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检索条件"任意字段=3rd International Conference on Distributed Computing and Internet Technology"

共 8195 条记录，以下是181-190 订阅

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Enhancing Path Planning in UAVs: an Improved Goal-Biased RRT Algorithm for Complex 3D Environments 3

Enhancing Path Planning in UAVs: an Improved Goal-Biased RRT...

引用

3rd international conference on Artificial Intelligence, internet of Things and Cloud computing technology, AIoTC 2024

作者： Ming, Rui Chen, Jinrong College of Computer and Big Data Minjiang Universit Fuzhou350108 China

ISBN: (纸本)9798331530280

This paper addresses the issues of high randomness and low search efficiency in the traditional Rapidly-exploring Random Tree (RRT) algorith m when applied to high-dimensional spaces and complex environments. An improved algorithm is proposed, which incorporates a goal-biased strategy to direct sampling points towards the goal, enhancing the efficiency of path generation. Additionally, fixed sample points that point towards the goal are generated within the exploration space, reducing search randomness. The Euclidean heuristic function is used to calculate the cost of sample points within the exploration space, selecting the point with the minimum cost to form the path, thereby optimizing path quality. Experimental results demonstrate that the improved algorithm outperforms the traditional RRT algorithm in terms of path length, generation time, and the number of explored nodes. This study provides an efficient and reliable solution for path planning in complex three-dimensional environments for unmanned aerial vehicles (UAVs), improving the overall efficiency and reliability of UAV mission execution. © 2024 IEEE.

关键词： Random forests

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AI-based Pneumonia Detection using a Deep Context Axial Reverse Capsule Neural Network with Sea-Horse Optimizer 3

AI-based Pneumonia Detection using a Deep Context Axial Reve...

引用

3rd international conference on Intelligent Data Communication Technologies and internet of Things, IDCIoT 2025

作者： Mohideen AbdulKader, M. Tuteja, Ankush Joshi, Darsh Tholath, Joshua Bobby Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu India

ISBN: (纸本)9798331527549

Pneumonia detection with AI uses advanced deep learning algorithms for identifying the patterns in the chest X-ray images which makes diagnosis faster and efficient for physicians. AI-powered radiography improved the diagnostic approaches, even then existing methods face some limitations such as reduced accuracy and high computational costs. The Deep Context Axial Reverse Capsule Neural Network with Sea-Horse Optimizer (ADpCARCNNet+SHO) for pneumonia detection is proposed to overcome these challenges. This framework processes chest X-ray images from the Chest X-ray 14 dataset, beginning with Color Wiener Filtering (CWF) for noise reduction. Key features are then extracted using the Dynamic Range Quantized Discrete Haar Wavelet Transform (DRQDHWT), followed by classification using the ADpCARCNNet, optimized with the Sea-Horse Optimizer (SHO) to ensure high precision. Proposed method achieves a remarkable 99.9% accuracy on the Chest X-ray 14 dataset, outperforming existing techniques. The results demonstrate improved diagnostic accuracy, faster image analysis, enhanced reliability, and reduced computational cost, highlighting the potential of the ADpCARCNNet+SHO framework for efficient and reliable AI-driven pneumonia detection. © 2025 IEEE.

关键词： Diagnostic radiography

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The Model of Distance Learning Resource Library for Literature Teaching Based on Cloud computing 3rd

The Model of Distance Learning Resource Library for Literatu...

引用

3rd EAI international conference on Application of Big Data, Blockchain, and internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Zhang, Wentong Ding, Miao Lijiang Culture and Tourism College Yunnan674199 China Yunnan College of Business Management Yunnan650106 China

ISBN: (纸本)9783031631382

With the continuous development and maturity of internet technology, many traditional industries are gradually being influenced or even subverted by the internet. As a traditional humanities, literature teaching has also begun to try to complete the modernization transformation with the help of cloud computing technology. The remote literature teaching resource library model based on cloud computing was born for this purpose. This model utilizes cloud computing technology to centrally store literature teaching resources distributed in different locations in the cloud, achieving resource sharing and disclosure. At the same time, the advantages of cloud technology also allow students to access these resources anytime and anywhere through the network, without being limited by time and space. Teachers can also upload and manage courseware in the cloud, achieving real-time updates and maintenance of classroom information. The emergence of a cloud computing based remote literature teaching resource library model has provided new ideas and approaches for the modernization transformation of literature education. It fully leverages the advantages of cloud computing technology, freeing students and teachers from traditional time and space constraints, and promoting the improvement of the quality and efficiency of literature teaching. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Cloud computing

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Deep Learning Framework for Sign Language Recognition Using Inception V3 with Transfer Learning 3

Deep Learning Framework for Sign Language Recognition Using ...

引用

3rd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kirubavathi, G. GuruAkshya, C. Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

Sign language recognition poses unique difficulties due to the complexities of hand gestures and movements. Real-world applications often require robust models capable of handling diverse environmental conditions and variations in signing styles. Transfer learning, a technique that utilizes knowledge from pre-trained models, has emerged as a promising method to address these challenges. Although transfer learning has been successful in various visual recognition tasks, its application to American Sign Language (ASL) recognition remains relatively unexplored. This research showcases the effectiveness of combining transfer learning with advanced CNN architectures like Inception V3 for American Sign Language (ASL) recognition. Through fine-tuning Inception V3, adjusting learning rates, applying data augmentation, and rigorous evaluation, the model achieved an impressive 99.12% accuracy on test datasets. These findings represent a significant improvement in ASL recognition accuracy compared to baseline models. The success of this method underscores the importance of fine-tuning and data augmentation in advancing accessible and reliable sign language recognition technology for practical use. © 2024 IEEE.

关键词： Deep learning

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Reconstruction and Update of 3D Model of Mechanical Products Based on 3D Point Cloud Data 3

Reconstruction and Update of 3D Model of Mechanical Products...

引用

3rd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Zhou, Fujing Anhui Vocational And Technical College Anhui Hefei China

ISBN: (纸本)9798350318609

With the help of high-resolution images, 3D model reconstruction can obtain and extract the original feature information of the object, thereby achieving accurate measurement of product geometric dimensions, materials and other parameters. This technology has broad application prospects in the field of mechanical processing. This article first analyzes the current research status of 3D micro-measurement technology and three-dimensional surface reconstruction methods at home and abroad. Subsequently, this article uses three-dimensional point cloud data for modeling, and performs real-time shooting and data processing of parts. Afterwards, this article uses cloud data processing technology to extract the mechanical products in the sample, and obtains the dimensional parameters of the mechanical products through image fusion. Finally, this article conducts a performance test on the reconstructed three-dimensional model of the mechanical product. The test results show that after optimization, the dynamic response time of the model is reduced from 8-12s to 3-5s. This shows that the model can quickly respond to machine data and achieve high-efficiency operation;after optimization, the durability basically reaches more than 79%. The results of this study can show that the reconstruction effect of this model has been significantly improved, but it still needs improvement. © 2024 IEEE.

关键词： Image fusion

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Enhancing Decentralized Federated Learning with User Feedback Loops: A Novel Approach for Personalized and Adaptive Learning in IoT Environments 3

Enhancing Decentralized Federated Learning with User Feedbac...

引用

3rd international conference on computing and Machine Intelligence (ICMI)

作者： Sarker, Arpita Jesser, Alexander Speidel, Markus Heilbronn Univ HHN Inst Intelligent Cyber Phys Syst ICPS Heilbronn Germany Heilbronn Univ HHN Heilbronn Germany

ISBN: (纸本)9798350372977;9798350372984

The burgeoning field of the internet of Things (IoT) demands innovative approaches to data privacy and user-centric services. This study introduces a novel cross-platform application engineered using the Flutter framework, which orchestrates federated learning (FL) and natural language processing (NLP) to facilitate personalized event discovery within IoT environments. The application features an NLP-based chatbot for user interaction and employs Dendrite, a second-generation Matrix homeserver, to manage decentralized communication. Central to the system's design is the stringent upholding of data privacy: user data, including browsing history and application usage patterns, is processed locally on user devices to construct profiles that inform personalized event suggestions. The system's deployment of end-to-end encrypted communication underscores its commitment to user privacy and security. This integration of FL and NLP showcases a significant leap forward in the realm of privacy-preserving, personalized applications, charting a new course for user engagement in IoT.

关键词： federated learning natural language processing internet of things Flutter Dendrite artificial intelligence decentralized communication personalized event discovery end-to-end encryption

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A Routing Algorithm Based on Software-Defined computing First Network 3

A Routing Algorithm Based on Software-Defined Computing Firs...

引用

2023 3rd international conference on Electronic Communication, Computer Science and technology, ECCST 2023

作者： Bai, Chenchen Gong, Xiaoming Ren, Shuangyin Xu, Tianji Wang, Jingchao University of Electronic Science and Technology of China Chengdu China Institute of Systems Engineering AMS PLA Beijing China

With the continuous development of mobile internet technology, new services and applications appear, such as AR/VR, internet of Vehicles, and Ultra HD Video. They not only require higher network bandwidth resources but also need more computing resources to ensure normal operation and user experience. As a new network technology, computing first network promotes the deep integration of distributed computing resources and networks to make efficient use of these resources. This paper proposes a computing power routing algorithm based on software-defined computing first network (SD-CFN) architecture. It comprehensively considers computing resources and available bandwidth resources of network links. Experimental results show that compared with the nearby scheduling strategy, the proposed computing power routing algorithm can better allocate computing resources to services. Moreover, it can improve the utilization rate of computing resources and provide optimal bandwidth resources in the process of service transmission. © Published under licence by IOP Publishing Ltd.

关键词： Routing algorithms

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Integrating Cloud and Fog Technologies with IoT to Create Smart Agriculture 3

Integrating Cloud and Fog Technologies with IoT to Create Sm...

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3rd IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer Engineering, MI-STA 2023

作者： Alaty, Mohamed Mahfoud Younis, Younis A. University of Benghazi Faculty of Information Technology Benghazi Libya

ISBN: (纸本)9798350319897

Not long ago, there was a move from traditional farming to smart farming, which depended on data and communication technologies to boost crop output and quality. One of the main technologies that have contributed to the existence of smart agriculture is the internet of Things (IoT). The internet of Things technology faced many challenges, so cloud computing was proposed as a complement, yet there were some shortcomings. Fog computing technology was introduced to overcome the shortcomings of integrating the internet of Things and cloud technology. This paper defines and studies IoT, cloud and fog technologies. They are utilized to implement a smart agricultural system consisting of three layers interacting with each other to manage, monitor, and control the agricultural environment. The implemented system has been deployed and tested on a small farm, which can be controlled remotely to observe the humidity of the soil and notify the user of any hazard, such as fire. © 2023 IEEE.

关键词： internet of things

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Efficient Classification of Bird Species Using Photographic Images: A MobileViT Based Approach 3

Efficient Classification of Bird Species Using Photographic ...

引用

3rd international conference on Artificial Intelligence For internet of Things, AIIoT 2024

作者： Polisetty, Venkata Ramana Murthy Chokkalingam, Sp Chennai601103 India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai601103 India

ISBN: (纸本)9798350372120

In the quest to enhance biodiversity conservation and support ecological monitoring, the identification of bird species through automatic recognition systems has emerged as a vital tool. This study introduces a novel approach employing the Mobile Vision Transformer model, capitalizing on its efficiency in handling complex visual data across a comprehensive dataset encompassing 525 bird species. MobileViT integrates the advantages of Convolutional Neural Networks (CNNs) with Transformer technology, surpassing typical deep learning models in terms of feature extraction and contextual comprehension abilities. Incorporating advanced data augmentation methods has bolstered the dataset's variability, ensuring the model's preparedness for a wide spectrum of imaging scenarios. With a direct and impressive outcome, the model achieved a 97.5% accuracy rate in species identification. The adoption of MobileViT promises a scalable and potent solution for bird species recognition within ornithological research, marking a forward leap in employing machine learning for environmental conservation. © 2024 IEEE.

关键词： Image classification

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EmoSync: Facial Emotion Detection for Adaptive Music Playback 3

EmoSync: Facial Emotion Detection for Adaptive Music Playbac...

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3rd international conference on Artificial Intelligence For internet of Things, AIIoT 2024

作者： Devarajan, Devikanniga Rajkumar, K. GITAM Deemed to be University Computer Science and Engineering Nagadenahalli Doddabalpur Karnataka Bangalore561203 India SRM Institute of Science and Technology Department of Data Science and Business Systems School of Computing Tamil Nadu Kattankulathur603 203 India

ISBN: (纸本)9798350372120

This work investigates using Convolutional Neural Networks (CNN) and VGGNet architecture, for realtime emotion detection. Raw data from the FER-2013 dataset is transformed from CSV format into organized image directories, segmented by distinct emotions. The resulting model, equipped with convolutional, pooling, and dense layers, effectively classifies emotions from 48x48 grayscale images. In application, the model overlays detected emotions onto a webcam stream. An integrated music player module synchronizes music with the subject's emotions. Overall, this research explores a comprehensive interactive system focused on recognizing and responding to human emotions. Without further training data, our model, to the best of our understanding, achieves modern single-network accuracy of 74.57% on FER2013 © 2024 IEEE.

关键词： Python

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