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检索条件"任意字段=IEEE International Conference on Distributed Computing Systems"

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A Practical YOLOV5 Face Detector with Decoupled Swin Head

A Practical YOLOV5 Face Detector with Decoupled Swin Head

引用

2023 ieee international conference on systems, Man, and Cybernetics, SMC 2023

作者： Yuan, Shuozhi Guo, Wenming Yang, Feng Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China Beijing University of Posts and Telecommunications Xinjiang Institute of Engineering Beijing China Xinjiang Institute of Engineering Urumqi China

ISBN: (纸本)9798350337020

Face detection is a fundamental and practical problem in computer vision, which aims to indicate the face positions in a wild environment precisely. However, different from the generic object detection tasks, there are a large number of face samples that suffer from unconstrained poses, occlusion, extreame lights, or other harmful conditions. YOLOV5 is an incredible milestone in the object detection area, but still not powerful enough for the challenging samples. To ease these difficulties, in this paper, we customize DSH-YOLOV5, a practical face detector. Specifically, we integrate a decoupled head with swin transformer layers, whose self-attention mechanism has great potential to explore subtle interconnection details. Additionally, we use two context modules (CBAM and SSH) to enhance the performance of features. Furthermore, we design a novel copy-paste data augmentation to fit the above challenging scenes. Extensive experiments demonstrate that we achieve SOTA performance on WIDER FACE, FDDB, and PASCAL FACE with competitive computational costs. Moreover, under COVID-19, we add the breathing mask and gender classification branches based on the ROI Align to produce more practical face information. © 2023 ieee.

关键词： Face Detection Swin-Transformer YOLOV5

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Pragmatic Review on Progressions in Multimodal Disease Prediction with Combination of Machine Learning, Deep Learning and Electronic Health Records 4

Pragmatic Review on Progressions in Multimodal Disease Predi...

引用

4th ieee international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Dimakatso, Thulaganyo Kuthadi, Venumadhav Selvaraj, Rajalakshmi Dinakenyane, Othapile Botswana International University of Science and Technology Department of Computing and Informatics Palapye Botswana

ISBN: (纸本)9798331518981

Machine Learning (ML) and Deep Learning (DL) methods can be integrated with multimodal data, particularly Electronic Health Records (EHR), presents a significant opportunity to enhance disease prediction. This review paper synthesizes key advancements, identifies gaps in multimodal disease prediction systems, and proposes a roadmap for integrating ML and DL models in clinical settings. By offering a critical comparison of current methods, this paper highlights challenges such as data integration, scalability, and real-world clinical application. In combination of data from various sources like Electronic Health Records (EHR), demographic and clinical dataset, many models have been reviewed and demonstrated with significant improvements in prediction performance compared to existing single-modality methods. This paper highlights the potential of multimodal disease prediction systems and discusses the research gaps that need to be addressed for broader clinical adoption and effectiveness. © 2024 ieee.

关键词： Electronic health record

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Fuzzy Logic and Fog computing in Electronic Health Records Data Privacy: A Comprehensive Review 2

Fuzzy Logic and Fog Computing in Electronic Health Records D...

引用

2nd international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Mohan, Devendra Singh Bansal, Varun Kumar, Anil Shobhit University Gangoh Saharanpur India Shobhit University Gangoh Saharanpur India GCET Gautam Buddha Nagar India

ISBN: (纸本)9798331530389

The potential to improve healthcare services has drawn attention to the use of fuzzy logic and fog computing into electronic health records (EHRs). This study offers a thorough analysis of the research on Fog computing (FC) enabled Fuzzy Logic c-based EHR systems. While fog computing allows for real-time data processing and decentralized storage near end users, fuzzy logic provides a reliable method for handling ambiguities in patient data, diagnoses, and decision-making. When combined, these technologies enhance data security, scalability, and system responsiveness. This research paper includes major approaches, architectures and important applications such as in cloud computing fog-based solutions reduces bandwidth and latency related issues with the help of fuzzy logic how clinical decision support system improves. In this study we list the future research possibilities to address these type problems and also explains obstacles such as complexity of computing, interoperability and privacy of data. These type of study assists academics and practitioners to creating effective, intelligent and safe EHRs' system. In this research paper privacy preservation of EHRs addressed by integration of fuzzy logic with fog computing. © 2025 ieee.

关键词： Electronic health record

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Efficient FPGA-Based Transformer Accelerator Using In-Block Balanced Pruning 13

Efficient FPGA-Based Transformer Accelerator Using In-Block ...

引用

13th ieee international conference on Communications, Circuits and systems (ICCCAS)

作者： Wang, Saiqun Zhang, Hao Ocean Univ China Informat Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9798350386288;9798350386271

Recently, transformer models have been widely deployed in natural language processing and image processing. However, its superior performance comes with high amount of parameters and computations which make it difficult to deploy transformer models in resource limited devices. To reduce the computation cost of transformer models, in this paper, an improved network pruning method is proposed. In the proposed pruning method, the parameter matrix is decomposed into blocks of a specific size. Then, pruning is applied to each block so that the number of parameters remaining in each block is the same. To further reduce the memory requirement of the parameters, an efficient memory storage pattern for sparse parameters is also proposed in this paper. Finally, by combining the proposed methods, an energy efficient transformer accelerator architecture is proposed. The proposed accelerator is implemented in FPGA devices and implementation results show that the proposed design can significantly improve the speed performance and energy efficiency when compared with previous designs.

关键词： Transformer Accelerator Network Pruning FPGA Energy-Efficient computing Sparse Storage Pattern

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Scalable Bidirectional Smart charging SGAM architecture: use case analysis for a viable business to integrate Electric Vehicles as distributed energy resources based on IEC standards

Scalable Bidirectional Smart charging SGAM architecture: use...

引用

ieee international conference on Smart Mobility (SM)

作者： Maldonato, Francesco Vereno, Dominik Eschlberger, Simon Neureiter, Christian Goehlich, Dietmar Tech Univ Berlin CARIAD SE Berlin Germany Univ Appl Sci Ctr Dependable Syst Engn Salzburg Austria Tech Univ Berlin Mobility2Grid Res Campus Berlin Germany

ISBN: (纸本)9798350390148;9798350390131

Bidirectional charging for Electric Vehicles (EVs) is presently primarily addressed and incentivized within small Micro-Grids (MGs), in the form of Vehicle-to-Home (V2H) or Vehicle-to-Building (V2B) systems. Despite extensive research that confirms the advantages of integrating EVs into larger urban-scale MGs as distributed Energy Sources (DERs), there is a lack of state-of-the-art Vehicle-to-Grid (V2G) use cases to encourage users to allow their EVs to participate as prosumers in the electric grid. This paper addresses the issues surrounding the business viability of bidirectional energy trading of EVs from public charging stations while proposing a singular solution feasible within the European energy market. By proposing a combination of different use cases, such as EV-battery leasing for Demand Response (DR) flexibility contracts, Frequency Containment Reserve (FCR), and energy arbitrage, it is possible to establish a convenient framework for all the contracted parties in the electricity market. The proposed approach is designed with harmonized roles defined by ENTSO-E and backed by IEC standards, thus making international adoption of such a model feasible. The resulting model is presented as a SGAM (Smart Grid Architecture Model) architecture, a domain-specific language aimed at implementing such a model, serving as the foundation for further exploration to implement such a concept.

关键词： Smart grids Electric Vehicles e-mobility DER SGAM IEC SRD 63200 IEC 62559-2 Smart charging e-MSP

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Simulation of Cloud computing Resource Allocation Optimization Model Based on Graph Neural Network

Simulation of Cloud Computing Resource Allocation Optimizati...

引用

2023 international conference on Internet of Things, Robotics and distributed computing, ICIRDC 2023

作者： Duan, Ruiwei Mu, Xuelian Lin, Shanshan Weifang Engineering Vocational College Shandong Qingzhou262500 China

ISBN: (纸本)9798350375275

Cloud computing technology improves the resource utilization of clusters by integrating and redistributing physical resources. Virtualization technology is the foundation for abstracting, classifying, and integrating physical resources, as well as providing elastic computing services. With the increasing complexity of application scenarios and requirements, the structure of network models is constantly being optimized and updated. In this paper, by offline or online learning of the graph cloud computing resource allocation optimization model, the neural network structure, weights, thresholds, and other parameters are continuously adjusted to achieve the optimal criterion function of a certain error, thereby obtaining the resource allocation optimization model of the modeled nonlinear system. We optimized cloud computing resource allocation based on GNN (Graph Neural Network) and constructed a model. Through simulation experiments, it has been shown that the GNN based cloud computing resource optimization allocation model constructed in this article has more resource allocation than traditional models. The maximum allocation total of the optimization model constructed in this article can be 2800, while the maximum allocation total of the traditional model is 2300. It can be seen that the optimized model in this article has a greater throughput. © 2023 ieee.

关键词： Cloud computing

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Unveiling Uncertainty: Supporting Learners Through NLP-Driven Confusion Identification

Unveiling Uncertainty: Supporting Learners Through NLP-Drive...

引用

2023 ieee international conference on Dependable, Autonomic and Secure computing, 2023 international conference on Pervasive Intelligence and computing, 2023 international conference on Cloud and Big Data computing, 2023 international conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Jhajj, Gaganpreet Dewan, M. Ali Akber Lin, Fuhua School of Computing and Information Systems Athabasca University Edmonton Canada

ISBN: (纸本)9798350304602

Online learning has increased significantly in popularity over the past several years, driven by global events such as the pandemic and the accessibility offered by educational platforms such as Moodle, Brightspace and so on. However, online learning platforms present challenges, including limited access to support and a sense of disconnection among students. This research works to mitigate these challenges by identifying confusion in learners in online learning platforms by analyzing their posts in course discussion forums. We utilized the Stanford MOOCPosts dataset, evaluated the performance of various ma-chine learning (ML) models, and explored the effectiveness of a custom classification embedding model on the Cohere. This Artificial Intelligence (AI) platform provides access to Large Language Models (LLM) and natural language processing (NLP) tools through an application programming interface (API). Our findings highlight the utility of AI platforms and LLMs in identifying and classifying confusion in online learners. With a substantial potential for the classification task to be dealt with by a custom model running on a third-party platform, researchers can focus on developing conversational agents to support learners with their confusion in courses in online learning platforms. © 2023 ieee.

关键词： Curricula

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Enhanced Multilingual Image Captioning: Integrating Text-to-Speech Translation and Sentiment Analysis

Enhanced Multilingual Image Captioning: Integrating Text-to-...

引用

2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Reddy, Tanuja Konda Veeksha, S. Kavitha, C.R. Amrita School of Computing Department of Cse Bengaluru India

ISBN: (纸本)9798331543617

The aim of this project is to create a system that will improve the way we interact with text and images by using the capabilities of computer vision and natural language processing. The main purpose of using this system to generate human-readable image captions is to train a model that can accurately describe an image's contents. The project extends the functionality of the image captioning system by adding language translation inorder to give a multilingual support. The use of a few pre-trained models will enable users with varying language backgrounds use this system to acquire captions in different target languages. This involves integrating language translation techniques into the system and training different models for every target language. Further we extend the project by working in the field of sentiment analysis by identifying the sentiment (positive, negative, or neutral) of captions by examining text and images. For a better understanding of the emotional tone of the information, the system combines CNNs with natural language processing (NLP) algorithms for image analysis and text analysis. © 2024 ieee.

关键词： Natural language processing systems

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Optimal Dispatch of distributed Generators in Multiple Microgrids Using Hybrid PSO-GWO 6

Optimal Dispatch of Distributed Generators in Multiple Micro...

引用

6th international conference on Energy Conservation and Efficiency, ICECE 2023

作者： Hassan, Usman Ahmad, Aftab University of Engineering and Technology Taxila Department of Electrical Engineering Taxila Pakistan

ISBN: (纸本)9798350332193

In modern energy systems, policies are evolving the grid structures speedily to enhance the penetration of more distributed generation (DGs). Among DGs, renewable energy sources (RES) are a better choice to reduce the cost of main grid. This paper proposed a hybrid approach based on particle swarm optimization and grey wolf optimization (PSO-GWO) to solve the optimal dispatch problem of distributed microgrids (MGs). Both dispatchable and non-dispatchable DGs were considered in the case study comprising three MGs. The proposed dispatch strategy to dispatchable DGs like microturbines, fuel cells, and energy storage systems along with non-dispatchable DGs such as solar photovoltaic and wind turbines reduced the energy cost of the MGs. The results of this study make MGs less dependable on the main grid and enhance their reliability. The proposed technique was implemented in MATLAB R2021a and the cost was improved by $217.55 for main grid. © 2023 ieee.

关键词： distributed power generation

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Hybrid Islanding Identification Strategy for PV Fed distributed Generators Using Rate of Frequency Variation and even Harmonic Perturbation

Hybrid Islanding Identification Strategy for PV Fed Distribu...

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2023 ieee World conference on Communication and computing, WCONF 2023

作者： Praveen Raj, R.S. Savier, J.S. Mar Baselios College of Eng. and Tech. EE Dept. Kerala India College of Engineering Trivandrum EE Dept. Kerala India

ISBN: (纸本)9798350311204

The power sector has benefitted from Renewable Energy Source (RES) dependent distributed Generation (DG) to a large extent, as there could be a substantial reduction in the investment in large transmission system, especially in remote areas. The environment owes the renewable energy source dependent DG due to the reduction in the emission. Due to the introduction of RES fed DGs, the dependability of the power supply has been improved. The Synchronization of DGs with the grid is inevitable as the surplus power in one can meet the deficiency in the other. However, Synchronization of DG with the grid poses certain concerns as well. One of the major concerns is the islanding state of the DGs. Islanding is the scenario in which a DG keeps a site powered despite the absence of exterior bulk Electric Power systems (EPS). It is mandatory to identify involuntary islanding as quickly as possible and to isolate the DG, due to serious hazards to equipment and working personnel. In this paper a robust hybrid Islanding Identification Strategy (IIS), with Rate of frequency variation (ROFV) as passive method and Even Harmonic Perturbation (EHP) as active method is presented and their performances are assessed. MATLAB/SIMULINK 2023a software is used to simulate the proposed *** obtained results show that the hybridized methods outperforms well when compared with the conventional methods. © 2023 ieee.

关键词： distributed power generation

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