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computer Vision-Based Hand Gesture Recognition 4

Computer Vision-Based Hand Gesture Recognition

引用

4th international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Swedheetha, C. Naveen, Palanichamy Akilan, T. Manikandan, P. Pushpavanam, B. Velammal College of Engineering and Technology Department of Cse Madurai India Dr. N.G.P. Institute of Technology Coimbatore India Amity University Department of Iqac Kolkata India Kalasalingam Academy of Researchh and Education Department of Ece Virudhunagar India Psna College of Engineering and Technology Department of Eee Dindigul India

ISBN: (纸本)9798331529635

Hand gesture recognition plays a vital role in bridging communication gaps for individuals with hearing impairments. this abstract explores the application of computer vision and machine learning techniques to give sign language gestures. By analyzing and classifying the intricate movements of hand and finger configurations, these systems facilitate real-time translation of sign language into text or speech. the potential impact on accessibility and inclusivity is substantial, empowering the deaf and hard-ofhearing community to communicate effortlessly with the broader society. this abstract encapsulates the significance of hand gesture recognition in breaking down linguistic barriers and fostering a more inclusive world. © 2024 ieee.

关键词： Real time systems

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the Methodology of Mask Detection Employing the YOLOv7 Framework 4

The Methodology of Mask Detection Employing the YOLOv7 Frame...

引用

4th international conference on Neural Networks, Information and communication Engineering, NNICE 2024

作者： Zhang, Fucheng Tian, Pinfang Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-TimeIndustrial Systems Wuhan China

ISBN: (纸本)9798350394375

In the current field of object detection, the YOLOv7 model demonstrates significant advantages in terms of both detection speed and accuracy. However, the model shows deficiencies in focussing on key information, and its loss function's sensitivity to bounding boxes results in suboptimal performance in densely populated environments and small target detection scenarios. To address these issues, this study proposes a mask target detection method based on YOLOv7. By integrating a channel attention mechanism into the YOLOv7 model, this method enhances the model's focus on important information in images, effectively reducing interference from background factors. Additionally, the method incorporates Normalized Wasserstein Distance (NWD) into the loss function to optimize the model's evaluation of small target detection. Experimental verification shows that the improved model maintains its original detection speed while increasing the overall accuracy to 92.1%, a 2.9% improvement over the original YOLOv7 model. It achieves a 3.3% increase in the mAP.5 metric. this indicates that the optimised YOLOv7 model has superior detection capabilities, allowing it to perform mask-wearing detection tasks more rapidly and accurately in natural environments. © 2024 ieee.

关键词： Object detection

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Human-computer Interaction in Smart Devices: Leveraging Sentiment Analysis and Knowledge Graphs for Personalized User Experiences 4

Human-Computer Interaction in Smart Devices: Leveraging Sent...

引用

4th international conference on Electronic Information Engineering and computer communication, EIECC 2024

作者： Duan, Shiyu Carnegie Mellon University Pittsburgh United States

ISBN: (纸本)9798331534622

this study proposes a user experience analysis method for smart devices based on user behavior pattern analysis and device response optimization, aiming to improve the response accuracy and user satisfaction of smart devices in human-computer interaction. through the collection of interaction data between users and smart devices, the study delves into the application of sentiment analysis and knowledge graphs within smart devices. this research analyzes user behavior patterns, emotional shifts, and the device's response strategies. the experimental results show that by optimizing the device response strategy, not only the response accuracy is improved but also the user satisfaction is significantly improved. In particular, when the device can make personalized feedback according to the user's emotional changes, the interaction effect is significantly improved. In addition, the experiment also verifies the device's responsiveness under different emotional states, demonstrating the optimized device's accurate understanding and intelligent response to user needs. this study provides new ideas for emotional perception, demand prediction, and personalized interaction design of smart devices, and lays a theoretical foundation for future user experience optimization based on artificial intelligence. © 2024 ieee.

关键词： sentiment analysis Smart devices user behavior analysis user experience

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Multi-Source Correlation Data Traffic Prediction Based on TextCNN and BiLSTM Model 4

Multi-Source Correlation Data Traffic Prediction Based on Te...

引用

4th international conference on Electronic Information Engineering and computer communication, EIECC 2024

作者： Zhang, Zhuoren Shen, Hang Li, Bao Pei, Yang Wang, Fu Luo, You Zhejiang Institute of Mechanical & Electrical Engineering CO. LTD. Research Institute of Digital Technology Hangzhou China Zhejiang Institute of Mechanical & Electrical Engineering CO. LTD. Research Institute of Intelligent Transportation Hangzhou China

ISBN: (纸本)9798331534622

Predicting traffic flow at different highway nodes can provide operational staff with basis for control measures. Current model only predicts the traffic flow for a single node and cannot capture the traffic flow information of different related *** tackle this issue, this paper presents a model that combines the TextCNN and BiLSTM architectures while incorporating features derived from multi-source correlation data. through online testing on the Hangzhou-Shaoxing-Taizhou highway (Taizhou Section) in Zhejiang Province,the experimental results demonstrate that this model effectively captures traffic flow characteristics from neighboring nodes. which is better than other models. Specifically, compared to the Historical Average Algorithm, the RMSE result improved by approximately 36.29%, and the MAE result improved by about 31.59%. © 2024 ieee.

关键词： Prediction models

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Gesture Recognition Method Based on computer Vision and Surface Electromyography : Implementing Intention Recognition of the Healthy Side in the Hand Assessment Process 4

Gesture Recognition Method Based on Computer Vision and Surf...

引用

4th international conference on Neural Networks, Information and communication Engineering, NNICE 2024

作者： Yang, Chaojie Xie, Longhan South China University of Technology Shien-Ming Wu School of Intelligent Engineering Guangzhou China

ISBN: (纸本)9798350394375

With the rapid development of sensing technology, a variety of hand rehabilitation assessment systems have emerged as choices for patients engaged in home-based rehabilitation. However, challenges such as fixed assessment sequences and operational constraints persist. To enhance the efficiency of hand rehabilitation assessment systems, employing gesture recognition to detect the healthy side's movement intent for assisting in the evaluation is a viable solution. In recent years, significant improvements have been made in gesture recognition methods, with some based on computer vision(CV) and surface electromyography(sEMG) demonstrating promising results. this paper explores a gesture recognition method based on both CV and sEMG. Initially, image data and surface electromyographic signals are collected, and joint coordinate data is extracted using the Mediapipe API. Subsequently, both sets of data undergo preprocessing and feature extraction, followed by training random forest(RF) and Convolutional Neural Network(CNN) classifiers separately. A weighted decision-making approach is then employed, achieving a commendable performance in gesture recognition, with an accuracy rate of 96.66%. © 2024 ieee.

关键词： Decision making

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Research on Voice communication Enhancement of Network Learning Platform Based on Human-computer intelligent Interaction 6

Research on Voice Communication Enhancement of Network Learn...

引用

6th international conference on communications, Information System and computer Engineering, CISCE 2024

作者： Wang, Lihong Gou, Weijie School of Basic Education Beijing Polytechnic Beijing China School of Automotive Engineering Beijing Polytechnic Beijing China

ISBN: (纸本)9798350353174

In order to improve the resource dispatching performance of network learning platform and enhance the ability of voice communication enhancement of network learning platform, a voice communication enhancement model of network learning platform based on human-computer intelligent interaction is proposed. the resource allocation method of high concurrent access requests is adopted to conduct channel vertical switching processing of voice communication. An optimal control model of voice communication of network learning platform is established with network congestion degree and user data transmission rate as constraint objective parameters. through artificial intelligence interaction and adaptive control, combined with multi-objective optimization switching and analysis, the comprehensive benefit value is obtained to realize voice communication enhancement. Experiments show that the method used for processing voice communication enhancement of network learning platform can improve communication stability, reduce output interference and improve the ability of resource adaptive allocation of English network learning platform. © 2024 ieee.

关键词： Federated learning

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SubCoPLeD: Superpixel Based Color Distribution Driven Plant Leaf Disease Detection 4

SubCoPLeD: Superpixel Based Color Distribution Driven Plant ...

引用

4th ieee international conference on Computing, communication, and intelligent Systems, ICCCIS 2023

作者： Singh, Vivek Kumar Sharda University Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350306118

Plant leaf diseases are haveily threatening the agricultural products. It is responsible for losses in agriculture economic. Observing plant leaf for early detection of diseases is essential to control disease spread and reduce its impact on the agriculture economic. therefore, plant leaf disease automation is required for effectively controlling the plant leaf diseases in agricultural activities. Recent advances in image processing techniques emerged new possibility for plant leaf disease detection in effective manner. this paper present a simple and robust methodology for early and fast plant leaf disease detection using visual of plant leaf. the proposed method utilizes superpixel of the image in several color models jointly with empirically leant thresholds for identification of plant leaf either diseased or non-diseased. this method the experimental results and analysis on publicly available Plant village dataset supports the advantages of the proposed method. © 2023 ieee.

关键词： Image classification

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Human-computer Interaction Data Scheduling Algorithm Based on Artificial Intelligence 4

Human-Computer Interaction Data Scheduling Algorithm Based o...

引用

4th international conference on Neural Networks, Information and communication Engineering, NNICE 2024

作者： An, Ning Ibrahim, Huda Ahmad, Mazida Northern University Iasdo Institute for Advanced and Intelligent Digital Opportunities School of Computer Science Malaysia

ISBN: (纸本)9798350394375

Traditional scheduling algorithms have certain limitations when faced with complex tasks and dynamically changing resource environments, making it difficult to achieve efficient task allocation and resource scheduling. this paper proposes a human-computer interaction data scheduling algorithm based on artificial intelligence, and elaborates its research methods. the algorithm uses machine learning and intelligent optimization technology to learn and optimize resource allocation strategies through the analysis of historical data and real-time data, improving resource utilization and scheduling accuracy. the algorithm also has the characteristics of adaptive adjustment and decision-making optimization, and can dynamically adjust according to task characteristics and resource status to find the optimal scheduling strategy. through the comparison and experimental evaluation of the human-computer interaction data scheduling algorithm based on artificial intelligence and the traditional scheduling algorithm, this paper obtains the research results, and the mean square error of the algorithm in this paper is kept between 0.06-0.08. In terms of resource utilization, the algorithm in this paper shows obvious advantages, which can accurately predict the resource requirements of tasks and avoid resource waste and idleness. In terms of scheduling accuracy, this algorithm can better match tasks and resources and improve task execution efficiency and response time. In terms of mean square error, the algorithm in this paper reduces prediction errors and improves system stability and performance by establishing task requirements and resource allocation models. © 2024 ieee.

关键词： Scheduling algorithms

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PFMRC: A Unified MRC Framework for Chinese Named Entity Recognition via Semantic Feature Fusion 4

PFMRC: A Unified MRC Framework for Chinese Named Entity Reco...

引用

4th international conference on Artificial Intelligence, Robotics, and communication, ICAIRC 2024

作者： Deng, Zhenrong Zhang, Jinglin Xiao, Yannan Wei, Shiwei Guilin University of Electronic Technology Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin China Nanning Research Institute Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Images and Graphics Intelligent Processing Guilin University of Electronic Technology Guilin China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin China

ISBN: (纸本)9798331531225

the landscape of natural language processing (NLP) has been revolutionized by advancements that enable the upstream training of knowledge-rich models for downstream tasks, particularly named entity recognition (NER). Traditional NER approaches, treating it as a sequence labeling problem, often fail to deeply understand the whole text and to capture syntactic nuances of text. To address these issues, we introduce PFMRC, a novel machine reading comprehension (MRC) framework that leverages the strengths of both contextual semantics and syntactic information for enhanced NER performance. PFMRC employs a dual-pronged feature extraction strategy, utilizing RoBERTa and BiGRU to encode text features for contextual semantic information and representing them as node features of the graph structure, and a weighted embedding representation to translate syntactic dependencies into graph-structured edge features. Our model further incorporates Graph Attention Networks (GAT) to harmonize node and edge features, facilitating a nuanced understanding of text. the integration of prior knowledge and the modeling of intricate syntactic relationships through Graph Attention Networks (GAT) not only improves entity recognition accuracy but also enriches the feature representation, leading to more robust NER outcomes. Extensive experiments on three Chinese datasets demonstrate PFMRC's superior performance over state-of-the-art models. © 2024 ieee.

关键词： Semantics

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Comprehensive Architectural Framework for the Engineering and Optimization of Mobile Software Ecosystems 4

Comprehensive Architectural Framework for the Engineering an...

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4th ieee international conference on Applied Electromagnetics, Signal processing, and communication, AESPC 2024

作者： Tomar, Riya Chauhan, Naman Chauhan, Chhavi Tomar, Yash Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India University of Petroleum and Energy Studies Dehradun India CMR University Bengaluru Department of Computer Science and Engineering India

ISBN: (纸本)9798331528171

Mobile application development has become increasingly popular due to the widespread use of mobile devices. Mobile applications cater to various sectors such as business, gaming, social networking, and digital governance. this paper presents a framework to assist in the design and development of mobile applications, with a focus on selecting appropriate components from multiple alternatives. the framework is based on the COTS (Commercial Off-the-Shelf) paradigm and employs a structured first-order predicate calculus to formalize component requirements. It introduces an acceptance indicator equation that evaluates components based on vendor viability, maintainability, and matching features with system specifications. the framework aims to facilitate informed decision-making in selecting the best component for mobile applications. A case study demonstrates the practical application of the framework, which ultimately resulted in the selection of Certicom's Security Builder SSL for a wireless data transmission application. the framework's contribution lies in integrating maintainability into the early stages of the evaluation process, enabling modular specification, and assisting developers in making informed choices. © 2024 ieee.

关键词： Mobile applications

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