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Study on Carbon Sink Value of Marine Resources 20

Study on Carbon Sink Value of Marine Resources

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20th international symposium on distributed computing and applications for business engineering and science, DCABES 2021

作者： Zou, Ying Liu, Xinyi Luo, Liying International Business Faculty BNUZ Zhuhai China BNUZ Department of Management Zhuhai China CPU Department of Pharmacy Nanjing China

ISBN: (纸本)9781665428897

Ecosystem service has two characteristics: one is typical public goods, the other is external, so the development and utilization of ecological environment cannot be realized directly through the market. It is necessary to take adequate measures to make up for the shortage of market mechanisms, so the evaluation of ecosystem service value has gradually become the focus of scholars. This paper uses conditions with Guangdong area as an example, evaluating the ocean carbon sink, the economic value of the services, the influencing factors of Guangdong residents' willingness to pay, it is concluded that Guangdong ocean carbon sinks the higher total monetary value of ecosystem services, the respondent's age, level of education, occupation, family income, degree of understanding of the ocean carbon sink, all the five factors have a significant influence on willingness to pay. Gender did not correlate with a willingness to pay. © 2021 IEEE.

关键词： Marine biology

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A hybrid ARIMA-ANN model for Internet Food safety risks 20

A hybrid ARIMA-ANN model for Internet Food safety risks

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20th international symposium on distributed computing and applications for business engineering and science, DCABES 2021

作者： Wang, Wei Sun, Jun School Of Artificial Intelligence and Computer Science Jiangnan University WuXi China

ISBN: (纸本)9781665428897

As Internet technology becomes more and more mature, Internet food sales have become more and more problematic. problems from raw materials to food processing to food distribution are not easily regulated because of online sales. Firstly, crawl the take-out sales data of cities across the country, based on the data the time series prediction model is established to realize the prediction of business and regional risk values, From this obtains the merchant early warning as well as reminds the consumer's function. According to the data analysis, the ARIMA+ANN model is used for short-term prediction. Then the data analysis results of different regions are compared, and the data analysis of both sides of time and space is conducted, The value at risk ranking allows consumers to make a clear comparison of effects, In this way, we can realize the real and effective supervision of store owners from the economic level, realize the real and effective supervision of Internet food sales, and ensure food safety. © 2021 IEEE.

关键词： Data acquisition

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Fault Diagnosis of Power Grid Based on Convolutional Neural Network

Fault Diagnosis of Power Grid Based on Convolutional Neural ...

引用

international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Liping Qu Jie Zhang Tailu Gao School of Electrical Engineering and Information Beihua University Jilin China

ISBN: (纸本)9781665454636

Due to the operation of regional networking, the scale of the power grid is becoming larger and larger, and a fault in the power grid needs to be located in the fault area timely and accurately. The models and structures of BP neural network and convolution neural network are analyzed. The training and test samples are constructed for a power grid model, and the BP neural network and convolution neural network are used for simulation verification respectively. The simulation results show that the convolutional neural network based grid fault diagnosis method has higher accuracy and fault tolerance.

关键词： Fault diagnosis Training Fault tolerance Convolution Simulation Neural networks Fault tolerant systems

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Efficient Detection of DDoS Attacks in E-Government Clouds Using Sparse Neural Networks 4

Efficient Detection of DDoS Attacks in E-Government Clouds U...

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4th IEEE international Conference on ICT in business Industry and Government, ICTBIG 2024

作者： Bixapathi, A. Ali, H. Mohammed Maranan, R. Sudhakar, A.V.V. Beschi, I.S. Ramya, M. T.S Medchal district501301 India Srm Institute of Science and Technology Department Mechanical Engineering Ramapuram Campus Chennai89 India Simats Saveetha School of Engineering Department of Research and Innovation Tamil Nadu Chennai602105 India Sr University Department of Electrical and Electronics Engineering Telangana Warangal506371 India Department of Computer Applications Kovur Chennai India Lovely Professional University Division of Research and Development Jalandhar - Delhi G.T.Road Punjab Jalandhar144411 India

ISBN: (纸本)9798331518981

E-government (Electronic Government) refers to the use of information and communication technology (ICT) by governmental bodies to provide public services, manage government operations, and facilitate communication with citizens, businesses, and other government entities. E-government platforms have expanded significantly in recent years, offering a range of services such as online tax filing, license renewals, healthcare services, and more. These services, often hosted on cloud-based infrastructures, benefit from improved scalability, efficiency, and accessibility through the use of cloud computing technologies. However, the growing volume of legitimate traffic from a diverse range of users on e-government platforms makes distinguishing between normal and malicious traffic, such as DDoS (distributed Denial of Service) attacks, increasingly difficult. To address this challenge, this manuscript proposes the use of Sparse Neural Networks (SNN) for DDoS attack detection in cloud-based e-government systems. Initially, data from the CSE-CIC-IDS2018 dataset is collected and processed through a feature selection phase. The selected features are selected using the Energy Valley Optimizer. These optimized features are fed into the Sparse Neural Network, which categorizes traffic as benign, DDoS, or DoS. To further enhance the accuracy of DDoS detection, the Ladybug Beetle Optimization (LBOA) is employed to optimize the Sparse Neural Network's parameters, ensuring optimal performance in classifying attacks. This approach aims to safeguard cloud-based e-government systems from cyber threats while maintaining reliable public service delivery. In comparison to current methods such as data clustering for the detection of DDoS attacks in the cloud for e-government (DBSCAN), e-government impact factor analysis cleverly using enhanced machine learning (ML-EGOV), and Problems with Machine Learning in Cloud computing E-Government Models in Developing Nations, the proposed DCNN-QSA-PDO

关键词： Cloud platforms

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Application of Network Calculus Models to Heterogeneous Streaming applications

Application of Network Calculus Models to Heterogeneous Stre...

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IEEE international symposium on Parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Clayton J. Faber Roger D. Chamberlain SimpleRose Washington University in St. Louis. Dept. of Computer Science and Engineering Washington University in St. Louis

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Network calculus has seen extensive use in the performance modeling of communications systems. Here, we apply network calculus techniques to the modeling of streaming data applications running on heterogeneous computing platforms. We quantitatively compare the performance predictions from network calculus with predictions from a discrete-event simulation model and a previously presented queuing theory model.

关键词： distributed processing Communication systems Computational modeling Conferences Predictive models Calculus Data models

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Application of Machine Learning and Blockchain Technology in Improving Supply Chain Financial Risk Management 6

Application of Machine Learning and Blockchain Technology in...

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6th international Conference on Contemporary computing and Informatics, IC3I 2023

作者： Atta, Anas Ahmad Bani Ahmad, Ahmad Yahiya Ahmad Bani Allahham, Mahmoud Izzat Sisodia, Dharini Raje Singh, Rupesh Roshan Maginmani, Umme Habiba Middle East University Financial Technology Faculty of Business Financial and Accounting Science Department Amman11831 Jordan Middle East University Financial and Accounting Science Department Amman11831 Jordan College of Business LTUC Jordan Army Institute of Management and Technology Management India Block-14Mittal School of Business Lovely Professional University Punjab Phagwara India Secab Institute of Engineering and Technology Department of Computer Science & Engineering Karnataka Vijayapur India

ISBN: (纸本)9798350304480

The database used by blockchain innovation is run by numerous components and creates a network pattern using hash indexes. The blockchain employs numerous nodes and transmits numerous data access points, decreasing reliance on the primary Internet server and preventing the danger of data destruction and server breakdown. The data files saved in the blockchain are protected using encryption techniques to maintain their authenticity and prevent illegally added or discarded data files. With technological characteristics like non-tampering, distributed ledger, and reliability, blockchain innovations and machine learning have innate benefits in supply chain finance. They also have great prospects to construct faith in order to overcome the major issues in supply chain finance, which is helpful for fostering economic advancement in the Tonkin Gulf region. This paper focuses on introducing the study on using blockchain innovation in supply chain finance in the Tonkin Gulf region and aims to offer suggestions on how supply chain finance could evolve there using blockchain innovation. This paper suggests supply chain finance game applications for pertinent investigations as well as blockchain innovation, supply chain banking threats assessment on the blockchain and machine learning, and supply chain finance implementation study methodologies in the Tonkin Gulf region. The empirical findings presented in this paper demonstrate that the built blockchain supply chain finance platform has an algorithm with an overall processing time of 4.10 seconds, a computational efficiency that is quicker, and the ability to more accurately analyse the pertinent concerns. © 2023 IEEE.

关键词： Blockchain technology Machine learning supply chain financial risk

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Ensuring Web Application Security: An OWASP Driven Development Methodology

Ensuring Web Application Security: An OWASP Driven Developme...

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2024 international Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Sakthivel, M. Sivanantham, S. Bharathiraja, N. Bala Krishna, N. Kamalraj, R. Kumar, V. Saravana Sri Shanmugha College of Engineering and Technology Department of Artificial Intelligence and Data Science Salem Sankari India Saveetha Institute of Medical and Technical Sciences Department of Cse Saveetha School of Engineering Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Biomedical Engineering Chennai India Mohan Babu University School of Computing Department of Ai&ml Andhra Pradesh Tirupati India University School of Cs and It Jayanagar Bangalore India Sreenidhi Institute of Science and Technology Department of It Hyderabad India

ISBN: (纸本)9798350359688

Website security is a big threat for both large companies and individual developers, the more advanced the technology employed, the more challenging it is to build secure website development procedures. Hackers can easily access vulnerabilities that were not patched during development and that were thereafter distributed. This could result in significant financial losses for the business or the person. Users who visit vulnerable websites may be subject to XSS attacks that could compromise their systems, or an unsecure database configuration may result in a potential data leak, in which case the password of every user who has registered on the website is compromised. Users who use the same password on numerous websites run the danger of identity theft. This study offers the most common vulnerabilities that should be fixed in any application, giving developers the crucial place to start. The overwhelming number of vulnerabilities in each application that is being developed served as the impetus for this project. Every developer, regardless of expertise level, needs a place to start in order to fix any vulnerabilities that may have appeared in their program. The goal of this project is to create a safe online application in accordance with the recommendations of the Open Web Application Security Project (OWSAP). This demonstrates how web application vulnerabilities can be mitigated through coding changes, configuration adjustments, and patch applications. The top ten vulnerabilities according to OWASP are SQL injections, faulty authentication, sensitive data exposure, faulty access control, and XML external entities. The web application's security has been examined and shown to have defenses in place for those vulnerabilities. © 2024 IEEE.

关键词： Information leakage

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Reducing Data Bottlenecks in distributed, Heterogeneous Neural Networks 17

Reducing Data Bottlenecks in Distributed, Heterogeneous Neur...

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17th IEEE international symposium on Embedded Multicore/Many-core Systems-on-Chip, MCSoC 2024

作者： Lin, Ruhai Zhu, Rui-Jie Eshraghian, Jason K. Uc Santa Cruz Dept. of Electrical and Computer Engineering Santa CruzCA United States

ISBN: (纸本)9798331530471

The rapid advancement of embedded multicore and many-core systems has revolutionized computing, enabling the development of high-performance, energy-efficient solutions for a wide range of applications. As models scale up in size, data movement is increasingly the bottleneck to performance. This movement of data can exist between processor and memory, or between cores and chips. This paper investigates the impact of bottleneck size, in terms of inter-chip data traffic, on the performance of deep learning models in embedded multicore and many-core systems. We conduct a systematic analysis of the relationship between bottleneck size, computational resource utilization, and model accuracy. We apply a hardware-software co-design methodology where data bottlenecks are replaced with extremely narrow layers to reduce the amount of data traffic. In effect, time-multiplexing of signals is replaced by learnable embeddings that reduce the demands on chip IOs. Our experiments on the CIFAR100 dataset demonstrate that the classification accuracy generally decreases as the bottleneck ratio increases, with shallower models experiencing a more significant drop compared to deeper models. Hardware-side evaluation reveals that higher bottleneck ratios lead to substantial reductions in data transfer volume across the layers of the neural network. Through this research, we can determine the trade-off between data transfer volume and model performance, enabling the identification of a balanced point that achieves good performance while minimizing data transfer volume. This characteristic allows for the development of efficient models that are well-suited for resource-constrained environments. © 2024 IEEE.

关键词： Deep learning

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Diagnosis of power operation and maintenance records based on pre-training model and prompt learning

Diagnosis of power operation and maintenance records based o...

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international symposium on distributed computing and applications to business, engineering and science (DCABES)

作者： Jun Jia Hui Fu Ziyang Zhang Jinggang Yang Jiangsu Electric Power Test and Research Institute Electric Power Research Institute of State Grid Jiangsu Electric Power Co. Ltd Nanjing China State Grid Jiangsu Electric Power Co. Ltd Nanjing China

ISBN: (纸本)9781665454636

The operation and maintenance records of power equipment contain abundant historical operation state information of equipment. However, due to the characteristics of multi ambiguity, difficult to segment ambiguity and multi noise, this paper proposes a two-stage model for the text diagnosis of power equipment. First, the large-scale pre-training model is trained based on the massive text, and then the pre-training language model is fine-tuned by the prompt technology for equipment diagnosis. The proposed solution is assessed through experiments and the numerical results demonstrate that the proposed solution can achieve about 20% improvement over the traditional method.

关键词： Computational modeling Maintenance engineering Numerical models distributed computing business

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A Reflection on Methodologies, Algorithms and Software for HPDC 22

A Reflection on Methodologies, Algorithms and Software for H...

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31st international symposium on High-Performance Parallel and distributed computing (HPDC)

作者： Cappello, Franck Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781450391993

The core of the HPDC research and the main scientific contributions push forward the state of the art in how to program and execute parallel and distributed applications and analysis faster, more efficiently, more reliably, and in a more secure way. Researchers in our domain are facing two important challenges when transforming an idea into an innovative algorithm and an innovative algorithm into a successful software: evaluation methodology and software engineering. These challenges arise, in particular, every time a new branch of HPDC research topic emerges. In this talk, I will discuss the relation between methodology, algorithms, and software for HPDC. I will specifically focus on three domains: experimental platforms for HPDC research, fault tolerance at extreme scale, and lossy compression for scientific data. For these three domains I will review the motivations behind the research, the current situation, and what I see as the potential next directions.

关键词： Methodologies algorithms and software for parallel and distributed computing fault-tolerance and compression of scientific data

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