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共 13588 条记录，以下是821-830 订阅

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Research on Face recognition based on Grey Wolf algorithm optimization 4

Research on Face recognition based on Grey Wolf algorithm op...

引用

4th international conference on Computer Vision, Image and Deep learning, CVIDL 2023

作者： Chen, Yuanqiong Wang, Pingping Ling, Jing Wu, Zhengpeng Ding, Lei School of Computer Science and Engineering Jishou University Zhangjiajie427000 China School of Computer Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9798350326444

Face recognition technology has made significant progress and has been widely applied in various fields due to its advantages of non-contact, non-intrusive, and concurrency, which ensures real-time performance while improving efficiency and accuracy. However, the classic Convolutional Neural Network (CNN) algorithm model used in face recognition lacks adaptability to facial changes, is difficult to handle interference factors, and has limited generalization ability. To address these issues, we propose a CNN prediction model based on the Grey Wolf Optimizer (GWO) algorithm. Compared with traditional optimization algorithms, GWO has faster convergence speed and better global search capability, which avoids falling into local optima and improves the model's generalization ability. therefore, it can optimize the performance of CNN and improve the accuracy of CNN algorithm models in face recognition tasks. We conducted comparative experiments based on the public dataset CASIA-FaceV5 Dataset. Compared with the classic CNN algorithm, our optimization algorithm in this paper improved the original prediction accuracy from 79.81% to 96%. © 2023 IEEE.

关键词： Face recognition

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the Effect of Loss and optimization Functions on Bitcoin Rate Prediction in LSTM 4

The Effect of Loss and Optimization Functions on Bitcoin Rat...

引用

4th international Congress on Human-Computer Interaction, optimization and Robotic applications, HORA 2022

作者： Kirci, Berke Kaan Baydogmus, Gozde Karatas Istanbul Kultur University Department of Mathematics and Computer Sciences Istanbul Turkey Marmara University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9781665468350

In recent years, Bitcoin cryptocurrency has become a growing trend in the world. For this reason, researchers from many fields are examining various artificial intelligence models to predict Bitcoin rates. In particular, Deep learning algorithms have been shown to outperform traditional models in predicting cryptocurrency rates. However, very few studies have examined the effect of parameters used in deep learning algorithms on the algorithm. optimization and loss functions are very important, which affect the algorithm's ability to make a successful prediction. In this study, Long-Short Term Memory, a deep learning algorithm, is used to predict daily Bitcoin prices and the effect of optimization/loss functions on the accuracy rate is evaluated. Experimental results showed that the Long-Short Term Memory model made the best predictions as a result of working with the Adam optimization function and the Mean Square Error loss function. © 2022 IEEE.

关键词： Bitcoin

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Research on Foreign Language Teaching Effect Prediction Model Based on Deep learning 4

Research on Foreign Language Teaching Effect Prediction Mode...

引用

4th international Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Ren, Aima Xi'an Eurasia University Xi'an China

ISBN: (纸本)9798331528676

this paper proposes a predictive model for foreign language teaching effectiveness based on deep learning. Firstly, the paper introduces the application of label transfer technology in cross-language environment, and realizes the efficient conversion of labels between different languages through deep learning algorithm, which lays a foundation for the subsequent entity extraction and translation work. through the combination of shared presentation layer and task-specific layer, the optimization of multiple tasks such as label migration, cross-language entity extraction and self-learning dictionary translation is realized. We use deep neural networks to automatically identify and extract key entity information from multiple languages. At the same time, the self-learning dictionary translation technology is introduced to improve the accuracy and efficiency of translation through continuous learning and optimization. A prediction model for foreign language teaching effect is constructed and its validity and accuracy are verified by many experimental data. the experimental results show that the model can effectively predict the effect of foreign language teaching and provide an important reference for teachers and students. ©2024 IEEE.

关键词： Prediction models

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Reconfigurable UI /UX Design for Robotic applications based on User Experience 4

Reconfigurable UI /UX Design for Robotic Applications based ...

引用

4th IEEE Asian conference on Innovation in Technology, ASIANCON 2024

作者： Manoharan, Sakthiprasad Kuttankulungara Megalingam, Rajesh Kannan Lazar, Ann Mariya Ajay Labs Department Of Electronics and Communication Amritapuri India

ISBN: (纸本)9798350354218

In this research, we present a novel method aimed at enhancing the user experience (UX) in robotic applications through a customizable UI/UX system that adapts to users' skill levels. the Continuous Testing and optimization method ensures that the system dynamically adjusts its graphical user interface (GUI) in response to users' evolving proficiency levels in C++ programming. the primary objective of the adaptive UI/UX system is to optimize usability and user satisfaction through iterative refinement guided by qualitative feedback analysis, and user performance metrics. this approach enables the system to cater to users with varying levels of expertise, offering personalized learning pathways for novice, intermediate, and advanced users. the seamless integration of the adaptive GUI for an Educational Programming Robot within the system architecture further enhances the individualized learning experience for users. To validate the effectiveness of the system, two studies were conducted. these studies demonstrate the system's ability to accurately measure user expertise and dynamically adjust the GUI based on users' proficiency levels. the results of these studies indicate significant improvements in user experience and learning outcomes. this underscores the potential of reconfigurable UI/UX design to promote personalized skill development within robotic applications. By leveraging continuous testing and optimization, our approach addresses the challenge of adapting interfaces to users' changing needs and proficiency levels, paving the way for more effective human-robot interaction and skill acquisition. © 2024 IEEE.

关键词： C++ (programming language)

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Investigation of Quantum Machine learning for Smart Eco System Focusing on Energy optimization 5th

Investigation of Quantum Machine Learning for Smart Eco Syst...

引用

5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Hussain, S. Mahaboob Malviya, Nishit Pareek, Prakash Vishnu Institute of Technology Andhra Pradesh Bhimavaram India Indian Institute of Information Technology Jharkhand Ranchi India

ISBN: (纸本)9783031770746

this work explores the integration of Quantum Machine learning (QML) with various applications in development of smart eco system, focusing on its potential to optimize various urban systems and address complex challenges. Mainly, we explored energy management and optimization techniques, considering Quantum Variational Algorithm (QVL). With this study we recognize that the integration of quantum machine learning approaches in smart city applications enhance the sustainability goals with comparison to the classical techniques. Further, we also investigated on how QML algorithms can revolutionize in various aspects in easing transportation, digital public services and policies, security enhancements, and urban planning, offering opportunities to enhance efficiency, sustainability, and resilience in urban environments. However, this study presents several challenges, including scalability limitations, data privacy concerns, and security vulnerabilities in smart cities. Application of QML holds immense promise for urban innovation and transformation. Discussed, various future directions for integrating QML in smart cities applications. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Sustainable city

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Optimized Deep learning Models Using Particle Swarm Intelligence for MindMend, Stroke Rehabilitation System 4

Optimized Deep Learning Models Using Particle Swarm Intellig...

引用

4th international Mobile, Intelligent, and Ubiquitous Computing conference

作者： Ayman, Abdelrahman Mohamed, Habiba Antoun, Mina Mohamed, Shehab Eldeen Amr, Habiba Talaat, Youssef Hassan, Walaa Misr Int Univ Fac Comp Sci Cairo Egypt

ISBN: (纸本)9798350367782;9798350367775

Stroke is a leading cause of long-term disability, significantly affecting patients' motor skills and daily activities. Traditional rehabilitation methods, while beneficial, often lack the precision and adaptability required for optimal recovery. this paper explores the integration of deep learning models optimized with Particle Swarm optimization (PSO) to enhance stroke rehabilitation outcomes using brain-computer interface (BCI) technology. We employed a dataset from the BCI Competition IV, which includes EEG data from multiple participants engaged in motor imagery tasks. Various deep learning models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), EEGNet, and Multi-layer Perceptrons (MLP), were optimized using PSO to improve classification accuracy. the results demonstrate that PSO significantly enhances the performance of these models, providing a robust framework for developing advanced rehabilitation systems. this approach not only improves the accuracy of motor imagery classification but also offers a personalized rehabilitation experience for stroke patients.

关键词： Deep learning Particle Swarm optimization (PSO) Brain-Computer Interface (BCI) Stroke Rehabilitation Convolutional Neural Networks (CNN) Recurrent Neural Networks (RNN) EEGNet Multi-layer Perceptrons (MLP)

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Automatic Recognition of English Pronunciation Features Based on Machine learning 4

Automatic Recognition of English Pronunciation Features Base...

引用

4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Gao, Aiwang Chengdu Neusoft University Department of Foreign Languages Sichuan Chengdu China

ISBN: (纸本)9798350359589

Aiming at the problems of insufficient semantic analysis and low accuracy in English translation, this project intends to study a multi-feature fusion algorithm for automatic error recognition of machine English translation. Continuous implicit Markov model as the infrastructure of English language recognition. the voice processing chip embedded in DSP is designed optimally. A multi-feature information fusion algorithm is proposed to realize automatic identification of error information in English translation. the soft and hard maximum function with tan function is used to extract the characteristics of mechanical English translation. Pearson coefficient and the fusion of various features are used to predict English translation. the experiment proves the result is good in practice. this paper presents an automatic recognition system for spoken English based on embedded technology, which can not only improve the recognition rate of English pronunciation, but also produce positive results. the system is worth spreading in practice. © 2024 IEEE.

关键词： English speech recognition feature recognition machine learning multi-feature fusion

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A Comprehensive survey on Emotion Recognition 4

A Comprehensive survey on Emotion Recognition

引用

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

作者： Lakshmi Bai, K. Jhansi Harshita, Anantaneni Gaddam, Tejaswi Mirtipati, Sravanth Peyyala, Deepika Vnr Vjiet Department of Cse Hyderabad India

ISBN: (纸本)9798331518981

Facial emotion recognition is crucial for interaction between human & computer. this paper delves into understanding various methods and techniques used for emotion recognition. Moreover, this study goes beyond emotion recognition to enable real-time depression analysis by correlating detected emotions with potential indicators of mental health. this approach underscores the importance of comprehensive datasets and robust methodologies in advancing facial emotion recognition and its applications in mental health assessment, offering insights for future research in this evolving field. © 2024 IEEE.

关键词： Adversarial machine learning

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Embodied Intelligent Driving: Key Technologies and applications 4

Embodied Intelligent Driving: Key Technologies and Applicati...

引用

4th IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Wang, Yadong Chen, Sihan Li, Zhiwei Shen, Tianyu Wang, Kunfeng Beijing University of Chemical Technology College of Information Science and Technology Beijing China

ISBN: (纸本)9798350349252

Embodied intelligence emphasizes direct interaction between machines and the physical world, enabling intelligent agents to exhibit intelligent behaviors and autonomous evolution through the interplay of the brain, body, and environment. this paper discusses key technologies involved in embodied intelligent driving, based on the core requirements of embodied intelligence and autonomous driving, focusing on two core aspects: embodied perception and execution, and embodied learning and evolution. the development status and existing issues of these technologies are analyzed. Additionally, the paper elaborates on datasets and practical system applications related to embodied intelligent driving. Subsequently, it presents reflections and prospects for this field, considering the significant role and application potential of parallel robot, aiming to promote the application of embodied intelligent driving in highly dynamic real-world scenes. © 2024 IEEE.

关键词： Adversarial machine learning

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PILCO Based Attitude Control for Morphing Aircraft 4

PILCO Based Attitude Control for Morphing Aircraft

引用

4th international conference on Control theory and applications, ICoCTA 2024

作者： Li, Jiarui Liu, Jiarun Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (纸本)9798331506155

Morphing aircraft have garnered significant attention due to their adaptability across a broad spectrum of operational scenarios. these aircraft exhibit superior aerodynamic performance compared to conventional fixedwing designs. However, achieving stable attitude control is challenging owing to the system's inherent complex nonlinear dynamics. We explore the implementation of a sophisticated and efficient reinforcement learning approach for attitude control in morphing aircraft. Initially, the nonlinear three-degree-of-freedom dynamics of the 'XunYing' telescopic wing morphing aircraft are modeled using the Newton-Euler method. Subsequently, an attitude control system for the morphing aircraft is designed, utilizing the Probabilistic Inference for learning Control (PILCO) method. the controller parameters are optimized using a Gaussian process (GP) and the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, accommodating multiple operational points. Finally, real-Time simulations of the morphing aircraft are conducted across various scenarios. the experimental outcomes demonstrate that the proposed control scheme effectively satisfies the attitude control demands of morphing aircraft. © 2024 IEEE.

关键词： Reinforcement learning

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