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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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Handling Rollbacks with Separated Response Control Service for Microservice Architecture 3

Handling Rollbacks with Separated Response Control Service f...

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3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Gordesli, Mustafa Nasab, Ahad Varol, Asaf Maltepe University College of Engineering and Natural Sciences Department of Computer Engineering Istanbul Turkey The University of Chattanooga College of Engineering and Computer Science Dept. of Engineering Management and Technology ChattanoogaTN United States

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

Working with large scale microservice based applications may be hard to maintain and control. The services in those applications must be isolated from each other and they must work in harmony. There must be an intelligent transaction management system to implement this kind of collaborative work of microservices. There are two main methodologies for transaction management, namely Two Phase Commit (2PC) and Saga pattern. There are two phases in 2PC that prepare phase and commit phase. In Saga pattern, there are two ways of implementing Saga pattern;choreography based, and orchestration based. When there is a problem in the whole transaction, controlling the rollback scenario may be hard because each microservice has its own rollout and rollback solutions in both ways of Saga pattern. If we separate the rollback system from the microservices, we might have simpler microservices and microservices that can run in parallel. So whole system works safer and faster. In this work we focus on parallel execution of microservices with separate rollback system that controls the whole application in error condition. © 2022 IEEE.

关键词： Federated learning Microservice architectures computer architecture Control systems Informatics Software engineering

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Third international conference on Electronics and Communication;Network and computer Technology, ECNCT 2021

Third International Conference on Electronics and Communicat...

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3rd international conference on Electronics and Communication;Network and computer Technology, ECNCT 2021

ISBN: (纸本)9781510652101

The proceedings contain 119 papers. The topics discussed include: research on error and correction system of electric energy experimental metering device based on configuration design;two applications of machine learning on COVID-19;optimal control of slow traffic at intersections based on green building materials and signal processing technology;design of off-line detection device for spare parts of shipborne satellite communication antenna servo system;a garbage collection method based on integrity and mobile communication technology;mimic honeypot based on dual mimicry mechanism;the application value of deep convolution level set in ABUS image analysis of varicose vein of lower extremity;reliability optimization of distribution network based on the interaction between electric vehicles and distributed generations;and design of meter-reading recognition system for intelligent converter station based on machine vision technology.

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The Whole Process Education Management System Based on machine learning Algorithm 1

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3rd EAI international conference on application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Ma, Jun Nanning University Guangxi Nanning530200 China

ISBN: (数字)9783031631399

ISBN: (纸本)9783031631382

In recent years, with the development of education and teaching in our country and the continuous deepening of new curriculum reform, the subject of ideology and politics occupies an increasingly important teaching position in the education and teaching of university in our country. How to improve the quality of education and teaching of political thought theory courses in university is very important. Therefore, in the education and teaching process of political thought theory courses in university, it is necessary to adhere to the integrity and continuity of education guidance, so as to provide high-quality political thought theory teaching for college students, and guide students to use political thought theory in real life. Only by solving problems with theoretical knowledge content can we improve the teaching quality of political thought theory courses in university. This article will focus on the establishment of an political thought theory course education system that keeps pace with the times, the establishment of a "monitoring mechanism" for moral education behaviors to do a good job in post-grant service, and the introduction of topic-based teaching into the political thought theory course education in university. From these three aspects This paper analyzes the strategies for improving the education quality of political thought theory courses in university under the whole-process education model, in order to provide constructive suggestions for the improvement of the teaching quality of political thought education in university. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Teaching

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Design of Web application Firewall System through Convolutional Neural Network and Deep learning 3

Design of Web Application Firewall System through Convolutio...

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3rd international conference on computers, Information Processing and Advanced Education, CIPAE 2022

作者： Wang, Shihao Liu, Ruiheng Guo, Xu Wei, Gaoda School of Software Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665468121

Attacks against web applications emerge in endlessly, and protecting the security of web applications is an important part of the current cyberspace security. This paper constructs one cloud web application firewall system based on deep learning. The system architecture adopts a multi-server distributed architecture. Taking into account the real-time nature of web requests and the user's sense of user experience, through the use of a high-performance server framework Openers and high-efficiency volumes. The convolutional neural network improves the operating efficiency of the system while ensuring the recognition rate. At the same time, the system implemented in this paper also saves server request information in the database to further provide targeted data sets for artificial intelligence training. © 2022 IEEE.

关键词： Cybersecurity

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Classification tree algorithm and its application in general archives management system 3

Classification tree algorithm and its application in general...

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3rd international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Zhao, Ziye Xi'An Medical University Shaanxi Xi'an721000 China

With the rapid development of higher education in China, more and more archives are managed by the archives of colleges and universities. Therefore, many colleges and universities have equipped archives management software to manage archives by computer. However, at present, the mainstream archives management software for colleges and universities does not have the function of automatic classification of archives. In order to reduce the workload of college archives staff, this paper explores a text automatic classification method suitable for college archives. Classification tree algorithm is a method of organizing and storing information. The classification tree is used to classify data into various categories, such as customers and products. It also provides an effective way to search for data using keywords or phrases. Classification tree is very useful in document processing, because it can help us find documents with similar characteristics, such as customers with similar requirements, products with similar functions, etc. The working principle of classification tree algorithm is to decompose the problem into smaller subproblems, and then solve one subproblem at a time until all problems are completely solved. © 2023 Elsevier B.V.. All rights reserved.

关键词： Trees (mathematics)

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Customizable Text-to-Image Modeling by Contrastive learning on Adjustable Word-Visual Pairs 3rd

Customizable Text-to-Image Modeling by Contrastive Learning ...

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3rd international conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 24th international conference on Human-computer Interaction (HCII)

作者： Lu, Jun-Li Ochiai, Yoichi Univ Tsukuba Res & Dev Ctr Digital Nat Tsukuba Ibaraki Japan Univ Tsukuba Fac Lib Informat & Media Sci Tsukuba Ibaraki Japan

ISBN: (纸本)9783031056437;9783031056420

Co-creation with AI is trending and AI-generation of images from textual descriptions has shown advanced and attractive capabilities. However, commonly trained machine-learning models or built AI-based systems may have deficient points to generate satisfied results for personal usage or novice users of painting or AI co-creation, maybe because of deficient understanding of personal textual expressions or low customization of trained text-to-image machine learning models. Therefore, we assist in creating flexible and diverse visual contents from textual descriptions, by developing neural-networks models with machine learning. In modeling, we generate synthesized images using word-visual co-occurrence by Transformer model and synthesize images by decoding visual tokens. To improve visual and textual expressions and their relevance with more diversities, we utilize contrastive learning applying on texts, images, or pairs of texts and images. In the experimental results of a dataset of birds, we showed that the rendering quality was required of models with some scale neural-networks, and necessary training process with fined training by applying relatively low learning rates until the end of training. We further showed contrastive learning was possible for improvement of visual and textual expressions and their relevance.

关键词： AI visual content creation Text-to-image model Contrastive learning

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A Research On The New Generation Artificial Intelligence Technology Generative Pretraining Transformer 3 3

A Research On The New Generation Artificial Intelligence Tec...

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3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Aydin, Nazif Ayhan Erdem, O. Gazi University Graduate School of Natural and Applied Sciences Department of Computer Engineering Ankara Turkey Gazi University Faculty of Technology Department of Computer Engineering Ankara Turkey

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

In the digitalizing world, Artificial Intelligence (AI) paves the way for the automation of routine work done by humans and makes life easier. Recently, there is no area where AI and its applications are not used in daily life, from health to education, from transportation to energy, and from agriculture to tourism. AI applications are making rapid progress in the direction of important and current developments, especially in Natural Language Processing (NLP) and Deep learning (DL). A concrete example of progress in these areas is the GPT-3 (Generative Pre-trained Transformer 3) language model. AI-assisted GPT-3 technology is the DL model that is effectively used in many NLP fields, which can produce long and consistent content similar to the texts written by people using pre-trained algorithms. The GPT-3 architecture has reached a level that can compete with people in many areas by producing optimum solutions for all kinds of inputs by using the Transformer-based language model, which is an attention-based deep learning technique. This article aims to convey the efficiency, structure and potential of the Transformer-assisted GPT-3 model, which is one of the most up-to-date NLP technologies, to the reader. Since the number of Turkish studies in the field of GPT-3 AI is quite limited, it is considered that this study will contribute to the literature in terms of both quantity and quality. In addition, the performance parameters of the model were examined by making a customized fine-tuned sample application in the beta version of the GPT-3 model. © 2022 IEEE.

关键词： Deep learning

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Fair and Private CT Contrast Agent Detection 2nd

Fair and Private CT Contrast Agent Detection

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2nd international Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and 3rd international Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with the international conference on Medical Image Computing and computer Assisted Interventions, MICCAI 2024

作者： Kaess, Philipp Ziller, Alexander Mantz, Lea Rueckert, Daniel Fintelmann, Florian J. Kaissis, Georgios AI in Healthcare and Medicine Technical University of Munich Munich Germany Department of Radiology Massachusetts General Hospital Boston United States Department of Diagnostic and Interventional Radiology University Medical Center of the Johannes Gutenberg University Mainz Mainz Germany Department of Computing Imperial College London London United Kingdom Institute of Machine Learning in Biomedical Imaging Helmholtz Munich Germany

ISBN: (纸本)9783031727863

Intravenous (IV) contrast agents are an established medical tool to enhance the visibility of certain structures. However, their application substantially changes the appearance of Computed Tomography (CT) images, which - if unknown - can significantly deteriorate the diagnostic performance of neural networks. Artificial Intelligence (AI) can help to detect IV contrast, reducing the need for labour-intensive and error-prone manual labeling. However, we demonstrate that automated contrast detection can lead to discrimination against demographic subgroups. Moreover, it has been shown repeatedly that AI models can leak private training data. In this work, we analyse the fairness of conventional and privacy-preserving AI models during the detection of IV contrast on CT images. Specifically, we present models which are substantially fairer compared to a previously published baseline. For better comparability, we extend existing metrics to quantify the fairness of a model on a protected attribute in a single value. We provide a model, fulfilling a strict Differential Privacy protection of (Ε,δ)=(8,2.8·10-3), which with an accuracy of 97.42% performs 5%-points better than the baseline. Additionally, while confirming prior works, that strict privacy preservation increases the discrimination against underrepresented subgroups, the proposed model is fairer than the baseline over all metrics considering race and sex as protected attributes, which extends to age for a more relaxed privacy guarantee. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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Liver Disease Prediction using Semi Supervised based machine learning Algorithm 3

Liver Disease Prediction using Semi Supervised based Machine...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Rani, A.Jaya Mabel Nishanthini, S. Josephine, D.C.Jullie Venugopal, Hridya Nissi, S.Gracia Jacintha, V. Kings Engineering College Department of Computer Science and Engineering Chennai India Kings Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Recently, liver disease is emerging as one of the most common diseases with high fatality rate. The number of liver affected patients are steadily increasing due to various factors such as excessive alcohol consumption, inhalation of hazardous fumes, eating tainted food, pickles, and narcotics. Liver disease can also lead to a variety of serious illnesses, including liver cancer. To enhance the process of liver disease classification, a semi supervised machine learning algorithm has been presented in this research work. The use of liver patient datasets in the development of classification algorithms to predict liver disease is being investigated. The proposed study employs hybrid SVM and K-Means algorithm-based model to examine the entire patients' liver disease. Chronic liver disease is defined as a liver ailment that exist for at least six months. As a result, the considered population will be classifiers as liver disease affected and non-affected people. The SVM classifiers are used to determine the number of liver disease affected individuals, and then the hybrid k means clustering method is used to determine how much of the liver has been affected. As a consequence, the output from the proposed hybrid K-Means clustering model reveals high prediction accuracy. © 2022 IEEE.

关键词： Forecasting

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The application of Building Information Model in the Structural Design of Intelligent Buildings 3

The Application of Building Information Model in the Structu...

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3rd international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Zhao, Huizhi Architecture and Environmental Engineering Dongying Vocational Institute Shandong Dongying257091 China

Due to the irreversibility of building structures in the construction process, unreasonable construction methods are likely to cause economic losses and great negative social impacts. Therefore, the study of scientific and efficient intelligent construction methods for building structures has become a research hotspot in recent years, mainly including intelligent building design, construction control and safety analysis methods. Therefore, it is very meaningful and necessary to carry out research on intelligent construction technology based on BIM technology. By applying BIM technology to the process of intelligent construction of structures, this paper intends to achieve six major objectives such as building performance analysis and parametric design of structures, and independently develop a BIM construction process management platform, which combines construction progress, quality, safety, cost and green construction through BIM technology for information management of projects, making it more convenient for owners and construction unit staff to software application, thus greatly improving the communication and use efficiency, and providing important guidance for the construction process control and progress control of the project. © 2023 Elsevier B.V.. All rights reserved.

关键词： Information management

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