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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Zorarpaci, Ezgi Istanbul University Faculty of Computer and Information Technologies Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798331531492

Text categorization (TC) is a natural language processing (NLP) task commonly used in text mining. It is defined as the automatic assignment of text to a set of given categories. Ensemble-based methods that unify multiple models effectively improve the prediction performance and result in higher accuracies than a standalone classifier in general. Motivated by this idea, it is proposed to evaluate the performances of the different ensemble classifiers on Turkish TC dataset (i.e., TTC-3600) in this paper. Firstly, OneR attribute evaluator algorithm diminishes the high dimensionality of Turkish text training data. Different combinations of the base classification algorithms are then compared using this reduced data to assess the impact of ensemble learning on Turkish text data. Bayesian network (BN), extra tree (ET), random forest (RF), and multinomial naive bayes (MNB) classification algorithms are used for the base classifiers. bagging, adaBoost, and voting mechanisms are employed to unify these base classifiers in ensemble learning. According to the experimental results, the proposed model achieves a competitive classification accuracy (93.83 %) to the state-of-the-art (SOTA) methods. Moreover, the proposed method has a pretty low runtime that is only 125.8 seconds for TC task. © 2024 IEEE.

关键词： Contrastive Learning

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Multispectral point cloud superpoint segmentation

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Science China(Technological Sciences) 2024年第4期67卷 1270-1281页

作者： WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao GU YanFeng Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies Application Kunming 650500China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关键词： multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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Application of Infrared Thermographic Camera Software for Corrections of Radiation Values 6

Application of Infrared Thermographic Camera Software for Co...

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6th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2023

作者： Glavas, Hrvoje Jukic, Dina Baric, Tomislav Gavran, Mateo Computer Science and Information Technologies Faculty of Electrical Engineering Osijek Croatia

ISBN: (纸本)9798350328752

Infrared thermography as a non-destructive testing method provides radiometric records of surface temperature distribution in the form of an image. During thermographic measurements, emissivity coefficient should be (re)factored to obtain the correct temperature values. Corrections in corresponding software for infrared thermographic cameras for surfaces with high emissivities yield sufficiently accurate results. However, surfaces that are characterized by low emissivity which behaviors differ significantly from that of high emitters pose a challenge. This paper analyses the application of software assistance in the correction of radiation values of two surfaces with contrasting emissivities that compose the same object. Results of the analysis are presented in tabular and graphical form to illustrate the challenge of correction process. © 2023 IEEE.

关键词： Thermography (imaging)

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Fractal Measurement of Images Human Brain from MRI

Fractal Measurement of Images Human Brain from MRI

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Anarova, Shahzoda Jabbarov, Jamoliddin Uralova, Iroda Tashkent University of Information Technologies Department of Convergence of digital technologies Computer Engineering Faculty Tashkent Uzbekistan Department of Computer science and technology Intelligent systems and computer technologies Faculty Samarkand State University Samarkand Uzbekistan

ISBN: (纸本)9798400716928

This article is devoted to the determination of the fractal size of the damaged part of the human brain on the basis of images obtained from MRI (magnetic resonance imaging). There are various mathematical methods for determining the size of fractal structures, this article uses two variants of the cell counting method and describes the analysis of errors in determining the fractal size of the damaged part of the human brain. The article shows that the damaged part of the human brain has a fractal structure and that it has a fractal dimension. Fractal measurement has also been used to determine the size of the damaged part of the human brain, unlike common methods. In determining the fractal scale, cases of changes in human body organs were not taken into account. In the article, the determination of the size of the damaged part of the human brain is studied on the basis of mathematical formulas. Two different variants of the special cell counting method were used to calculate the fractal measurements, as well as the corresponding number of experimental results. In addition, these measurements were used to assess the extent to which patients suffered brain damage and to provide information on the practical application of these fractal measurements. © 2024 ACM.

关键词： Magnetic resonance imaging

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FastSlowMo: Federated Learning With Combined Worker and Aggregator Momenta

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1041-1050页

作者： Yang, Zhengjie Fu, Sen Bao, Wei Yuan, Dong Zomaya, Albert Y. University of Sydney Faculty of Engineering and Information Technologies School of Computer Science SydneyNSW2006 Australia University of Sydney School of Electrical and Information Engineering SydneyNSW2006 Australia

In this article, we propose and analyze FastSlowMo, a combined Nesterov accelerated gradient (NAG) style worker momentum and aggregator momentum algorithm for federated learning (FL). Existing NAG momentum-based FL algorithms depend on either worker momentum only or aggregator momentum only, which may be inefficient due to infrequent usage of momentum, data heterogeneity, and out-of-date momentum. FastSlowMo combines the advantages of worker momentum and aggregator momentum to address these issues. We then provide mathematical proof for the convergence of FastSlowMo. Finally, extensive experiments based on real-world datasets and trace-driven simulation are conducted, verifying that FastSlowMo decreases the total training time by 3-61%, and outperforms existing mainstream benchmarks under a wide range of settings. © 2020 IEEE.

关键词： Learning systems

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Automation of Monitoring and Optimization of Docker Containers Using Artificial Intelligence 24

Automation of Monitoring and Optimization of Docker Containe...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Stanišić, Sava Luković, Vanja Belotić, Branislav Faculty of Technical Sciences Department of Computer and Software Engineering Čačak Serbia Faculty of Technical Sciences Department of Information Technologies Čačak Serbia

ISBN: (纸本)9798331515799

The increasing adoption of Docker containers in modern software development requires effective monitoring and optimization to ensure high performance and efficient resource utilization. This paper provides an overview of the various tools and technologies that leverage artificial intelligence (AI) to automate the monitoring and optimization of Docker containers. The authors explore the capabilities of AI-driven tools such as machine learning-based anomaly detection, predictive analytics, and automated resource management. By examining these tools, the authors highlight their benefits in enhancing container performance, scalability, and availability. Through comparative analysis, the strengths and limitations of each tool and their impact on container management are discussed. The findings underscore the potential of AI to revolutionize container monitoring and optimization, leading to more efficient and robust IT infrastructure. © 2025 IEEE.

关键词： Resource allocation

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The Simulation Model of Multipath Data Transmission in Wireless Networks with Interference

The Simulation Model of Multipath Data Transmission in Wirel...

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2025 International Russian Smart Industry Conference, SmartIndustryCon 2025

作者： Abramova, Evgenia A. Duc, Ngo Minh Van Thuan, Nguyen University ITMO Faculty of Software Engineering and Computer Technology Saint Peterburg Russia University ITMO Faculty of Secure Information Technologies Saint Peterburg Russia

ISBN: (纸本)9798331511241

Load balancing methods in networks aim to distribute traffic evenly between nodes to prevent overloading of individual routes and improve overall network performance. For instance, machine learning-based routing allows you to analyze network behavior and predict optimal routes for data transmission, which is especially effective in complex dynamic networks. Another option is Pareto Optimization, which is used to consider multiple criteria, such as minimizing latency and energy consumption, simultaneously. The reliability of networks, especially wireless ones, is becoming an issue that requires solutions that can adapt to constantly changing conditions, creating stable communication systems. The article describes and tests a simulation model of a wireless network in which requests are sent from sources through towers to two different recipients simultaneously along several paths. The use of intermediate switching nodes allows using the AOMDV protocol to build different data transmission routes. The simulation results for various modifications of the model allow us to evaluate the advantages of using multipath routing. © 2025 IEEE.

关键词： Data communication systems

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Beyond Integration: Advancing Electricity Metering with Secure and Transparent Blockchain-IoT Solutions 47

Beyond Integration: Advancing Electricity Metering with Secu...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Koštál, Kristian Bahar, Muhammad Nasim Numyalai, Sultan Ries, Michal Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia

ISBN: (纸本)9798350365597

As the demand for reliable and accurate electricity metering escalates in the digital era, traditional systems increasingly need to improve due to their centralized nature, prone to inaccuracies, privacy breaches, and cybersecurity threats. This paper proposes a novel solution that harnesses the transformative power of Blockchain and Internet of Things (IoT) technologies to address these critical challenges. By integrating the immutable ledger capabilities of blockchain with the real-time data acquisition prowess of IoT, we introduce a decentralized system that ensures the secure and transparent measurement and billing of electricity for domestic consumers. Adopting Hyperledger Fabric within our framework offers a robust, scalable architecture that supports secure transactions and enhances trust through digital identities for all participants. This system mitigates the vulnerabilities associated with centralized authorities and efficiently monitors and manages energy consumption with unprecedented precision. Through this approach, we aim to revolutionize the electricity metering infrastructure, providing a blueprint for future advancements in the energy sector. © 2024 IEEE.

关键词： Smart contract

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Application of the digital twin model in higher education

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Multimedia Tools and Applications 2024年 1-18页

作者： Selim, Aybeyan Ali, Ilker Saracevic, Muzafer Ristevski, Blagoj Faculty of Engineering and Architecture International Vision University Gostivar Macedonia Department of Computer Sciences University of Novi Pazar Novi Pazar Serbia Faculty of Information and Communication Technologies – Bitola University "St. Kliment Ohridski" Bitola Macedonia

Globalization and technological development are the two main pillars of the basis of modern enterprises. Different business decisions want to determine the balance between the potential of the company and the demand for its products or services. Making decisions requires timely and accurate information obtained from management information systems. The technology of digital twins is a relatively new technology, which proves its value day by day through optimization during the development of projects. This study presents the application model of digital twin technology, which uses the NFC (near-field communication) protocol for user authentication and authorization. As a bridge between the natural and digital, we use the digital twin to create a copy of the services in a digital environment. NFC technology dramatically simplifies the interaction between mobile accessories and allows data transfer between devices. Our model collects the data and allows storing, analyzing, and using them for different purposes. We tested the proposed model with the decision tree and the k-means algorithms. The testing results showed that digital twins are fruitful and promising perspectives in effective future educational process planning. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： information management

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