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Research on Economic data Collection and Analysis Platform Based on Cloud computing and Internet of Things Technology

Research on Economic Data Collection and Analysis Platform B...

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international conference on AI and Metaverse in Supply Chain Management (AIMSCM)

作者： Tang, Zhengyuan Li, Mengjie He, Zhicong Yang, Haoyu Lanzhou Jiaotong Univ Lanzhou Peoples R China Harbin Normal Univ Harbin Peoples R China Taiyuan Univ Sci & Technol Taiyuan Peoples R China Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9798400708251

The rise of the digital economy is shaping the future of business and society. In this digital era, data collection and analysis have become crucial to support decision-making, innovation, and efficiency improvement. Cloud computing and the Internet of Things technology, as the core driving forces of the digital economy, are opening new possibilities and providing strong support for data collection and analysis platforms. To broaden the scope of network big data applications, improve the accuracy of network data storage and management to a greater extent, and reduce the time for network data processing and control, a research method for network big data based on cloud computing and the Internet of Things in the context of the digital economy is proposed. The author first uses hierarchical network coding to transmit network data, based on the transmitted data, the CRC algorithm is used to calculate network data, and then the data is stored in a group storage manner, secondly, the high-precision query of network data is carried out using the hierarchical reverse stacking positioning method, thereby completing the research on network big data. Finally, to demonstrate the overall performance of network big data research methods based on cloud computing and the Internet of Things, a simulation experiment is conducted. The experimental results show that when the data query parameter epsilon is 8, the data query accuracy curve is unstable, and the query accuracy is low. When the data query parameter epsilon is 4-5, the data query accuracy curve is relatively flat, and the query accuracy is high. The method proposed by the author can comprehensively and concretely study network big data, improve data processing accuracy and network data calculation speed, increase network data storage capacity and query efficiency, reduce the loss rate of network data during operation, expand the operating range of network data, and provide a strong basis for subsequent research on network

关键词： Cloud computing Internet of Things technology data acquisition

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SHIELD: Sustainable Hybrid Evolutionary Learning Framework for Carbon, Wastewater, and Energy-Aware data Center Management 14

SHIELD: Sustainable Hybrid Evolutionary Learning Framework f...

引用

14th international Green and Sustainable computing conference (IGSC)

作者： Qi, Sirui Milojicic, Dejan Bash, Cullen Pasricha, Sudeep Colorado State Univ Ft Collins CO 80523 USA Hewlett Packard Labs Palo Alto CA USA

ISBN: (纸本)9798400716690

Today's cloud data centers are often distributed geographically to provide robust data services. But these geo-distributed data centers (GDDCs) have a significant associated environmental impact due to their increasing carbon emissions and water usage, which needs to be curtailed. Moreover, the energy costs of operating these data centers continue to rise. This paper proposes a novel framework to co-optimize carbon emissions, water footprint, and energy costs of GDDCs, using a hybrid workload management framework called SHIELD that integrates machine learning guided local search with a decomposition-based evolutionary algorithm. Our framework considers geographical factors and time-based differences in power generation/use, costs, and environmental impacts to intelligently manage workload distribution across GDDCs and data center operation. Experimental results show that SHIELD can realize 34.4x speedup and 2.1x improvement in Pareto Hypervolume while reducing the carbon footprint by up to 3.7x, water footprint by up to 1.8x , energy costs by up to 1.3x, and a cumulative improvement across all objectives (carbon, water, cost) of up to 4.8x compared to the state-of-the-art.

关键词： Geo-distributed data centers carbon emissions wastewater machine learning evolutionary algorithms

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Improving Vietnamese Question-Answering system with data Augmentation and Optimization

Improving Vietnamese Question-Answering system with Data Aug...

引用

2023 RIVF international conference on computing and Communication Technologies, RIVF 2023

作者： Thanh Bac, Nguyen Ba Van Vinh, Nguyen VNU University of Engineering and Technology Computer Science Department Hanoi Viet Nam

ISBN: (纸本)9798350315844

Extractive question answering (EQA) is one of the most important tasks in natural language processing (NLP) which has both commercial and research value. Recently, methods using neural networks, especially transformer architecture achieved state-of-the-art results in this field. Because of the rise of large language models, datasets need to be more complex to evaluate models strictly. Despite being meticulously fine-tuned on the most cutting-edge and comprehensive datasets available, these models still exhibit a surprising and concerning tendency to struggle with seemingly uncomplicated scenarios. In this paper, we propose a simple and efficient method for Extractive question answering: (1) to augment data to improve question answering task using models and original datasets;(2) we also use deque data structure to enhance post-processing process to guarantee finding the best answer and to decrease complexity to O(n) in there, n is length of context in question answering problem. © 2023 IEEE.

关键词： Large datasets

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data Analytics for Disaster Management Response using Sentiments

Data Analytics for Disaster Management Response using Sentim...

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2023 international conference on Sustainable computing and Smart Systems, ICSCSS 2023

作者： Kamoji, Supriya Jayaprakash, Nair Dhananjay Biju, Chacko Bibin Benoy, George Anna University of Mumbai Fr. Conceicao Rodrigues College of Engineering Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350333602

Social media has a very significant influence on numerous fields in the modern world. These social media platforms generate big data that can be used to analyze an event. The impacts of catastrophic events like natural disasters could be efficiently managed by the analysis of this huge amount of data. Information-sharing platforms like Twitter have proven extremely helpful in gathering information to offer data linked to relief efforts run by NGOs or volunteers. The extraction of tweets from the Twitter platform using the Twitter application programming interface, their classification into 'need of resources,' 'availability of resources,' and irrelevant tweets with the help of deep learning, clustering, and various natural language processing techniques like BERT, distilBERT, RF, LR, NB, and SVM classification models, and the comparison of their results are aimed to be conducted in this research. © 2023 IEEE.

关键词： Logistic regression

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Proceedings - 2023 international conference on Computer Simulation and Modeling, Information Security, CSMIS 2023

Proceedings - 2023 International Conference on Computer Simu...

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2023 international conference on Computer Simulation and Modeling, Information Security, CSMIS 2023

ISBN: (纸本)9798350343724

The proceedings contain 129 papers. The topics discussed include: optimization simulation of information transmission of Internet of Things nodes based on constraint clustering algorithm;assessment of urban drainage capacity and flood risk based on computing simulation;design of network security storage algorithm based on Markov model;analysis and verification of control principle of aspheric numerical control molding machine tool;optimal design of communication digital twin system architecture based on neural network model;construction of hub engineering evaluation system based on intelligent algorithms;security attack detection method combining side channel with fault injection;integration of civil engineering intelligent building data system based on BIM technology;and research on automobile sales forecasting model based on data mining and cloud computing.

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Secure and privacy-preserving federated learning-based resource allocation for next generation networks

Secure and privacy-preserving federated learning-based resou...

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international conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Dandoush, Abdulhalim Gouissem, Ala Ciftler, Bekir Sait Division of Information Technology College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar ESME Sduria/L2TI at Sorbonne University Paris Nord France Division of Data Science and Artificial Intelligence College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar

ISBN: (纸本)9781032803722

This paper surveys recent research on federated learning-based resource allocation for next-generation networks in order to identify research gaps and potential future directions. We start by outlining the main challenges and requirements for secure and privacy-preserving resource allocation in these networks. The existing solutions and techniques proposed in the literature are then reviewed. The strengths and limitations of these solutions are analyzed, and trade-offs in terms of security, privacy, efficiency, and scalability are discussed. The research gaps and promising directions for future research are identified, such as integrating multiple solutions (e.g., game theory or optimization techniques with federated learning) and developing new models and algorithms for AI-based resource allocation that address specific challenges and requirements of next-generation networks. The goal is to inspire further research in this critical and rapidly evolving field. © 2025 The Author(s).

关键词： Federated learning

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Cloud computing Application of Multi-Sensor Earth Observation Images as the Big data: A KOMPSAT Use Case 2

Cloud Computing Application of Multi-Sensor Earth Observatio...

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2nd international conference on Big data and Privacy computing, BDPC 2024

作者： Lee, Kiwon Hansung University Dept. of Applied Convergence Security Seoul Korea Republic of

ISBN: (纸本)9798350307597

In the era of big data, the importance of Earth Observation (EO) satellite imagery is increasing. However, satellite imagery cannot be directly used without pre-processing and calibration. Recently, products called Analysis Ready data (ARD) have been developed that complete these pre-processing tasks and allow users to use satellite information immediately. This study aims to explain the concept and components of cloud computing-based Soft-as-a-Service (SaaS) that provide numerical functions to generate ARD. We also present a case study in which satellite imagery derivative products generated by this SaaS by open source stack are fused with other types of satellite imagery, such as Synthetic Aperture Radar (SAR), to create high-value products such as soil moisture for agriculture applications or environmental studies. In this study, we used Korea Multi-Purpose Satellite (KOMPSAT)-3/3A optical imagery and KOMPSAT-5 SAR satellite imagery. As the demand for satellite imagery increases worldwide, the need for such fusion research is expected to increase as many application areas that process it as big data are generated. © 2024 IEEE.

关键词： Soil moisture

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Exploring Graph Classification Techniques Under Low data Constraints: A Comprehensive Study 14

Exploring Graph Classification Techniques Under Low Data Con...

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14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Kothari, Kush Mehta, Bhavya Nambiar, Reshmika Shrawne, Seema Veermata Jijabai Technology Institute Department of Computer Engineering and Information Technology Mumbai India

ISBN: (纸本)9798350335095

This survey paper presents a brief overview of recent research on graph data augmentation and few-shot learning. It covers various techniques for graph data augmentation, including node and edge perturbation, graph coarsening, and graph generation, as well as the latest developments in few-shot learning, such as meta-learning and model-agnostic meta-learning. The paper explores these areas in depth and delves into further sub classifications. Rule based approaches and learning based approaches are surveyed under graph augmentation techniques. Few-Shot Learning on graphs is also studied in terms of metric-learning techniques and optimization-based techniques. In all, this paper provides an extensive array of techniques that can be employed in solving graph processing problems faced in low-data scenarios. © 2023 IEEE.

关键词： Graph neural networks

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Load Balancing algorithms for Cloud computing Performance: A Review 5th

Load Balancing Algorithms for Cloud Computing Performance: A...

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5th international conference on computing, Communications, and Cyber Security, IC4S 2023

作者： Chauhan, Shubham Soni, Shivangam Kumar, Abhishek Kaur, Simran Sharma, Ruchika Kalsi, Priyanka Chauhan, Riya Birla, Abhishek Yogananda School of AI Computers and Data Science Shoolini University Bajhol Himachal Pradesh Solan173229 India

ISBN: (纸本)9789819725496

This paper delves into the critical role of load balancing in optimizing cloud computing performance, emphasizing the equitable distribution of workloads through virtual machines (VMs). Load balancing as a service (LaaS) is essential for managing data traffic, preventing resource underutilization, or overuse. Static and dynamic load balancing algorithms, exemplified by round robin, Min-Min, Max-Min, central queue, local queue, and least connection, offer varied approaches with trade-offs in adaptability and system overhead. The comparative analysis highlights the importance of selecting algorithms based on specific system requirements, considering parameters such as reliability, fault tolerance, scalability, and overall performance. algorithms like central queue, local queue, and least connection demonstrate high reliability and fault tolerance, but the choice ultimately hinges on the unique needs of the system, necessitating experimentation, or simulation for precise algorithm selection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud computing

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Application of Improved AlexNet for Seismic data Denoising

Application of Improved AlexNet for Seismic Data Denoising

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2023 international conference on algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Ji, Shangran Shan, Wei Zhao, Dan Yue, Yurong Cui, Shaohua School of Physics and Electronic Information Huaibei Normal University Huaibei235000 China

ISBN: (纸本)9781510671881

We propose an improved AlexNet network model, to address the problems of low denoising performance of traditional LeNet-5 neural networks in removing random noise from seismic data. The network retains the original 8-layer calculation depth, and uses ReLU as the Activation function to reduce the convolution core and the number of nodes in the convolution layer, thus obtaining higher noise feature extraction accuracy. The network trains the network with 10000 seismic data, tests the network with 1000 data, and optimizes the network. Experiments were conducted using a wide range of Marousi2 seismic data, and the results showed that the proposed network has good denoising performance. Compared with traditional wavelet algorithms, SVD, and LeNet-5 networks, experimental results show that the proposed network can achieve higher PSNR and SNR values, and has better seismic data denoising performance compared to the above networks. © 2023 SPIE. All rights reserved.

关键词： Convolution

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