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检索条件"任意字段=International Conference on Computer Technologies in Physical and Engineering Applications"

共 41538 条记录，以下是241-250 订阅

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Development of Human Neck Muscle physical Model for Rehabilitation Verification

Development of Human Neck Muscle Physical Model for Rehabili...

引用

2024 IET international conference on engineering technologies and applications, ICETA 2024

作者： Lee, Cheng-Yang Lee, Yun-Ju Chang, Jen-Yuan Department of Power Mechanical Engineering National Tsing Hua University Hsinchu Taiwan Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu Taiwan Department of Mechanical and Computer-Aided Engineering National Formosa University Yunlin Taiwan

ISBN: (纸本)9781837242672

In recent years, prevalence of chronic neck muscle diseases has spurred research into neck rehabilitation machines, yet verification faces significant challenges. The neck's critical role in human anatomy increases the risk of human trials. However, complexity and individual variability of neck muscles makes obtaining biological signals difficult. Existing head-neck models, such as those for car crash simulations, differ greatly from rehabilitation needs. This study aims to develop a biomechanical head-neck model for rehabilitation verification. A virtual model for the neck system was established by using OpenSim supported by biomechanical theories. The complex neck muscle system was linearized from the original virtual model for further analyses and error corrections to achieve a model that can effectively substitute for the real human head-neck. © The Institution of engineering & Technology 2024.

关键词： Digital elevation model

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The applications and Research of NLP and Deep Learning technologies in Public Security Data 24

The Applications and Research of NLP and Deep Learning Techn...

引用

8th international conference on Electronic Information Technology and computer engineering, EITCE 2024

作者： Zhu, Yupeng Zhu, Yuhao College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China The Smart City Research Institute of China Electronics Technology Group Corporation Guangdong Shenzhen China

ISBN: (纸本)9798400710094

With the deepening reform and construction of the police system, it is imperative to fully utilize technologies such as artificial intelligence, big data, blockchain, cloud computing, and the Internet of Things to rapidly enhance the intelligence level of the new generation command center. Natural language processing and deep learning technologies can achieve recognition, understanding, and correlation analysis of public security textual information, which are core steps in building intelligence. Due to the diversity of information in the public security field, the accuracy of these technologies still requires further research. In the analysis of public security incidents, the application of NLP technology faces challenges in optimizing technical models and algorithms, which greatly enhances the efficiency and accuracy of public safety management by analyzing, understanding, predicting, and responding to various security challenges in an intelligent manner. With the advent of the big data era, the public security sector has accumulated massive amounts of police incident data, which contain rich information but also bring difficulties in processing and analysis. Traditional NLP models often struggle to cope with the complex and ever-changing police incident texts, leading to compromised accuracy and reliability of the analysis results. This paper will explore the key technological applications of NLP and deep learning in the field of public safety and provide a review of their research progress. © 2024 Copyright held by the owner/author(s).

关键词： Deep learning

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Integrating Predictive Models and Remote Sensing for Sustainable Agriculture, Energy, and Environmental Management 4

Integrating Predictive Models and Remote Sensing for Sustain...

引用

4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Kumar, Raj Ather, Danish Sagar, Pinki Yusupov, Ibrohimbek Duggal, Sonia Jain, Vishal Department of Computer Applications Faridabad India Amity University Department of It and Engineering Tashkent Uzbekistan G.L. Bajaj Institute of Technology and Management Greater Noida India Tashkent University of Information Technologies Named after Muhammad ibn Musa al-Khwarizm Department of Artificial Intelligence Tashkent Uzbekistan Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350387490

This paper focuses on a critical analysis of the application of integrated works of predictive models and remote sensing in agriculture and renewable energy, and environmental conservation. This method involves the use of deep learning approaches, UAV data collection and hyperspectral imagery which gives the researchers an understanding on the real world effectiveness of the systems and technologies analyzed;on predictive models and resource utilization efficiency. © 2024 IEEE.

关键词： Agriculture Deep Learning Energy Management Environmental Man-Agement Predictive Models Remote Sensing UAV

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Plant Leaf Diseases Classification Using SVMs, XGBoost, and CNNs Based Texture Features 6

Plant Leaf Diseases Classification Using SVMs, XGBoost, and ...

引用

6th IEEE international conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Sami, Abdullah Kamal Al-Dulaimi, Khamael Al-Nahrain University Computer Science Dept. Baghdad Iraq School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia

ISBN: (纸本)9798350389692

Plant diseases identification is frequently used by physical examination or laboratory investigation which creates delays that, by the time identification is finished, results in yield loss. Diseases may affect different parts of plant, specifically the leaf. Different Research have made significant contributions to this sector and used computer vision technologies and machine learning effectively. Consequently, having a thorough understanding of the most recent advancements in machine learning technology and its applications for the identification of leaf diseases. In this paper, a technique is proposed by employing texture features with SVM, XGBoost and CNN to identify leaf disease of five types of plants: Alstonia, Gauva, Jamun, Lemon, and Mango. We have used Local Binary Pattern to extract texture features from leaf because the diseases can affect the texture of plant, such as hairs, ridges, and waxy coatings. We have used challenging dataset which taken under different conditions, such as humidity, light, nutrition, water, and temperature which affect the texture, shape, colour and size of leaves. The findings show that the CNN based LBP achieved high accuracy in comparing with others models. © 2024 IEEE.

关键词： Machine learning

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Flower Classification using Pre-trained ResNet Models in computer Vision 15

Flower Classification using Pre-trained ResNet Models in Com...

引用

15th international conference on Advances in Computing, Control, and Telecommunication technologies, ACT 2024

作者： Chandra, Jyotish K. Jishnu Ashirwaad Sarbesh Pavithra, G. Ninawe, Swapnil S. Dayananda Sagar College of Engineering Bangalore India Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (纸本)9798331300579

This report explores a methodology for flower classification using a pre-trained ResNet *** approach involves fine-tuning the ResNet model on a flower dataset to achieve high classification *** processes include data preprocessing, augmentation, model training, and evaluation, resulting in a validation accuracy of 95%.The project "Flower Classification Using Pre-trained ResNet Models in computer Vision" aims to explore the capabilities of advanced deep learning techniques in accurately classifying flower *** pre-trained Residual Networks (ResNet), this study demonstrates the efficacy of transfer learning in the domain of image classification, particularly for flora *** abstract outlines the methodology, key findings, and potential applications of the project, emphasizing the significant improvements in classification accuracy and computational efficiency achieved through the use of pre-trained models. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Enhancing Cyber-physical Systems Security: A Review of Deep Learning and Blockchain Integration 5

Enhancing Cyber-Physical Systems Security: A Review of Deep ...

引用

5th international conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Selvi, K. Dilip, Golda SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Vadapalani Campus Chennai India

ISBN: (纸本)9798350367171

Cyber-physical Systems (CPS) combine physical and computational elements to produce intelligent systems communicating with their surroundings. By integrating digital and physical processes, CPS provides increased functionality in various industries, including industry, transportation, and healthcare. However, there are security, data integrity, and privacy issues brought on by the extensive integration of CPS across several domains. Therefore, addressing these issues is significant for an effective and reliable CPS model. This study presents a comprehensive review exploring the synergistic relationship between two cutting-edge technologies, Deep Learning (DL) and Blockchain, deployed to resolve these issues in CPS. This study explores the efficiency of leveraging DL and blockchain to enhance the reliability and effectiveness of the CPS models. Also, this study examines the challenges and limitations faced by those DL and blockchain approaches during their implementation across different CPS domains. This comprehensive review provides promising directions for future research in this dynamic and interdisciplinary field for mitigating the challenges in CPS models. It also aids researchers, practitioners, and policymakers by providing a deeper understanding of the possible effects, difficulties, and opportunities related to utilizing DL and Blockchain technologies to improve the security and functionality of CPS. © 2024 IEEE.

关键词： Blockchain technologies Cyber-physical Systems Deep Learning Internet of Things

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Interactive Video Virtual Assistant Framework with Retrieval Augmented Generation for E-Learning 3

Interactive Video Virtual Assistant Framework with Retrieval...

引用

3rd international conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Abraham, Shawn Ewards, Vinodh Terence, Sebastian Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

In the era of a new digital world, there has been a decline in social interactions among humans in a physical setting. These concerns have made educational institutions focus on holistic development, which is recognized to be crucial for the overall growth and performance of a student. Nevertheless, concerns like inadequate student teacher and peer interactions, in an online setting can have adverse effects in the long run. This paper presents an analysis and the benefits of utilizing an interactive virtual assistant framework as a means to abridge the growing disconnect of modern technologies from the traditional classroom setting, closing in on the gap brought about by virtual learning medium. This research study examines the online and hybrid learning approach, highlighting its drawbacks in comparison to conventional teaching methodologies while trying to improve the current situation. The virtual assistant framework presented in this study makes use of cutting-edge technologies in the field of NLP dialogue systems and video generation using Wav2Lip, (a lip-sync model) and GFPGAN model for face-specific enhancement. The Virtual assistant is developed and demonstrated as a learning aid in both hybrid and physical classrooms. Further applications and performance improvements of the framework are discussed. © 2024 IEEE.

关键词： E-learning

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A Comprehensive Review of Interaction Methods for Mixed Reality Sandbox applications 4

A Comprehensive Review of Interaction Methods for Mixed Real...

引用

4th international conference on computer Vision and Pattern Analysis, ICCPA 2024

作者： Wang, Guowei Li, Zemin Li, Yifei Liu, Cong Shen, Junjie China Electronics Technology Group Corporation 15th Research Institute Beijing China Science and Technology on Complex System Simulation Laboratory Beijing China

ISBN: (纸本)9781510682528

Mixed reality (MR) technology is a combination of computer technologies that introduce real-world information into virtual environments and enable interaction between the two within a unified visual space. As a typical application form of MR technology, mixed reality sandbox applications significantly enhance the ability for three-dimensional situational awareness. Human-computer interaction (HCI), as an essential component of MR technology, directly affects the user experience of MR technology and has been a hot topic of research both domestically and internationally in recent years. Introducing gesture interaction into mixed reality sandbox systems can well meet users' needs for continuous observation and editing of three-dimensional objects, providing a real-time and natural interaction experience for users. This paper focuses on mixed reality sandbox applications, discusses the design principles and evaluation methods for human-computer interaction oriented towards mixed reality sandboxes, proposes a set of general non-contact gesture interaction rules, investigates the cross-critical technologies relied upon in interaction methods within mixed reality, summarizes the issues that need to be addressed in the next phase of research for mixed reality sandbox applications, and finally provides a conclusion and outlook for the study of interaction methods in mixed reality sandbox applications. © 2024 SPIE.

关键词： Mixed reality

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A Comparative Analysis of Startups Applying AI in Clinical Oncology 5

A Comparative Analysis of Startups Applying AI in Clinical O...

引用

5th international conference on Computing, Networking, Telecommunications and engineering Sciences applications, CoNTESA 2024

作者： Meca, Alba Ali, Maaruf Universita Degli Studi di Padova Department of Information Engineering Padova Italy Universiteti Metropolitan Tirana Department of Computer Science Tirana Albania

ISBN: (纸本)9798331534530

The rise and exploitation of artificial intelligence (AI) in general and in particular the medical field is fuelled by breakthroughs in deep learning (DL), advances in computing hardware devices, as well as the exponential growth of clinical data used for decision-making. Modern oncological research is intensively adopting AI-based technologies, with most of the related elements such as machine learning (ML) and DL models being utilised to improve the accuracy and efficiency of cancer diagnosis, prevention and treatment, with studies showing that AI could have many additional applications in cancer care. In most countries, numerous AI technologies have received governmental approvals for use in oncology, most notably in radiology. In response to the growing interest in the use of AI technologies in clinical oncology, the number of AI start-ups that concentrate their efforts on combating cancer has surged. The present research study adopts a mixed methodological approach to perform a comparative analysis of the top five AI start-ups that are focused on the application of AI technologies in clinical oncology. The five chosen AI start-ups include: CancerIQ (USA);Panakeia (UK);MultiplAI Health (UK);MNM Bioscience (USA) and X-Zell in Singapore. The selection of the AI start-ups was based on the availability of statistical metrics of interest to the study and the impact of their projects and applications on clinical oncology. The study performs a quantitative and qualitative analysis of selected metrics associated with the use of AI in clinical oncology and the related financial performance metrics. © 2024 IEEE.

关键词： Diseases

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Revolutionizing Military Training: A Comprehensive Review of Tactical and Technical Training through Augmented Reality, Virtual Reality, and Haptics 9

Revolutionizing Military Training: A Comprehensive Review of...

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9th IEEE international conference for Convergence in Technology, I2CT 2024

作者： Tanwar, Vishesh Anand, Vatsala Aggarwal, Priyanshi Kumar, Mukesh Kumar, Gotte Ranjith Chitkara University Institute of Engineering & Technology Chitkara University Punjab India Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun248002 India Sr University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350394474

With technologies such as robots, artificial intelligence, the Internet of Things, and quantum computing being developed to attain digital superiority, modern military applications have the potential to fundamentally alter defense operations and tactics. The two technologies that have the potential to transform combat are augmented reality (AR) and virtual reality (VR). Large-scale simulation environments for the military and paramilitary sectors might be produced using AR and VR, changing everything from soldier training to the creation of fresh warfighting tactics. Tactical augmented reality, digital twins, weapons aiming, spatial orientation, situational awareness, and combat simulations are among the applications of AR that work well. According to Goldman Sachs, by 2025 the AR/VR sector will bring in US $1.4 billion in income from the military alone. © 2024 IEEE.

关键词： Augmented reality

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