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

共 68829 条记录，以下是851-860 订阅

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Research on Fog Computing Task Scheduling Strategy with Deadline Constraints 7

Research on Fog Computing Task Scheduling Strategy with Dead...

引用

7th IEEE Advanced Information technology, Electronic and automation Control conference, IAEAC 2024

作者： Liu, Lindong Wu, Yiling Guangdong University of Education School of Computer Science Guangzhou China

ISBN: (纸本)9798350339161

The task scheduling problem in Fog Computing environments generally studies the scheduling strategy between tasks and computing resources, seeking a solution based on task scheduling execute time and performance optimization. There is little research on task scheduling strategies with deadline constraints. Based on the improved Apriori algorithm and utilizing the obtained task scheduling classification rules, this paper designs and proposes a task scheduling model and DLCM algorithm that meet time constraints in Fog Computing environment. The DLCM algorithm was validated through simulation experiments, and compared to similar algorithms, the algorithm showed significant improvements in task execution time and scheduling costs. © 2024 IEEE.

关键词： Fog computing

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Weather Prediction on Different LSTM Techniques for Time Series Data in Chennai, India

Weather Prediction on Different LSTM Techniques for Time Ser...

引用

2023 international conference on New Frontiers in Communication, automation, Management and Security, ICCAMS 2023

作者： Tk, Harikishan Sasikiran, S. Manoj Kumar, G. Joseph, Ferdin Joe John Sathiyanarayanan, Mithileysh Srm Institute of Science and Technology Student of Computer Science and Engineering Ramapuram Campus Chennai India Thai-Nichi Institute of Technology Thai-Nichi International College Bangkok Thailand Research & Innovation Mit Square London United Kingdom

ISBN: (纸本)9798350317060

The prediction of weather has always been an integral factor in our lives as it determines if the day of activity leads to favorable or unfavorable conditions. The existence of different deep learning models has facilitated several major problems which without such techniques would have been a very time-consuming and exhaustive process. In this following paper, we explore various types of LSTM models and analyze which among them provides the best fit for the Time Series data that is present in the Weather data. Upon gathering the datasets, a strategy called Long Short-Term Memory (LSTM) is carried out. LSTM is a deep learning model which is primarily focused on time-series data. By employing such technical improvisations on the same data with multiple models each with their own unique characteristics, we can ultimately determine the best model which has an overall better accuracy. Based on the metrics produced by employing each technique, it is found that our LSTM model which proposed here has the best performance in terms of predicting the data with an overall low error rate and loss during the training phase. © 2023 IEEE.

关键词： Long short-term memory

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Feature Decoupled of Deep Mutual Information Maximization 2

Feature Decoupled of Deep Mutual Information Maximization

引用

2nd international conference on automation, Robotics and computer Engineering, ICARCE 2023

作者： He, Xing Peng, Changgen Wang, Lin Tan, Weijie Wang, Zifan State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China Guizhou Big Data Academy Guizhou University Guiyang China Guizhou Minzu University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guiyang China Institute of Guizhou Aerospace Measuring and Testing Technology Guiyang China

ISBN: (纸本)9798350308341

In deep learning, supervised learning techniques usually require a large amount of expensive labeled data to train the network, and the feature representations extracted by the model usually mix multiple attributes, resulting in feature representations that are difficult to decouple and are non-interpretable, which restricts the application and development of deep learning techniques, and for this reason, it is particularly important to study decoupled feature representation methods for unsupervised learning. Although the Learning deep representations by mutual information estimation and maximization (DIM) method achieves excellent results in unsupervised learning, the feature representations learned by the DIM method still suffer from the problem of difficult decoupling. decoupling problem. To address this problem, we minimize the mutual information between the intermediate layer feature representations learned by the hidden layer of the encoder during the encoder training process, so that the features learned by each filter are as uncorrelated as possible, thus realizing feature decoupling, and our method is called FP-DIM. Finally, the effectiveness of the FP-DIM method is verified on the CIFAR-10, STL-10, and Fashion-MNIST datasets. The experiments show that our proposed FP-DIM method is more significant for the learned decouplable middle layer feature representation. Finally, we also propose a reflection of future research for the FP-DIM method, aiming to provide a research idea and direction for solving unsupervised interpretable machine learning and to lay a solid theoretical and application foundation for machine learning fields such as image classification and migration learning. © 2023 IEEE.

关键词： Unsupervised learning

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Establishing Vast Network of Medical Institution to Reduce Medical Waste

Establishing Vast Network of Medical Institution to Reduce M...

引用

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

作者： Dixit, Apoorv Tyagi, Anurag Prakash, Ayushi Avasthi, Sandhya Abes Engineering College Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350302820

The issue of medical waste is becoming increasingly important in the healthcare industry, as the amount of waste generated by medical facilities continues to grow. This study aims to explore the potential of establishing a vast network of medical institutions to reduce medical waste. Specifically, the study investigates the feasibility, advantages, and challenges of such an approach. A mixed-methods research design was used, including a survey of medical institutions, interviews with key stakeholders, and a review of relevant literature. The survey was conducted among 150 medical institutions, and the response rate was 70%. The interviews were conducted with 20 key stakeholders, including medical professionals, waste management experts, and policymakers. The findings indicate that establishing a vast network of medical institutions to reduce medical waste is feasible and has several advantages. In conclusion, the study suggests that establishing a vast network of medical institutions to reduce medical waste is a promising approach that can contribute to improving waste management practices, reducing costs, and promoting environmental sustainability. However, it requires a collaborative effort from all stakeholders, including medical professionals, waste management experts, policymakers, and funding agencies, to overcome the challenges and ensure its success. © 2023 IEEE.

关键词： Sustainable development

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Implementing Cloud Computing in Smart Cities 3

Implementing Cloud Computing in Smart Cities

引用

3rd international conference on technology Enhanced Learning in Higher Education, TELE 2023

作者： Kudryavtsev, Artem Zatsepina, Violetta Pavlov, Ivan Lipetsk State Technical University Automation and Computer Science Faculty Lipetsk Russia

ISBN: (纸本)9798350326567

With the development of the Internet of Things, the number of devices connected to the Internet has increased exponentially, leading to a huge increase in the amount of data to be processed in the cloud, with a significant increase in traffic at the expense of service quality. This, along with the low latency and mobility requirements of many web applications, has led to the emergence of computing options that are closer to network devices. Cloud computing has emerged as a new paradigm to address these issues, extending traditional cloud computing architecture to the boundaries of the network. Despite the rapid development of cloud computing, there are challenges associated with its implementation in real-world scenarios such as smart cities, where the heterogeneity and mobility of devices, as well as the required scalability and interoperability need to be considered. Accordingly, this paper presents a procedure to guide the implementation of this technology in smart cities. As a partial test, the proposed procedure is applied to the use case of rubbish collection © 2023 IEEE.

关键词： Cloud computing

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Research and Application of Image Captioning Based on the CNN-LSTM Method

Research and Application of Image Captioning Based on the CN...

引用

2023 IEEE international conference on Electrical, automation and computer Engineering, ICEACE 2023

作者： Liu, Jingwen Gao, Mingxu College of Science China University of Petroleum Qingdao Shandong China SDU-ANU Joint Science College Shandong University Jinan Shandong China

ISBN: (纸本)9798350309614

Image captioning refers to the generation of concise descriptions for input images by computers. Serving as a bridge between computer vision and natural language processing, image captioning finds extensive applications in fields like accessibility assistance, intelligent search, and autonomous driving. In this paper, this research propose a CNN-LSTM encoder-decoder architecture. Building upon the pre-trained ResNet-101, this research modify the network structure to serve as an encoder for image feature extraction. Subsequently, this research introduce an LSTM decoder with a Soft-Attention module to generate image descriptions. Lastly, this research optimize hyperparameters using the Optuna framework. Our approach demonstrates satisfactory results on the Flicker30k dataset, achieving a Bleu-4 score of 0.187, cross-entropy loss of 6.15, and a top-5 accuracy of 72%. The experimental outcomes compellingly establish the effectiveness of this algorithm. © 2023 IEEE.

关键词： CNN-LSTM deep learning hyperparameter optimization image captioning Soft-Attention

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Center of Excellence for Control and automation in the Context of ASEAN Factori 4.0 Project 8

Center of Excellence for Control and Automation in the Conte...

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Joint 8th international conference on Digital Arts, Media and technology and 6th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2023

作者： Thourn, Kosorl Kim, Bunthern Eng, Samphors Vai, Vannak Chou, Koksal Thiriet, Jean-Marc Lubineau, Denis Yahoui, Hamed Institute of Technology of Cambodia Dept. Telecom. and Network Eng. Phnom Penh Cambodia Institute of Technology of Cambodia Dept. Elec. and Energy Eng. Phnom Penh Cambodia Univ. Grenoble Alpes Gipsa-Lab Grenoble France Univ. Claude Bernard Lyon 1 Dépt Mécanique Lyon France

ISBN: (纸本)9798350396546

The innovative learning laboratory based on an industrial Programmable Logic Controller (PLC) is very important allowing students to perform practices under actual industrial conditions. In the ASEAN Factori 4.0 Erasmu+ project, each Asian partner university will have its own center of excellence with the PLC platform for industrial automation applications developed by the EU partner universities. This paper presents the elements of the center of excellence in conjunction with the existing pedagogical platforms in the department of Electrical and Energy Engineering at the Institute of technology of Cambodia. © 2023 IEEE.

关键词： Programmable logic controllers

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Spread Spectrum Steganography in Audio Containers 5

Spread Spectrum Steganography in Audio Containers

引用

5th international conference on Control Systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2023

作者： Zhelezov, Stanimir Kordov, Krasimir Nachev, Atanas Pavlova, Daniela Konstantin Preslavsky University of Shumen Dept. Computer Science and Technologies Shumen Bulgaria Konstantin Preslavsky University of Shumen Dept. Computer Informatics Shumen Bulgaria University of Library Studies and Information Technologies Sofia Bulgaria

ISBN: (纸本)9798350309126

In this paper, attention is paid to audio containers and the possibilities of using them as carriers of steganographic information. One of the most proven methods for increasing the reliability of stegoalgorithms is distributed embedding. An embedding method based on the use of a pseudo-random sequence generator and an algorithm for its implementation are proposed. The proposed method is investigated and the results presented show that it meets standard reliability metrics and is fully suitable for use in real-world settings. © 2023 IEEE.

关键词： Containers

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A Comparative Evaluation of Airplane Boarding Strategies with a Novel Method for Sustainable Air Travel 9

A Comparative Evaluation of Airplane Boarding Strategies wit...

引用

9th international conference on Information technology Trends, ITT 2023

作者： Kobbaey, Thaeer Bilquise, Ghazala Naqi, Ahmad Ali Higher Colleges of Technology Dept. of Computer Information Science Dubai United Arab Emirates

ISBN: (纸本)9798350327502

The airline industry is prioritizing sustainability and actively taking steps to achieve it. A potential solution for improving sustainability at airports and enhancing the passenger experience is to reduce boarding delays. Various strategies have been suggested and studied in the literature, using methods such as field experiments, discrete event simulation, and cellular automation. This study uses autonomous agent-based simulation to assess the effectiveness of commonly researched individual and group boarding methods while taking into account factors such as luggage, walking speed, and non-compliance by passengers. The study also proposes a new strategy for minimizing boarding delays. The results indicate that the proposed strategy performs better than the other methods, particularly when the flight's occupancy rate and luggage rate are high. © 2023 IEEE.

关键词： Sustainable development

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Content Based Deep Learning Image Retrieval: A Survey 9

Content Based Deep Learning Image Retrieval: A Survey

引用

9th international conference on Communication and Information Processing, ICCIP 2023

作者： Zhang, Chi Liu, Jie University of Chinese Academy of Sciences Institute of Automation Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400708909

With the development of digital technology, various fields generate and share a large amount of visual content. Image retrieval is a hot research direction in the field of computer vision. Efficient and accurate retrieval of query content from massive data is the ultimate form pursued by image retrieval technology. In recent years, the rise of deep learning technology has promoted the rapid development of the field of computer vision. Due to the powerful expressive ability of deep features on image content, image retrieval based on deep learning has become the most cutting-edge research direction in CBIR technology. This paper summarizes the relevant research on the classic deep learning image retrieval technology in recent years, first introduces the form of the CBIR problem, and then lists the classic datasets in this field. Afterwards, content-based deep image retrieval methods are reviewed from the perspectives of network models, deep feature extraction, and retrieval types. Finally, summarize the problems to be solved urgently in the current research, and look forward to the future research direction. © 2023 Copyright held by the owner/author(s)

关键词： computer vision

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