions 3rd

Equipment Life Assessment Based on Fault Tree-Bayesian Think...

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3rd international conference on Information Control, electrical engineering, and Rail Transit, ICEERT 2023

作者： Sun, Zhangzhe Zhang, Peng Yang, Zhiyong Ou, Sichen Yu, Lingtao College of Mechanical and Electrical Engineering Harbin Engineering University Heilongjiang150001 China Beijing Institute of Tracking and Telecommunications Technology Beijing100075 China

ISBN: (纸本)9789819746194

This paper aims at the problems of lack of test data sample size, difficulty in determining parameter estimation methods, and poor accuracy of theoretical parameter value setting when evaluating reliability parameter indicators of equipment whose life span of systems obeys the Weibull distribution type. Take the test craft, for example, a system is established based on the fault tree method. Fault tree determines the main fault types and obtains the reliability of the system. Combined with Bayesian thinking, the Weibull distribution, which is widely used in the life assessment of electromechanical products, is selected as the prior distribution of the test aircraft system, and MATLAB is used to conduct reliability testing to ensure that the distribution describes the life of the system reasonably and accurately. Then the relevant parameters of the Weibull distribution are obtained to determine the life of the system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Weibull distribution

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Collision-Free UAV Flocking System with Leader-Guided Cucker-Smale Reward Based on Reinforcement Learning 1

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3rd international conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Guo, Yunxiao Xu, Dan Wang, Chang Tan, Letian Shi, Shufeng Zhang, Wanchao Sun, Xiaohui Long, Han College of Sciences National University of Defense Technology Changsha410073 China College of System Engineering National University of Defense Technology Changsha410073 China College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China Shanghai Aerospace Control Technology Institute Shanghai201109 China

ISBN: (数字)9789819710874

ISBN: (纸本)9789819710867

Deep reinforcement learning has been applied to the control of flocking tasks for fixed-wing Unmanned Aerial Vehicles (UAVs) with successful results. However, previous research has given less attention to the design of flocking rewards, and the underlying mechanism of these rewards remains unclear. In this paper, we analyze the underlying mechanism of the flocking reward, and propose the leader-guided C-S reward to guide the fixed-wing UAV flock in a leader-follower structure, and prove that it is bounded when time is limited, which can avoid the gradient exploding problem. Additionally, we propose a collision-free fixed-wing UAV flocking system that uses multi-agent deep deterministic policy gradient to alleviate the non-stationary environment. The proposed system is simulate in 3 and 6 follower scenarios, and the results validate that it effectively controls UAV flocking. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Reinforcement learning

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Internet of Things for Sustainability: A Case Study of University Campus 3

Internet of Things for Sustainability: A Case Study of Unive...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Raikar, Meenaxi M Yaligar, Nagaratna K. L. E Technological University School of Computer Science and Engineering Hubballi India

ISBN: (纸本)9798350343632

Sustainability plays a key role in three different dimensions namely environmental, societal and economics. In line with the seventeen goals set by Sustainable Development Goals (SDG), the objective is to foresee the different options in building a sustainable University campus enabled using Internet of Things (IoT) technology. The different building blocks of sustainable campus infrastructure are water resource administration, waste management, electric appliance management, parking space management, disaster management, garden area management, renewable energy, security aspects and electric vehicle management. The remote monitoring and control of the devices in the campus is facilitated for optimal utilization of the resources thereby achieves sustainability. The device connected to the internet fall in three different sectors namely Personal Area Network (PAN), Wireless Local Area Network (WLAN) and Worldwide Interoperability for Microwave Access (WiMAX). The Message Queue Telemetry Transfer (MQTT) protocol is used for data exchange between the devices. The users can control the devices using the mobile application built using the MIT app inventor platform. The HC-05 and NODEMCU - ESP8266 are the components used to provide user with Bluetooth and Wi-Fi connectivity in the prototype unit developed. The standard Key Performance Indicators are used to evaluate the proposed system for assessment. © 2023 IEEE.

关键词： Broker Cisco packet tracer MQTT protocol Publish Subscribe

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Transaction fee forecasting in post EIP-1559 Ethereum using 1-D Convolutional Neural Network 3

Transaction fee forecasting in post EIP-1559 Ethereum using ...

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

作者： Kallurkar, Harshal Shridhar Chandavarkar, B.R. National Institute of Technology Karnataka Department of Computer Science & Engineering Surathkal Mangalore575025 India

ISBN: (纸本)9798350300710

Cryptocurrencies have established their identity as a healthy alternative to the maintenance of digital assets. Their applications include low-cost money transfers and yield farming. Ethereum is a blockchain that provides the functionality of doing more than a transaction regarding cryptocurrency. Ether is the default cryptocurrency of Ethereum, which is issued to the miners after the successful completion of the consensus mechanism to avoid fraudulent miners gaining profits. Transactions in Ether require that the user should include what is called a 'fee' besides the amount that is sent by the user. The EIP-1559 (Ethereum Improvement Proposals) upgrade to the Ethereum protocol has substantially changed how the transaction fee is calculated. Since this transaction data can be considered time-series data, many prior approaches have been proposed to forecast such a transaction fee using suitable methods effectively. One-dimensional Convolutional Neural Networks have recently been successfully applied to time-series forecasting problems, showing promising results. This paper proposes a univariate 1-D CNN for an effective forecast of transaction fees in the new Ethereum protocol. Furthermore, this paper also compares the proposed method with existing standard approaches, and the results show the superior performance of simple 1-dimensional convolutional neural networks over existing hybrid models. © 2023 IEEE.

关键词： Convolution

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An Optimized Approach for Monkey-Pox Prediction with Neural Networks 3

An Optimized Approach for Monkey-Pox Prediction with Neural ...

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3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Kiran, Kalvacherla Gudimilla, Pallavi Ravi, Silumula Mahender, Nagurla Mohmmad, Sallauddin SR University School of Computer Science and Artificaial Intelligence Telangana Warangal India Vaagdevi College of Engineering Telangana Warangal India Kakathiya Institute of Technology &science Telangana Warangal India

ISBN: (纸本)9798350343632

In the quest to combat the rare viral illness of monkeypox, particularly prevalent in Central and West Africa, our research has delved into refining detection methods using Artificial Neural Networks (ANN). Given the severity of the disease and its potential for community spread, early detection is imperative. Previous efforts to develop AI-based detection systems have aimed at increasing Accuracy, yet consistency has yet to be achieved due to the challenge of imbalanced models. In response, our study introduces an optimized ANN model for monkeypox detection. Through meticulous fine-tuning of hyperparameters, we identified optimal settings and evaluated the model's performance using Precision, Recall, F1 score, and Accuracy. Addressing the imbalance issue, we harnessed ensemble techniques, combining the most effective deep learning models through a majority voting approach based on their probabilistic outputs. Conducted on a publicly accessible dataset, our experiments yielded promising results, with average scores of 85.74% for Precision, 85.47% for Recall, 85.40% for F1-score, and 87.13% for Accuracy, showcasing the potential of our proposed ensemble methodology to enhance the detection of the monkeypox virus significantly. © 2023 IEEE.

关键词： Artificial neural network Deep Learning Evaluation Metrics Monkeypox Neural Networks

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Transi-Net: An Explainable Deep Learning Model Ensemble For Prostate's Transition Zone Segmentation 23

Transi-Net: An Explainable Deep Learning Model Ensemble For ...

引用

23rd IEEE international conference on Bioinformatics and Bioengineering, BIBE 2023

作者： Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos S. Kalantzopoulos, Charalampos Marias, Kostas Tsiknakis, Manolis Koutsouris, Dimitrios D. Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Engineering Laboratory School of Electrical & Computer Engineering Biomedical Research Institute National Technical University of Athens FORTH Athens Ioannina Greece Biomedical Research Institute FORTH Ioannina Greece Computational Biomedicine Laboratory FORTH Heraklion Greece Biomedical Engineering Laboratory School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Biomedical Research Institute Unit of Medical Technology and Intelligent Information Systems FORTH University of Ioannina Ioannina Greece

ISBN: (纸本)9798350393118

The identification of the location of prostate cancer is of paramount importance for improved treatment. This process is strictly bonded with the accurate segmentation of the prostate gland and its zones, on MR images. In the present study, an ensemble of 3 deep learning models along with a Meta-learner module able to refine the outcomes of the models, is proposed (Transi-Net) to segment the prostate's transition zone. A method to quantify the model's uncertainty is introduced to measure the confidence of an architecture with respect to its final decision. The backbone of Transi-Net consist the original U-net, Dense2U-net and Bridged U-net models. The proposed model showcased significant improvement in comparison with its base components as well as an independent model, the USE-Net, while it was proven more confident about its decision. The proposed model resulted in an improvement of 5%, 3%, 3% and 4% for Sensitivity, Balanced Accuracy, Dice Score and Rand Error Index respectively, compared to the second best, USE-Net. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Interpretation of Remote Sensing Data of Pipeline Geohazards 3

Interpretation of Remote Sensing Data of Pipeline Geohazards

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

作者： Tang, Yu Sun, Mingnan Du, Defei Liu, Chang Tang, Buyun Southwest Oil&Gasfield Company of Petrochina Safety Environment&Technology Supervision Research Institute Chengdu610041 China College of Mechanical and Transportation Engineering Beijing102249 China

ISBN: (纸本)9781665472784

For natural gas pipelines across the area in southwest China(2470km2), the small baseline subset SAR interferometry (SBAS-InSAR) and the differential InSAR (D-InSAR) are used to acquire the deformation information of the entire area from 2016-07 to 2021-21 based on the image data of Sentinel-1A and ALOS-2 high-resolution synthetic aperture radar (SAR). The results show that InSAR technology, as an emerging technical means of monitoring pipeline geohazards, can be used to monitor wide-area surface deformation, providing significant technical support for pipeline geohazard prevention and control. SBAS-InSAR can be used in the comparison between ALOS-2 data and Sentinel-1 data for pipeline geohazard prevention. The overall quality of ALOS-2 data interferogram is better than that of Sentinel-1 data, especially in areas covered by vegetation, the former can keep a fair coherence and have a higher resolution. For the analysis of pipeline geohazards in a wide area, it is recommended to adopt the block division by the same type according to the terrain trend. In addition to that, key areas can be divided into smaller blocks. In the analysis of InSAR data, the time series for analysis can be appropriately lengthened when the SBAS-InSAR technology is used. For areas with poor coherence and small hazard points, wavelet filtration shall be adopted to prevent effective coherent points from being masked;In areas with higher coherence, filtration shall be properly strengthened to eliminate error noise. A same area shall be divided into small blocks for InSAR identification, by which the results will be more accurate than that by the whole-area identification, and the small blocks shall be preferably between 50-130 square kilometers. © 2023 IEEE.

关键词： Remote sensing

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Data Mining and AI Approaches for Early Detection and Management of Gastrointestinal Disorders 3

Data Mining and AI Approaches for Early Detection and Manage...

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

作者： Natrayan, L. Ali, H. Mohammed Kaliappan, S. Kumar, Bura Vijay Saveetha School of Engineering Simats Dept. of Mechanical Engineering TN Chennai India Srm Institute of Science and Technology- Ramapuram Dept. of Mechanical Engineering TN Chennai India Lovely Professional University Division of Research and Development Punjab Phagwara India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350343274

This study presents an innovative integration of Data Mining and Artificial Intelligence (AI) to advance early detection and management of gastrointestinal disorders. The methodology encompasses data preprocessing, feature selection, model development, and continuous monitoring for timely intervention. Preprocessing involves standardization and normalization to ensure consistent feature scaling and distribution. Addressing missing values and outliers is achieved using suitable methods such as mean imputation and robust statistical techniques. Feature selection methods, including Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA), aid in identifying the most informative features associated with gastrointestinal disorders. Support Vector Machines (SVM) excels in binary classification, distinguishing healthy individuals from those with gastrointestinal disorders. Deep Neural Network (DNN) is employed for non-linear feature representation and classification. In conclusion, the proposed method showcased superior performance in critical parameters when compared to traditional methodologies. Its enhanced accuracy, sensitivity, specificity, and computational efficiency position it as a promising approach in the domain of Data Mining and AI for early detection and management of gastrointestinal disorders. This innovation holds potential for significant advancements, promising improved healthcare outcomes and better lives for individuals suffering from gastrointestinal disorders. © 2023 IEEE.

关键词： Support vector machines

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Object tracking based on foreground adaptive bounding box and motion state redetection 3

Object tracking based on foreground adaptive bounding box an...

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3rd international conference on Artificial Intelligence and computer engineering, ICAICE 2022

作者： Fu, Jingyi Liang, Qifeng Xie, Qingsong An, Zhiyong School of Computer Science and Technology Shandong Technology and Business University Yantai264005 China

ISBN: (纸本)9781510663473

Siamese network is successfully applied in object tracking. Most of the existing Siamese tracking methods extract template features in the first frame, which will cause the tracker to ignore the appearance change of the target in the subsequent video. In this paper, we propose a tracker based on foreground adaptive bounding box and motion state redetection. The tracker infers the reliability of tracking by the motion pattern of the bounding box. When an anomaly is detected, the tracker will redetect using the continuously updated template. Furthermore, our tracker employs an adaptive bounding box to avoid the effects of inaccurate rotation of the bounding box. The results on the VOT2018 dataset show that our tracker achieves stronger robustness and higher accuracy, providing superior performance compared to the current state-of-the-art trackers. © 2023 SPIE.

关键词： Image segmentation

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