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检索条件"任意字段=2023 International Conference on Computer Science and Automation Technology, CSAT 2023"

共 68649 条记录，以下是581-590 订阅

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Visual Object Tracking Based on Improved Convolutional Neural Network 4

Visual Object Tracking Based on Improved Convolutional Neura...

引用

4th international conference on computer Vision, Image and Deep Learning, CVIDL 2023

作者： Tang, Siming School of Automation Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9798350326444

Convolutional neural network(CNN) is widely used in the field of image processing due to their powerful feature extraction function. This paper is based on the theoretical foundation of CNN and aims to solve relevant key problems in a targeted manner. Firstly, This paper combines offline tracking of CNN with particle filtering and proposes two real-time tracking methods, namely GOTURN based tracking algorithm and kernel function based tracking algorithm. Secondly, This paper evaluates the GOTURN based tracking algorithm by comparing it with eight known tracking algorithms in the OTB100 dataset. Likewise, this paper also compares the kernel function based tracking algorithm with seven different tracking algorithms and evaluates it in the VA V123 dataset. Finally, The comparative analysis of experimental results on the dataset proves that these two visual object tracking algorithms based on CNN have good performance in tracking accuracy and real-time performance. © 2023 IEEE.

关键词： Convolutional neural networks

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A Post-Quantum Consortium Blockchain Based Secure EHR Framework

A Post-Quantum Consortium Blockchain Based Secure EHR Framew...

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2023 international conference on IoT, Communication and automation technology, ICICAT 2023

作者： Bansal, Abhilasha Arju Esha Mehra, Pawan Singh Delhi Technological University Department of Computer Science & Engineering India

ISBN: (纸本)9798350302820

Electronic health records (EHR) form a digitalized medical history of a person. In the era of Healthcare 4.0, such records can play a crucial role. However, these records require high security and confidentiality, to ensure patient privacy is maintained. A system that provides data security, confidentiality, integrity and access control is needed to maintain such records. The system must also be fast and scalable, as health records are present in enormous numbers. Blockchain based EHR systems have been researched for quite some time. These systems work well, but have some limitations, such as low scalability. Advent of quantum computers threatens their ability to provide security and confidentiality. In this paper, we present a post-quantum, consortium blockchain framework which is used to store and access EHRs. The design uses CRYSTALS Kyber-768 public key cryptosystem to provide security against quantum attacks. The system is designed to give persons absolute authority on their personal health data. © 2023 IEEE.

关键词： Blockchain

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Supervised Deep Learning based Leaf Disease and Pest Detection using Image Processing 7

Supervised Deep Learning based Leaf Disease and Pest Detecti...

引用

7th international conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Isravel, Deva Priya Somasundaram, Kumaresan Jestin Josephraj, M. Christopher Paul, L. Johnson, J.P. Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Sivaganga India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Kumbakonam India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Dharmapuri India

ISBN: (纸本)9798350397253

The key problems that influence plant health and crop yield quality are leaf disease and pests. To improve crop production many advanced technologies are deployed. One of the predominant technologies is the incorporation of Deep Learning (DL) techniques. DL supports numerous training and testing models that are suitable for smart agriculture. Real-time detection of plant diseases and pest detection is made simpler and more efficient by using DL models. This paper provides an extensive survey of the DL techniques associated with agriculture automation and discusses the latest models focusing on leaf disease detection and pest identification. To select the best features and to optimize the accuracy of the results, an optimizer is identified and an enhanced deep learning model is proposed. The VGG16 and YOLOV5s models are deployed with ADAM optimizer. The results illustrate that the proposed optimized approach achieves an accuracy of 98.71% for leaf disease detection and 97.52% for pest detection. © 2023 IEEE.

关键词： Image processing

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Study and Survey on Gesture Recognition Systems 7

Study and Survey on Gesture Recognition Systems

引用

2023 7th international conference On Computing, Communication, Control And automation, ICCUBEA 2023

作者： Deshpande, Kshitij Mashalkar, Varad Mhaisekar, Kaustubh Naikwadi, Amaan Ghotkar, Archana Pune Institute of Computer Technology Information Technology Pune India

ISBN: (纸本)9798350304268

In recent years, there has been a considerable amount of research in the Gesture Recognition domain, mainly owing to the technological advancements in computer Vision. Various new applications have been conceptualised and developed in this field. This paper discusses the implementation of gesture recognition systems in multiple sectors such as gaming, healthcare, home appliances, industrial robots, and virtual reality. Different methodologies for capturing gestures are compared and contrasted throughout this survey. Various data sources and data acquisition techniques have been discussed. The role of gestures in sign language has been studied and existing approaches have been reviewed. Common challenges faced while building gesture recognition systems have also been explored. © 2023 IEEE.

关键词： computer vision gesture recognition gestures sign language

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A Clustering-Based Cooperative Caching and Multi-Link Bandwidth Strategy in Internet-of-Vehicles 7

A Clustering-Based Cooperative Caching and Multi-Link Bandwi...

引用

7th IEEE international conference on Smart Internet of Things, SmartIoT 2023

作者： Wang, Baichang Chen, Xin Jiao, Libo School of Automation Beijing Information Science and Technology University Beijing China School of Computer Science Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798350316575

With the emergence of the 6G network era and the exponential increase in data traffic, edge caching has emerged as a promising solution for reducing content retrieval latency and easing the backhaul burden of base stations in the context of Internet-of-Vehicles (IoV). This paper proposes a joint cooperative caching and bandwidth allocation policy (JCCBA) that aims to minimize user content retrieval latency while considering continuous task generation. To achieve this goal, the Affinity Propagation (AP) clustering algorithm is used to group vehicles, simplifying the vehicle request and content delivery process while reducing signaling overhead. The Deep Determination Policy Gradient (DDPG) algorithm is applied to obtain optimal caching and bandwidth allocation decisions based on the state information of vehicle users and road side units (RSUs). Simulation experiments show that JCCBA can effectively decrease the average acquisition latency of long-Term users in both random and dynamic IoV environments, outperforming the benchmark algorithm. © 2023 IEEE.

关键词： Clustering algorithms

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A Comparative Analysis in Using Deep Learning Models Which Results in Efficient Image Data Augmentation 4

A Comparative Analysis in Using Deep Learning Models Which R...

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4th international conference on Computation, automation and Knowledge Management, ICCAKM 2023

作者： Kumar Nassa, Vinay Kumar Satpathy, Susanta Pathak, Mrunal K. Takale, Dattatray G. Rawat, Swati Rana, Samir Rajarambapu Institute of Technology Rajaramnagar Sakharale Tel Walwa Dist. Maharshtra Sangli415414 India Vignan Foundation of Science Technology and Research Vadlamudi Computer Science and Engineering Andhra Pradesh Guntur India Aissms Institute of Information Technology Savitribai Phule Pune University Department of Information Technology Pune India Vishwakarma Institute of Information Technology Sppu Computer Engineering Pune India Ambala Mullana India Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350393248

'Deep learning' is now the speediest field in the 'Machine Learning (ML)' and 'Deep Neural Networks fields (DNN)'. 'Convolution Neural Networks (CNN)' are probably the most popular used technique for image classification and prediction among many DNN architectures. 'Deep neural networks' as well as their accompanying machine learning, despite their achievements and promises, face a number of serious challenges. CNNs have achieved attempting to cut results in a variety of multiclass classification, and they still face a number of challenges, despite their broad perspectives. They are driven primarily by the vast scale of the models, which can exceed millions of features, as well as a lack of valid training data sets, and they have issues with overfitting and generalization abilities. This research study focuses on the most commonly discussed difficulty in the area of 'machine learning', which is the absence of adequate 'training data' or an 'unequal class imbalance' within databases. The use of such data augmentation is one method of addressing this issue. Moreover, researchers have evaluated and analysed several data augmentation approaches throughout this research. Secondary method is used for this paper to gather in depth information about deep learning methods to get results in Image data augmentation. © 2023 IEEE.

关键词： Deep neural networks

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Sampled-Data Iterative Learning Control for Linear Parabolic Distributed Parameter Systems with Event-Triggered Strategy

Sampled-Data Iterative Learning Control for Linear Parabolic...

引用

2023 international Annual conference on Complex Systems and Intelligent science, CSIS-IAC 2023

作者： He, Jun Dai, Xisheng Zhou, Rusheng School of Automation Guangxi University of Science and Technology Institute of Intelligent Systems and Control Guangxi University of Science and Technology Liuzhou China School of Computer Science and Technology Guangxi University of Science and Technology Liuzhou China

ISBN: (纸本)9798350309003

This paper focuses on the output trajectory tracking problem for linear parabolic distributed parameter systems (DPSs) using sampled-data iterative learning control (ILC) approach. In addition, in order to reduce the number of controller updates, an event-triggered control (ETC) method is introduced, in which the event triggering condition is generated by comparing the output error of the previous iteration batch. The convergence of the output error of the system at the sampling instant is analyzed through a rigorous mathematical proof process, which concludes that the system output error at the sampling instant converges along the direction of the iteration axis. Finally, numerical simulations are given to prove the effectiveness of the proposed method. © 2023 IEEE.

关键词： Iterative methods

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Healthcare Analytics: Conceptualizing a Research Agenda

Healthcare Analytics: Conceptualizing a Research Agenda

引用

2023 international conference on ENTERprise Information Systems, CENTERIS 2023 - international conference on Project MANagement, ProjMAN 2023 - international conference on Health and Social Care Information Systems and Technologies, HCist 2023

作者： Elragal, Rawan Elragal, Ahmed Habibipour, Abdolrasoul Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

This research recognizes the pressing need for innovative research in healthcare, enabling the transition towards analytics, by explaining how previous studies utilized big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to understand the literature, identify research gaps, and posit research questions for researchers, academic institutions, and governmental healthcare organizations. We intend to explain how contemporary analytics have been used to address healthcare concerns as well as to posit several research questions for future studies based on gaps which we have identified. The study has multi-folds contribution areas: first, it provides a state-of-the-art review to healthcare analytics, second, it posits a research agenda to advance the knowledge in this area further. © 2024 The Author(s). Published by Elsevier B.V.

关键词： Adversarial machine learning

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Robotic Tactile Sensing System Based on Electrical Impedance Tomography 8

Robotic Tactile Sensing System Based on Electrical Impedance...

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8th international conference on Intelligent Informatics and Biomedical sciences, ICIIBMS 2023

作者： Liu, Lekang Zhu, Jun Duan, Zhiqiang Xu, Jiaxin Ren, Jie Dong, Yanzhi Yantai University School of Physics and Electronic Information Yantai China Nanjing University of Information Science and Technology School of Automation Nanjing China Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (纸本)9798350369564

With the rapid development of robotics technology, it has become crucial for robots to effectively perceive the sense of touch. This helps intelligent robots better understand their surroundings, improves their perception and interaction capabilities, and is directly related to the robot's motion decision-making and human-robot interaction safety. Electrical impedance tomography is a non-invasive medical imaging technique that can reconstruct the conductivity distribution of a conductive domain using a small amount of boundary voltage data. It demonstrates the unique advantages of large-area tactile sensors for human-computer interaction. In this paper, we present a tactile sensor system based on electrical impedance tomography. A robotic arm sensor with 16 electrodes was designed and prepared using 3D printing technology, a data acquisition hardware system and an upper computer image reconstruction software system were built. The sensor was applied to the robotic arm as an "electronic skin", and several physical interaction experiments were conducted. The experimental results show that the sensor is of great significance in realizing the tactile sensing function of robots and human-computer interaction. © 2023 IEEE.

关键词： Robotic arms

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Cluster Dependency and Synchronization in Multilayer Directed Complex Networks 3

Cluster Dependency and Synchronization in Multilayer Directe...

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3rd international conference on computer science, Electronic Information Engineering and Intelligent Control technology, CEI 2023

作者： Xie, Yongtao Zhai, Shidong Zhao, Penglei Peng, Hao School of Automation Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9798350372052

This paper makes an important contribution to solving the computational complexity problem of cluster dependence research in multilayer directed networks. In previous studies, the relationship between cluster partitioning and cluster dependency detection in multilayer complex networks has been studied, but the process is complicated and the computational complexity is too high. In order to overcome this challenge, the correlation analysis method of network quotient graph is proposed in this paper. By considering the relationships and interactions between clusters at a higher level of abstraction, this analysis allows for the immediate detection of dependencies without the need for extensive computation on the original network. Firstly, we propose a method of cluster partitioning, which is used to classify multilayer complex networks. The corresponding quotient graph is obtained according to the construction method of quotient graph proposed by us. In the dimension of quotient graph, we detect the dependencies between clusters. Finally, we provide a detailed simulation example to validate our findings. © 2023 IEEE.

关键词： Multilayers

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