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Готові списки джерел за темами / Intelligent Resource Management / Статті в журналах

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Intelligent Resource Management.

Статті в журналах з теми "Intelligent Resource Management"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

Liu, Zhi, Qiang Liu, Ryan Shea, Wei Cai, Zehua Wang, and Yongyi Ran. "Intelligent resource management for 5G." Wireless Networks 26, no. 3 (2020): 1535–36. http://dx.doi.org/10.1007/s11276-020-02270-x.

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2

Ma, Ruhao, Fansheng Meng, and Haiwen Du. "Research on Intelligent Emergency Resource Allocation Mechanism for Public Health Emergencies: A Case Study on the Prevention and Control of COVID-19 in China." Systems 11, no. 6 (2023): 300. http://dx.doi.org/10.3390/systems11060300.

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Анотація:

The outbreak of COVID-19 posed a significant challenge to the emergency management system for public health emergencies, especially in China, where the epidemic began. As intelligent technology has injected new vitality into emergency management, applying intelligent technology to optimize emergency resource allocation (ERA) has become a focus of research in the post-epidemic era. Based on China’s experience in preventing and controlling COVID-19, this paper first analyzes the characteristics and process of ERA in public health emergencies, and then synthesizes the relevant Chinese studies in

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3

Wang, Zheng, and Mei Dong. "Multi-users Coordinated Sharing Grid Resource Management System." MATEC Web of Conferences 246 (2018): 03039. http://dx.doi.org/10.1051/matecconf/201824603039.

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The fundamental task of grid resource management system is to supply on-demand resources service for various users and applications by aggregate heterogeneous, dynamic and increase resource. Most current grid resource management system is exclusive which brings two sides of deficiency distinctively. We bring forward and realize a multi-user coordinated sharing resource management system, which is a utility oriented resource management system to support resources sharing by multi users or multi workload schedulers, thus extendable resource serving capability can be improved. When the system is

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4

Gong, Hao, and Chen He. "Intelligent Management of Land Resources Based on Internet of Things and GIS Technology." Journal of Sensors 2022 (May 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/2216581.

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In order to improve the effect of land resource management, this paper combines the Internet of Things technology and GIS technology to build an intelligent management system for gradient resources to improve the efficiency of land resource management. Aiming at the hybrid intelligent model of wetland resource remote sensing monitoring technology, this paper analyzes and studies the remote sensing image processing theory. Moreover, this paper studies in detail remote sensing image restoration, TM image reflectivity simulation imaging, image enhancement technology, optimal band selection based

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5

Sun, Heng, Wan Ni, and Peng Zhao. "Design of a Media Resource Management System for Colleges Based on Cloud Service." International Journal of Emerging Technologies in Learning (iJET) 15, no. 21 (2020): 231. http://dx.doi.org/10.3991/ijet.v15i21.18195.

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Анотація:

In recent years, many new technologies, such as the Internet, cloud computing, and the 5th-generation (5G) mobile network, have been widely applied in colleges. The structure and functions of traditional media technology can no longer satisfy the demand for media resource management (MRM) in colleges. Therefore, this paper creates an intelligent MRM system based on cloud service, providing a brand-new intelligent MRM solution. Through cloud service and artificial intelligence (AI), the proposed system relies on intelligent algorithms to realize automatic, comprehensive, and quick identificatio

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6

Yabanci, Orhan. "From human resource management to intelligent human resource management: a conceptual perspective." Human-Intelligent Systems Integration 1, no. 2-4 (2019): 101–9. http://dx.doi.org/10.1007/s42454-020-00007-x.

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7

Tian, Xue Yong, Tian Qing Chang, Shao Hua Shi, Lei Zhang, and Yang Han. "Modeling of Test Resource Based on Multi-Agent and its Application in Intelligent Virtual Instrument." Advanced Materials Research 139-141 (October 2010): 1973–76. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1973.

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Because of the poor reconfigurability of the existing test resource models and the faultiness of the intelligent modeling methods in automatic test system, the modeling method based on Multi-Agent was studied. By analyzing the characters of test resources, the test resource models were classified as device model, adapter model and configuration model. The typical test resource model based on Multi-Agent with three layers and five agents was built. The structure and function of system management agent, model reconfiguration agent, function management agent, channel control agent and function re

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8

Zhu, Jialing. "Analysis on the Application of Artificial Intelligence in the Field of Logistics." Frontiers in Business, Economics and Management 14, no. 3 (2024): 66–68. http://dx.doi.org/10.54097/3pd01b02.

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This paper discusses the wide application of artificial intelligence in the logistics industry, from intelligent distribution to intelligent transportation, through route optimization, automated warehouse management, demand forecasting, inventory management and other means, greatly improve logistics efficiency and reduce costs. Intelligent transport enhances traffic management, ensures road safety and optimizes resource utilization with road condition monitoring, autonomous driving and freight optimization technologies. In the face of challenges such as data security and privacy protection, ar

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9

Cai, Yong. "Intelligent Application of Digital Shared Management System in Journal Resource Integration." 3C TIC: Cuadernos de desarrollo aplicados a las TIC 13, no. 1 (2024): 159–77. http://dx.doi.org/10.17993/3ctic.2024.131.159-177.

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In this paper, a journal resource integration system under digital sharing was designed to assess the symmetry of the journal resource matrix using the shared resource matrix analysis method, so that the journal resources can transmit information covertly among the subjects. Subsequently, the collected journal resource data were processed to improve the accuracy of information categorization and integration to form a perfect resource management system. The nature of journal resources was deeply understood by calculating the characteristic observable vectors of journal resources. Finally, with

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10

Smith, S. F., D. W. Hildum, and D. R. Crimm. "Comirem: An Intelligent Form for Resource Management." IEEE Intelligent Systems 20, no. 2 (2005): 16–24. http://dx.doi.org/10.1109/mis.2005.23.

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11

Anderson, J., and M. Evans. "Intelligent agent modelling for natural resource management." Mathematical and Computer Modelling 20, no. 8 (1994): 109–19. http://dx.doi.org/10.1016/0895-7177(94)90235-6.

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12

Yi, ChongYu, Yongqiang Liu, and Zhao Zhiqiang. "Multi-source Intelligent Management System of College Snow and Ice Teaching Based on Cloud Platform." Mathematical Problems in Engineering 2022 (May 31, 2022): 1–8. http://dx.doi.org/10.1155/2022/9881970.

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Анотація:

The conventional college snow and ice teaching multi-source resource intelligent management system has the problem of incomplete software resource analysis function, which leads to high CPU usage of system. A cloud platform based college snow and ice teaching multi-source resource intelligent management system is designed. Hardware part: record the state of the pins when the register is reset, account for the data bit width and storage capacity of DDR2 SDRAM memory, and optimise the data storage module; Part of the software: obtain ice and snow sports course objectives in colleges and universi

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13

Wang, Jingsheng, and Siyuan Hu. "Research on the Construction of Intelligent Media Ideological and Political Learning Platform Based on Artificial Intelligence Technology." Journal of Mathematics 2022 (January 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/6511962.

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Анотація:

Aiming at the problems of slow teaching resource sharing rate, long platform response time, and low student learning efficiency in traditional ideological and political learning platforms, a research on the construction of intelligent media ideological and political learning platforms based on artificial intelligence technology is proposed. We build an artificial intelligence open source development platform framework based on the cloud platform and use the Ceph method to optimize the storage of artificial intelligence training platform data. Under this framework, we design the business proces

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14

Selvarajan, Guru Prasad. "AI-Driven Cloud Resource Management and Orchestration." International Journal of Innovative Research in Science, Engineering and Technology 13, no. 11 (2024): 19367–80. https://doi.org/10.15680/ijirset.2024.1311206.

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Cloud computing has revolutionized the way organizations deploy and manage their IT infrastructure. However, the increasing complexity and scale of cloud environments pose significant challenges in resource management and orchestration. This paper explores the application of Artificial Intelligence (AI) techniques to enhance cloud resource management and orchestration. We present a comprehensive review of existing AI-driven approaches, propose a novel framework for intelligent cloud resource management, and evaluate its performance through extensive simulations and real-world case studies. Our

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15

Arjunan, Prabu. "A Framework for Resource-Efficient Large Language Model Fine-tuning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–8. https://doi.org/10.55041/ijsrem25112.

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Анотація:

This paper proposes a systematic framework for the fine-tuning process of Large Language Models. The proposed method addresses the computational challenges that naturally arise in LLM fine-tuning by incorporating a comprehensive architecture that effectively integrates intelligent resource management along with adaptive training strategies. Our framework introduces dynamic resource allocation mechanisms along with efficient data processing pipelines, thereby making LLM fine-tuning more accessible to organizations with limited computational resources. The methodology outlined focuses on model q

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16

Di, Danlin. "Application Strategies and Fields of Digital Technology in Urban Public Service Resource Allocation." Applied and Computational Engineering 113, no. 1 (2024): 9–13. https://doi.org/10.54254/2755-2721/2024.melb18228.

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This article discusses the application of digital technology in the allocation of urban public service resources, emphasizing the importance of big data, the Internet of Things, and artificial intelligence technologies in improving the efficiency of resource matching. In the field of transportation, by setting up sensors and monitoring equipment, establishing regional traffic information exchange platforms, intelligent traffic light systems, and real-time dispatch systems have effectively alleviated traffic congestion and optimized the allocation of public transportation resources. In terms of

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17

Yun, Deok-Won, and Won-Cheol Lee. "Intelligent Dynamic Real-Time Spectrum Resource Management for Industrial IoT in Edge Computing." Sensors 21, no. 23 (2021): 7902. http://dx.doi.org/10.3390/s21237902.

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Анотація:

Intelligent dynamic spectrum resource management, which is based on vast amounts of sensing data from industrial IoT in the space–time and frequency domains, uses optimization algorithm-based decisions to minimize levels of interference, such as energy consumption, power control, idle channel allocation, time slot allocation, and spectrum handoff. However, these techniques make it difficult to allocate resources quickly and waste valuable solution information that is optimized according to the evolution of spectrum states in the space–time and frequency domains. Therefore, in this paper, we pr

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18

Pradeep Kurra. "Hyperautomation and AI: The Next Evolution in Cloud Workload Management." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 3268–78. https://doi.org/10.32628/cseit25112805.

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Hyperautomation and artificial intelligence are revolutionizing cloud workload management by transforming static, rule-based automation into dynamic, autonomous systems capable of self-learning and adaptation. This transformation addresses critical challenges in increasingly complex multi-cloud environments where traditional approaches have proven inadequate. Drawing on extensive empirical data from global implementations, hyperautomation demonstrates remarkable improvements across operational domains. By combining machine learning, advanced analytics, and process automation within unified fra

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19

Lobacheva, A. S., and O. V. Sobol. "Ethics of the application of artificial intelligence in human resource management." E-Management 4, no. 1 (2021): 20–28. http://dx.doi.org/10.26425/2658-3445-2021-4-1-20-28.

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Анотація:

The article reveals the main ethical problems and contradictions associated with the use of artificial intelligence. The paper reveals the concept of “artificial intelligence”. The authors analyse two areas of ethical problems of artificial intelligence: fundamental ideas about the ethics of artificial intelligent systems and the creation of ethical norms.The paper investigates the work of world organizations on the development of ethical standards for the use of artificial intelligence: the Institute of Electrical and Electronics Engineers and UNESCO. The study analyses the main difficulties

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20

Xu, Chenhan, Kun Wang, and Mingyi Guo. "Intelligent Resource Management in Blockchain-Based Cloud Datacenters." IEEE Cloud Computing 4, no. 6 (2017): 50–59. http://dx.doi.org/10.1109/mcc.2018.1081060.

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21

Zhang, Yu, Jianguo Yao, and Haibing Guan. "Intelligent Cloud Resource Management with Deep Reinforcement Learning." IEEE Cloud Computing 4, no. 6 (2017): 60–69. http://dx.doi.org/10.1109/mcc.2018.1081063.

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22

Al agha, Khaldoun. "Resource management in wireless networks using intelligent agents." International Journal of Network Management 10, no. 1 (2000): 29–39. http://dx.doi.org/10.1002/(sici)1099-1190(200001/02)10:1<29::aid-nem349>3.0.co;2-r.

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23

Sui, Xianfu, Xin Lu, Yuchen Ji, et al. "Intelligent Oil Production Management System Based on Artificial Intelligence Technology." Processes 13, no. 1 (2025): 133. https://doi.org/10.3390/pr13010133.

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Анотація:

Production management serves as a pivotal component in the operational activities of oilfield sites, with the effectiveness of management practices directly influencing the success of developmental outcomes. To enhance the maintenance-free operational period of oil production systems, elevate management standards, and reduce overall operational costs, advanced technologies such as artificial intelligence (AI) and big data analytics have been strategically integrated into oilfield operations. These technologies are able to incorporate data resources from all stages of oilfield production, thus

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24

Adamik, Anna. "Technology Intelligence as a One of the Key Factors for Successful Strategic Management in the Smart World." European Management Studies 2023, no. 3 (2023): 71–101. http://dx.doi.org/10.7172/2956-7602.101.4.

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Анотація:

Purpose: Due to the poor recognition in the literature of the phenomenon of Technology Intelligence in contemporary organizations, the aim of the paper is to attempt to recognize the essence and significance of Technology Intelligence of organizations operating in a Smart World. Design/methodology/approach: The paper is conceptual-empirical in nature. The operationalization proposal is based on the identification and assessment of the Technology Intelligence of organizations as one of the key pillars of effective management in the Smart World. Findings: An organization’s Technology Intelligenc

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25

Huang, Guo Quan, Sui Xun Guo, and Wei Xiong. "Model Design of Self-Service Intelligent Resource Management System Based on Cloud Platform." Advanced Materials Research 846-847 (November 2013): 1491–95. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1491.

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Анотація:

The resources construction under the cloud platform should be considered a full range from resource integration and virtualization, resource sharing, resource elastic scalability and user interaction. This paper first analyzes the present situation of resource management based on cloud platform, points out the existing problems and shortcomings of the resource management platform . On this basis, proposed the establishment of an open, interactive, self-service intelligent resource management system model, which consists of three functional modules of organic composition.

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26

Ramamohan Kummara. "AI-Driven Cloud Optimization : Transforming Modern Infrastructure Management." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1152–69. https://doi.org/10.32628/cseit25112447.

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Анотація:

This article explores how AI-driven cloud optimization is transforming modern infrastructure management by enabling organizations to maximize their cloud investments while maintaining optimal performance. The convergence of artificial intelligence, machine learning, and cloud computing technologies has created systems capable of analyzing operational patterns, predicting resource requirements, and automatically adjusting cloud configurations without human intervention. It examines five key benefits of AI-driven optimization: cost reduction through intelligent resource allocation, performance e

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27

PETRE, Alina-Georgiana. "REDEFINING HUMAN RESOURCE MANAGEMENT IN THE HEALTHCARE SECTOR THROUGH ARTIFICIAL INTELLIGENCE." Business Excellence and Management 15, S.I.5 (2025): 48–57. https://doi.org/10.24818/beman/2025.s.i.5-05.

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Анотація:

We are living in a new era dominated by technological transformations designed for contributing to the efficiency of work through intelligent automation processes of certain activities in a wide range of industries. The implementation of artificial intelligence in human resource management had a major transformational impact by creating new and innovative processes in branches such as employee recruitment, employee evaluation, employee development, managerial decision-making. One of the sectors of activity in which artificial intelligence can have a crucial role is that of healthcare. The inte

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28

Rayaprolu, Ranjith, Kiran Randhi, and Srinivas Reddy Bandarapu. "Intelligent Resource Management in Cloud Computing: AI Techniques for Optimizing DevOps Operations." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 6, no. 1 (2024): 397–408. http://dx.doi.org/10.60087/jaigs.v6i1.262.

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Efficient resource management is a cornerstone of cloud computing, particularly for DevOps operations where automation and scalability are critical. Traditional resource allocation approaches often fall short in dynamic environments, leading to over-provisioning, under-utilization, or service disruptions. This paper explores how artificial intelligence (AI) techniques can optimize resource management in cloud environments, enhancing the performance and efficiency of DevOps workflows. We examine methods such as predictive analytics, reinforcement learning, and anomaly detection, providing case

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29

Zhong, Ruibo. "The Status Quo and Problem Analysis of the application of intelligent enterprise management accounting in the Big Data Era." Global Finance Review 3, no. 1 (2021): 17. http://dx.doi.org/10.18282/gfr.v3i1.1646.

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Анотація:

Management accounting plays a pivotal role in the operation of an enterprise. By means of management accounting, an enterprise could make reasonable accounting of enterprise costs, optimize resource allocation within enterprises and reduce the waste of resources. Therefore, management accounting is the major resource of the information that helps with decision-making of business managers. In recent years, the big data technology and intelligent technology has been continuously improved, so the traditional management accounting model is facing great challenges. Therefore, the application of int

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30

Sahaidak, Mykhailo, Marina Zavyalova, and Mariia Tepliuk. "INTELLIGENT RESOURCE ASYMMETRIES IN THE LOGISTICS MANAGEMENT OF WAREHOUSING IN THE ENTERPRISE." Scientific Journal of Polonia University 29, no. 4 (2018): 51–60. http://dx.doi.org/10.23856/2908.

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Анотація:

The article analyzes the theoretical and methodological foundations of enterprise resource management, namely: the relationship between the economic essence of the category “resources” and such concepts as intellectual resources, intellectual potential, intellectual support and intellectual resource asymmetries are investigated. This allowed to actualize the issues of improving logistics warehouse management in the enterprise in the context of resource theory. The results of the analysis of the main obstacles in ensuring of increase in the efficiency of functioning of the modern warehouse comp

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31

Zhao, Yi Jing, Yan Yan, Guo Xin Wang, Jia Hao, and Si Chen. "Semantic Based Intelligent Resource Service Composition Technology." Advanced Materials Research 591-593 (November 2012): 2456–60. http://dx.doi.org/10.4028/www.scientific.net/amr.591-593.2456.

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Intelligent resource includes the knowledge, hardware and software resources even workflow and human resource, etc. From the point of view of enterprise information management, the service composition is an essential element for companies’ informatization construction. Therefore, the paper describes a web service composition framework for semantically-annotated web service. Based on the state of art ontology technology, an OWL-S process model for the CAM software is established to describe resource service. Then a service discovery process which mainly consists of services’ property matching a

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32

Liu, Shiyan, and Yu Hu. "Intelligent Development of Tourism Resources Based on Internet of Things and 5G Technology." Advances in Multimedia 2022 (September 21, 2022): 1–10. http://dx.doi.org/10.1155/2022/2202414.

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In order to improve the effect of intelligent development of tourism resources, this paper combines the Internet of Things technology and 5G technology to carry out intelligent development of tourism resources to improve the recommendation effect of modern tourism resources. Meanwhile, this paper analyzes the basic knowledge of array signal DOA estimation to get the definition of narrowband signal. Simultaneously, this paper proposes a subspace-based DOA estimation algorithm based on the actual needs of tourism resource management, and uses the observed array data to construct a covariance mat

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33

ХОРКАНІН, МАКСИМ. "МОДЕЛЮВАННЯ ВЗАЄМОДІЇ В РОЗПОДІЛЕНИХ ІНТЕЛЕКТУАЛЬНИХ ІНФОРМАЦІЙНИХ СИСТЕМАХ МЕТОДАМИ АВТОМАТИЧНОГО ПЛАНУВАННЯ". Herald of Khmelnytskyi National University. Technical sciences 335, № 3(1) (2024): 293–96. http://dx.doi.org/10.31891/2307-5732-2024-335-3-39.

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Анотація:

This paper delves into the intricacies of resource management and task scheduling within distributed information systems, aiming to propose a processing flow for effective coordination and information exchange among system components. The research focuses on the coordination of actions and information exchange within distributed intelligent systems, highlighting the issues stemming from asynchronous operations, resource management complexities, and the necessity to adapt to dynamic environmental conditions. By leveraging these challenges, the study presents results showcasing the impact on tas

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34

Dmitrieva, J., D. Okuneva, and V. Elagin. "Analyzing Traffic Identification Methods for Resource Management in SDN." Proceedings of Telecommunication Universities 9, no. 6 (2023): 42–57. http://dx.doi.org/10.31854/1813-324x-2023-9-6-42-57.

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Анотація:

The article is devoted to the analysis of traffic classification methods in SDN network. The review of analytical approaches of traffic identification to identify the solutions used in them, as well as assessing their applicability in the SDN network. Types of machine learning are considered and input parameters are analyzed. The methods of intelligent analysis covered in the scientific articles are systematized according to the following criteria: traffic identification parameters, neural network model, identification accuracy. Based on the analysis of the review results, the conclusion is ma

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35

Yang, Jui-Pin, and Hui-Kai Su. "Integrated Resource Management for Fog Networks." Sensors 22, no. 6 (2022): 2404. http://dx.doi.org/10.3390/s22062404.

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In this paper, we consider integrated resource management for fog networks inclusive of intelligent energy perception, service level agreement (SLA) planning and replication-based hotspot offload (RHO). In the beginning, we propose an intelligent energy perception scheme which dynamically classifies the fog nodes into a hot set, a warm set or a cold set, based on their load conditions. The fog nodes in the hot set are responsible for a quality of service (QoS) guarantee and the fog nodes in the cold set are maintained at a low-energy state to save energy consumption. Moreover, the fog nodes in

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36

Fu, Sibao, Fan Yang, and Ye Xiao. "AI Inspired Intelligent Resource Management in Future Wireless Network." IEEE Access 8 (2020): 22425–33. http://dx.doi.org/10.1109/access.2020.2968554.

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37

Kumar Nunna, H. S. V. S., and Suryanarayana Doolla. "Multiagent-Based Distributed-Energy-Resource Management for Intelligent Microgrids." IEEE Transactions on Industrial Electronics 60, no. 4 (2013): 1678–87. http://dx.doi.org/10.1109/tie.2012.2193857.

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38

Yu, Han, Xin Bai, and Dan C. Marinescu. "Workflow management and resource discovery for an intelligent grid." Parallel Computing 31, no. 7 (2005): 797–811. http://dx.doi.org/10.1016/j.parco.2005.04.009.

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39

Azarmi, N., and R. Smith. "Intelligent scheduling and planning systems for telecommunications resource management." BT Technology Journal 25, no. 3-4 (2007): 241–48. http://dx.doi.org/10.1007/s10550-007-0080-y.

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40

Sleem, Ahmed, and Ibrahim Elhenawy. "Intelligent Waste Management System for Recycling and Resource Optimization." Journal of Intelligent Systems and Internet of Things 1, no. 2 (2020): 102–8. http://dx.doi.org/10.54216/jisiot.010205.

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Анотація:

This paper proposes a deep learning-based intelligent waste management system that can accurately classify waste types and optimize waste disposal processes. The proposed system utilizes a convolutional model to concisely identify the waste type from images captured by a camera system. Our system uses intelligent data augmentation to perform large datasets of waste item images and achieves a high classification accuracy rate. The waste types are classified into several categories, including glass, cardboard, metal, plastic, paper, and trash. Experimental results show that our system achieves h

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41

Rzevski, George, Petr Skobelev, and Alexey Zhilyaev. "Emergent Intelligence in Smart Ecosystems: Conflicts Resolution by Reaching Consensus in Resource Management." Mathematics 10, no. 11 (2022): 1923. http://dx.doi.org/10.3390/math10111923.

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A new emergent intelligence approach to the design of smart ecosystems, based on the complexity science principles, is introduced and discussed. The smart ecosystem for resource management is defined as a system of autonomous decision-making multi-agent systems capable to allocate resources, plan orders for resources, and to optimize, coordinate, monitor, and control the execution of plans in real time. The emergent intelligence enables software agents to collectively resolve conflicts arising in resource management decisions by reaching a consensus through a process of detecting conflicts and

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42

Cui, Qing Quan, Jing Ning, and Xun He Yin. "Research on the Intelligent Management of Access Control System for Laboratory Center." Applied Mechanics and Materials 421 (September 2013): 562–67. http://dx.doi.org/10.4028/www.scientific.net/amm.421.562.

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Анотація:

Access control system applied in laboratory center makes laboratory management more conveniently. The laboratory security can be guaranteed, even the efficiency of laboratory management and human resource arrangement are also improved. How to use the hardware and software resources of access control system sufficiently, and conduct intelligent management combined with experiment courses, which need deeper research and actual intelligent management. In the paper, take the Beijing Experimental Teaching Center-Experimental Teaching Center of Electrical and Intelligent Engineering on Building as e

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43

Li, Shaobo, Chenglong Zhang, and Jinglei Qu. "Location Optimization of Wireless Sensor Network in Intelligent Workshop Based on the Three-Dimensional Adaptive Fruit Fly Optimization Algorithm." International Journal of Online Engineering (iJOE) 14, no. 11 (2018): 202. http://dx.doi.org/10.3991/ijoe.v14i11.9544.

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Анотація:

The production process of modern manufacturing industry is complex and changeable, manufacturing resources have extensive dynamic characteristics. For effectively managing and controlling manufacturing resources, realizing real-time location data collection of intelligent workshop, a manufacturing resource location sensing architecture based on the wireless sensor network is proposed. For en-suring real-time accuracy of manufacturing resource location data in the intelligent workshop, a three-dimensional adaptive fruit fly optimization algorithm is de-signed to estimate the location coordinate

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44

Balje, Duda. "HUMAN RESOURCE MANAGEMENT AND BUSINESS INTELLIGENCE." KNOWLEDGE - International Journal 60, no. 1 (2023): 101–6. http://dx.doi.org/10.35120/kij6001101b.

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Анотація:

This paper will present the results of the research of relevant literature and research conducted in the practice of small and medium-sized enterprises with the aim of examining the possibilities of applying business intelligence and the impact of human resource management on the improvement of business operations. Companies today, in the conditions of increasing competition, have a complex task of how to survive in the global market and maintain a competitive advantage. In their business, companies are faced with huge amounts of data that they get from internal and external sources and have a

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45

Bhole, Anju. "Cloud Resource Management and Cost Optimization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–7. https://doi.org/10.55041/ijsrem26028.

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Анотація:

Cloud computing has emerged as a pivotal innovation, facilitating businesses in obtaining scalable, adaptable, and economical IT resources. Nonetheless, the intricate nature of cloud infrastructure management may result in unpredictable expenses if resources are not utilized efficiently. As organizations progressively shift their operations to cloud platforms, there is an increasing demand for effective resource management and strategies aimed at cost optimization to avert issues such as over-provisioning, underutilization, and decline in performance. This paper investigates essential strategi

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46

Wu, Shumei. "Intelligent Communication Management Terminal in the Construction of Human Resource Management Mode." Wireless Communications and Mobile Computing 2021 (November 23, 2021): 1–14. http://dx.doi.org/10.1155/2021/7106104.

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Анотація:

With the rapid development of the economy, the integration of corporate strategic management and human resource management has become an issue of concern. This research mainly discusses the role of intelligent communication management terminal in the construction of human resource management mode. In this research, the system development process of this research mainly uses the class library in the software architecture layer to support the software development process. The main development language of Android, JAVA, is to install the Android Develop Tools plug-in on eclipse and install the An

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47

Savio, Rajesh Dominic, and Jafar M. Ali. "Artificial Intelligence in Project Management & Its Future." Saudi Journal of Engineering and Technology 8, no. 10 (2023): 244–48. http://dx.doi.org/10.36348/sjet.2023.v08i10.002.

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Анотація:

Artificial Intelligence (AI) has emerged as a disruptive force, transforming industries and revolutionizing business processes. Among the domains significantly impacted, project management holds immense potential for transformation. AI’s integration promises intelligent automation, data-driven decision-making, and predictive capabilities, addressing challenges in traditional project management methodologies. Successful AI implementations have revolutionized project management, improving forecasting, resource allocation, and risk assessment. Despite the benefits, challenges hinder AI adoption,

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48

Hu, Changming. "Typical Applications of IoT Cloud Technology in the HVAC Industry." Architecture Engineering and Science 5, no. 1 (2024): 24. http://dx.doi.org/10.32629/aes.v5i1.1841.

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Анотація:

(Objective) Use the Internet of Things and cloud data technology to build intelligent HVAC environment, form the whole ecosystem with cloud technology as the core, running through designers, builders and managers. The application of virtualization based on cloud technology can integrate environmental resources, provide virtual resources such as intelligent management and control and intelligent construction, and provide an intelligent cloud platform for the whole HVAC environment. With the help of big data technology, we can effectively integrate the information of various building structures

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49

Dotsenko, Nataliia, Dmytro Chumachenko, Yuliia Husieva, Nataliia Kosenko, and Igor Chumachenko. "Sustainable Management of Healthcare Settings’ Personnel Based on Intelligent Project-Oriented Approach for Post-War Development." Energies 15, no. 22 (2022): 8381. http://dx.doi.org/10.3390/en15228381.

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Анотація:

The paper considers the transformation of human resource management processes in the healthcare settings of Ukraine in the context of war and the COVID-19 pandemic. It is noted that the unstable and hostile environment of a healthcare setting during times of crisis leads to the need to change the personnel selection and team formation model to increase the adaptability and resilience of human resources involved in the provision of medical care. The key features of the human resource management process in a turbulent environment are the high migration activity of personnel, which leads to the n

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50

Nurwan, Nurwan, Abdul Hamid Habbe, and Abdul Wahab. "Overview of Human Resource Management in Islamic Economics." Dinasti International Journal of Economics, Finance & Accounting 2, no. 5 (2021): 479–88. http://dx.doi.org/10.38035/dijefa.v2i5.1051.

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Анотація:

Islamic economic human resources are said to be important because Islamic economic resources must have morals, skills and competencies. The purpose of this study is to review how the concept of human resource management in Islam is.The data collection method in the article is a literature study with descriptive analysis. The results of the study stated thatHuman resources in Islam are those who have the noble qualities of the Prophet SAW, namely Siddiq (true and honest), Amanah (honest/trustful, responsible), Fathanah (intelligent) and Tabligh (transparent). These qualities will produce human

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