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检索条件"任意字段=3rd International Informatics and Software Engineering Conference, IISEC 2022"

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3rd international informatics and software engineering conference, iisec 2022

3rd International Informatics and Software Engineering Confe...

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3rd international informatics and software engineering conference, iisec 2022

ISBN: (纸本)9781665459952

The proceedings contain 80 papers. The topics discussed include: a secure and intelligent smart home controlling system;mobile platform for livestock monitoring and inspection;feature extraction and dimensionality reduction of cancer data using folded LDA;intrusion detection in computer networks using optimized machine learning algorithms;declarations of software engineering project managers managing remotely: provisions for hybrid working;vision transformers-based hand gesture classification;real-time automated license plate recognition and tracking of runaway vehicles;proposing an augmented reality-based system for fashion and beauty makeover;an augmented reality-based system for fashion and beauty makeover;and time series modeling and forecasting of expected monthly rainfall in some regions of northern Nigeria amid security challenges.

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3rd international conference on Internet of Everything, IoECon 2024

3rd International Conference on Internet of Everything, IoEC...

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3rd international conference on Internet of Everything, IoECon 2024

ISBN: (纸本)9783031844256

The proceedings contain 10 papers. The special focus in this conference is on Internet of Everything. The topics include: Parallelism Everywhere: The Internet of Everything and Parallel Computing;ioV Simulation Architecture for software-Defined Vehicular Fog Network Orchestration;toward Modeling of Flooding Attacks Targeting Massive IoT Networks;using the Internet of Everything for Machine-Learning-Based Computer System Design and Optimization;enhancing Urban Freight Delivery: A Machine Learning Approach to Predicting Delivery Speed;soil-Based Thermoelectric Energy Harvesting System for IoT Devices;a Methodology for Measuring Selected Performance Parameters of Smartphone-Based Vehicle-to-Pedestrian and Pedestrian-to-Infrastructure Communication;wordPress Architecture Modernization Projects.

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Empowering Precision Agriculture: A Comprehensive Real-Time Monitoring and Decision Support System for Farmers 3

Empowering Precision Agriculture: A Comprehensive Real-Time ...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Duraimurugan, N. Akalya, G. Aravind, S. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

A smart agricultural informatics platform integrated with Internet of Things (IoT) aims to revolutionize farming practices through a decentralized communication framework, the primary goal is to establish a knowledge-driven agricultural network in which farmers can discuss precision agronomy strategies, percentage know-how, and collaborate in real time to address challenges. The device carries a meteorological analytics module, powered by way of a RESTful Weather API, handing over a one-week climatic outlook to resource in dynamic choice-making. Advanced sensing technologies, including capacitive soil moisture sensors, electrochemical pH sensors, thermistors, and the DHT11 virtual sensor array, allow continuous tracking of soil physicochemical parameters and microclimatic situations for actionable insights. These features are wrapped in a mobility platform cell software program designed using the Flutter SDK, providing farmers with a modular UI/UX architecture that provides access to real-time farm diagnostics of their machines. Visualization of all facts using sensors. and predictive analytics for resources optimization Benefits of distributed computing and analytical aspects. When taken together this innovative platform helps farmers optimize spatial and temporal crop planning. Increase the sustainability of agricultural ecosystems and promote resilient, fact-driven agriculture. © 2025 IEEE.

关键词： Smart agriculture

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Knowledge-based Retrieval Methods for Enhancing Aerospace Model software Documentation 24

Knowledge-based Retrieval Methods for Enhancing Aerospace Mo...

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3rd international conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Duan, Jinjian Wei, Deming Wang, Meiqing Chen, Gaohui Zhang, Yang Gao, Yanhua School of Mechanical Engineering and Automation Beihang University Beijing China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (纸本)9798400707308

Addressing the critical challenges of poor structure, weak relevance, and limited reusability in aerospace model software development documents, we propose a knowledge-based retrieval method. This method constructs a knowledge base by analyzing key document features, including storage methods, file formats, content structure, inter-document associations, and naming conventions. The system implements structured document management and associates documents based on software configuration information. By partitioning the retrieval domain, constructing semantic vectors, and applying multi-dimensional weight configurations, the method enables efficient retrieval in domains characterized by dense information and frequent term repetition. An aerospace model software document retrieval system has been developed and preliminarily implemented in an aerospace enterprise, demonstrating its effectiveness in improving retrieval efficiency and enhancing the reuse of development knowledge. © 2024 Copyright held by the owner/author(s).

关键词： Content based retrieval

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Achieving covert communication in UCRNs via energy harvesting and primary transmitter jamming 24

Achieving covert communication in UCRNs via energy harvestin...

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3rd international conference on Signal Processing, Computer Networks and Communications, SPCNC 2024

作者： Fan, Hongbin Yang, Weiwei Bai, Mengmeng Zhu, Xinwei Feng, Wenqiang College of Communications Engineering PLA Army Engineering University Jiangsu Nanjing China

ISBN: (纸本)9798400710834

This paper focuses on covert communication in underlay cognitive radio networks (UCRNs) via energy harvesting (EH) and primary transmitter jamming, which can be applied to military communication and emergency communication. We derive closed-form expressions for the average detection error probability, transmission outage probability, and the maximum effective covert rate of covert communication in UCRNs system. Numerical results reveal the average detection error probability and the transmission outage probability decrease with the increase of the maximum interference tolerance of primary receiver. The combined effect of energy conversion efficiency and time allocation ratio of EH and the maximum interference power of PT provides the positive effective covert rate in the secondary network. © 2024 Copyright held by the owner/author(s).

关键词： Transmitters

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IoV Simulation Architecture for software-Defined Vehicular Fog Network Orchestration 3rd

IoV Simulation Architecture for Software-Defined Vehicular ...

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3rd international conference on Internet of Everything, IoECon 2024

作者： Alvarenga, Leonel D. C. Sousa, Pedro Costa, António Instituto Federal Goiano GO Rio Verde Brazil Centro Algoritmi LASI Department of Informatics University of Minho Braga Portugal

ISBN: (纸本)9783031844256

Internet of Everything (IoE) extends Internet of Things (IoT) by going beyond device connections and creating a novel interconnected society with innovative business models supported by intelligent applications that integrate people, things and data. As a specific case of IoT, Internet of Vehicle (IoV) paradigm is directly influenced by IoE-based applications, bringing even more intelligence to vehicular networks. In IoV context, many applications require lower latency, making Fog Computing a promising model to orchestrate cloud-based IoE services consumed by vehicle nodes. Recent research suggests that the software-Defined Networking (SDN) paradigm can optimize Fog Computing in resource and service management, leading to the concept of software-Defined Vehicular Fog Computing Networks (SDVFN). However, testing novel research solutions in real SDVFN environments is a challenge due to the cost of physically build these environments or the disruptive model they represent. In addiction, the lack of a comprehensive simulation tool for the SDVFNs scenarios represents a barrier to the advancement of research in this domain. This article introduces a simulation framework designed to facilitate the prototyping of Intelligent Transportation System Applications within a SDVFN. The framework enables the investigation of various fog node selection strategies and the implementation of adaptable datapaths using the SDN paradigm. Furthermore, it contributes to the evaluation and implementation of efficient computing services orchestration in SDVFN environments. Among the key features of the environment are its handling of vehicular mobility through service-oriented packet routing and its capability to anticipate OpenFlow forwarding rules by predicting handovers. © ICST Institute for Computer Sciences, Social informatics and Telecommunications engineering 2025.

关键词： Fog computing

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Research on automatic generation of engineering quantities from BIM models based on the revit platform 3

Research on automatic generation of engineering quantities f...

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3rd international conference on Algorithms, Network, and Communication Technology, ICANCT 2024

作者： Sha, Xuan Yin, Chenyang Yu, Ziyang Xia, Danyi Sun, Miao School of Civil and Transportation Engineering Southeast University ChengXian College Jiangsu Nanjing210088 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

Quantity extraction is a crucial aspect of BIM-based construction project management. However, the current industry practice typically involves separate modeling by design and cost estimation units, leading to imperfect conversions between design models and quantity models, making it difficult to achieve multi-use of a single model. In light of this, this paper explores a method for quantity extraction based on the Revit design software, which holds practical significance for integrating design and calculation, thereby facilitating optimal design. First, this paper compares Revit with the Revit-based Chenxi BIM software and other mainstream domestic BIM 3D quantity extraction software in terms of interaction methods, interaction characteristics, and software advantages and disadvantages. By utilizing the Revit API for secondary development, Visual Studio was chosen as the development environment, and C# as the programming language to read Revit data. A calculation plugin based on the Revit platform was designed, successfully extending and optimizing Revit's quantity extraction functions, enabling the automatic extraction of quantities from design models. The development of the plugin includes various aspects such as plugin framework definition, user interface design, data model processing, view display, tool functions, error handling, and log recording. It supports localized resource management, Revit command processing, and Excel export functionality. By comparing the quantity extraction results of a building model using this plugin with those calculated by the Chenxi BIM software, the error was found to be less than 1.5%, verifying the practicality and accuracy of the plugin. © 2025 SPIE.

关键词： Project management

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Text-dependent English pronunciation learning system based on GMM 3

Text-dependent English pronunciation learning system based o...

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3rd international conference on Electrical, Electronics, and Information engineering, EEIE 2024

作者： Chang, Shuyuan Chen, Chinta Wang, Lihui Chen, Weiyi Foreign Studies Zhaoqing University Guangdong China Electronic and Electrical Engineering Zhaoqing University Guangdong China Education Zhaoqing University Guangdong China

ISBN: (纸本)9781510688179

With the advancement of computer and human speech signal processing technology, the functionality and practicality of English pronunciation learning systems have been further enhanced. Gaussian Mixed Model (GMM) is a model that accurately quantifies things using Gaussian probability density function, decomposes a thing into several models based on Gaussian probability density function, and can directly approximate any probability distribution. GMM also combines the advantages of a single Gaussian density function and vector quantization, which can more effectively describe the distribution of speaker speech features. After extracting speech features using mel scale frequency cepstral coefficients (MFCC), the GMM algorithm is used for text-dependent English pronunciation learning system. The designed English pronunciation learning system is implemented on MATLAB software. This design collected the speech of 50 people with text-dependent for similarity English pronunciation, and after testing, the similarity reached over 93% under normal conditions. © The Authors.

关键词： Vector quantization

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FedVC: Virtual Clients for Federated Learning with Imbalanced Label Distribution 24

FedVC: Virtual Clients for Federated Learning with Imbalance...

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3rd international conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated Learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated Learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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An Ensemble Machine Learning-Based Approach for Detecting Malicious Websites Using URL Features 3rd

An Ensemble Machine Learning-Based Approach for Detecting Ma...

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3rd international conference on Trends in Electronics and Health informatics, TEHI 2023

作者： Uddin, Khandaker Mohammad Mohi Islam, Md. Ashraful Hasan, Md. Nahid Ahmad, Kawsar Haque, Mir Aminul Department of Computer Science andEngineering Southeast University Dhaka1208 Bangladesh Department of Computer Science and Engineering Dhaka International University Dhaka1205 Bangladesh Department of Computer Science and Engineering Jahangirnagar Univerisity Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka1216 Bangladesh

ISBN: (纸本)9789819739363

The Internet has transformed into a hub for a wide array of illegal activities, ranging from annoying spam ads to financial scams, all thanks to advancements in modern technology. With the constant enhancements in network technology, data traffic on the Internet is ballooning in both volume and scope. Consequently, the complexity and prevalence of cyber threats and attacks are on the rise, with websites becoming the prime targets for hackers. These intruders, or hackers, embed harmful content into web pages with the intent of carrying out nefarious activities. They employ various tactics, such as hacking into computers or trying to harvest information through compromised or malicious websites. Some of these tactics involve injecting malicious software into web links. Therefore, it has become more critical than ever to identify and block such websites before the average user can stumble upon them. In this paper, we propose a method that utilizes machine learning techniques to detect fraudulent websites. We evaluate the prediction accuracy of different machine learning classification methods in this study. We trained our model using various algorithms, including Naive Bayes, Support Vector Machines (SVM), K-Nearest Neighbors (K-NN), Decision Trees, Random Forests, and AdaBoost. Ultimately, we employed an ensemble approach and achieved an outstanding accuracy rate of 99.98%, surpassing other methods in effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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