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Добірка наукової літератури з теми "Intelligent Resource Management"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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Статті в журналах з теми "Intelligent Resource Management"

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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Анотація:

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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Анотація:

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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Дисертації з теми "Intelligent Resource Management"

1

Lei, Lin. "Intelligent human resource management system." Thesis, University of Macau, 2005. http://umaclib3.umac.mo/record=b1445897.

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2

Capela, Nelson Filipe. "Intelligent and transparent resource management." Doctoral thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/22720.

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Анотація:

Doutoramento em Engenharia Eletrotécnica<br>Wireless access networks have become available almost everywhere. In the same area we can have access to several networks of the same or di erent technologies that can present di erent characteristics. Alongside the evolution of access networks, we have the evolution of terminals. These are currently equipped with a multitude of wireless interfaces, easier to carry and more accessible to users. In uenced by these features, users began to change the way they use their devices to obtain information. The introduction of these new terminals in env

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3

Kochman, Robert W. (Robert Walter) 1979. "Decision theoretic resource management for intelligent environments." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29673.

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Анотація:

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.<br>Includes bibliographical references (p. 51-52).<br>Intelligent environments are becoming more complex, involving greater numbers of more sophisticated devices and agents. As complexity increases, it becomes difficult to build agents that effectively use the resources in their space. Agents may not know which resources are present in a given space, and they are almost certainly do not know which resources are inoperative or being used by other agents. To address this problem, t

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4

Zhao, Qiyang. "Intelligent radio resource management for mobile broadband networks." Thesis, University of York, 2013. http://etheses.whiterose.ac.uk/5518/.

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Анотація:

This thesis studies intelligent spectrum and topology management through transfer learning in mobile broadband networks, to improve the capacity density and Quality of Service (QoS) as well as to reduce the cooperation overhead and energy consumption. The dense deployment of small cell base stations (BSs) is an effective approach to provide high capacity density access. In the meantime, multi-hop wireless backhaul networks enable highly flexible deployment and self-organization of small cell BSs. A heterogeneous small cell access and multi-hop backhaul network is studied in this thesis as mobi

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5

Stein, Oliver. "Intelligent Resource Management for Large-scale Data Stream Processing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-391927.

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Анотація:

With the increasing trend of using cloud computing resources, the efficient utilization of these resources becomes more and more important. Working with data stream processing is a paradigm gaining in popularity, with tools such as Apache Spark Streaming or Kafka widely available, and companies are shifting towards real-time monitoring of data such as sensor networks, financial data or anomaly detection. However, it is difficult for users to efficiently make use of cloud computing resources and studies show that a lot of energy and compute hardware is wasted. We propose an approach to optimizi

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6

Gajos, Krzysztof Z. (Krzysztof Zygmunt) 1976. "A knowledge-based resource management system for the intelligent room." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86665.

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7

Joorabchi, Amirhassan. "Intelligent Predictive Models for Water resources Engineering." Thesis, Griffith University, 2009. http://hdl.handle.net/10072/367450.

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Анотація:

Fresh water is considered to be one of the most important resources for humans and the environment. Due to the increase in population and the currently unsustainable usage of this limited resource, more attention is needed in the management of water resources. Advanced computational methods can help in attaining a better understanding of all aspects of water. Indeed, a better understanding of water resources requires a vast knowledge of a wide variety of fields such as atmospheric science, geology, hydrology, hydraulics and mathematics etc. To assist in this process computing techniques have b

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8

Obeso, Duque Aleksandra. "Performance Prediction for Enabling Intelligent Resource Management on Big Data Processing Workflows." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-372178.

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Анотація:

Mobile cloud computing offers an augmented infrastructure that allows resource-constrained devices to use remote computational resources as an enabler for highly intensive computation, thus improving end users experience. Being able to efficiently manage cloud elasticity represents a big challenge for dynamic resource scaling on-demand. In this sense, the development of intelligent tools that could ease the understanding of the behavior of a highly dynamic system and to detect resource bottlenecks given certain service level constrains represents an interesting case of study. In this project,

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9

Anthony, Anish. "A comparison of agent paradigms for resource management in distributed sensor networks." Birmingham, Ala. : University of Alabama at Birmingham, 2007. https://www.mhsl.uab.edu/dt/2008r/anthony.pdf.

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Анотація:

Thesis (Ph. D.)--University of Alabama at Birmingham, 2007.<br>Additional advisors: Dale W. Callahan, B. Earl Wells, Gary J. Grimes, Roy P. Koomullil. Description based on contents viewed June 10, 2008; title from title screen. Includes bibliographical references (p. 124-129).

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10

Gaeddert, Joseph Daniel. "Facilitating Wireless Communications through Intelligent Resource Management on Software-Defined Radios in Dynamic Spectrum Environments." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77061.

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This dissertation provides theory and analysis on the impact resource management has on software-defined radio platforms by investigating the inherent trade-off between spectrum and processing effciencies with their relation to both the power consumed by the host processor and the complexity of the algorithm which it can support. The analysis demonstrates that considerable resource savings can be gained without compromising the resulting quality of service to the user, concentrating specifically on physical-layer signal processing elements commonly found in software definitions of single- and

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Книги з теми "Intelligent Resource Management"

1

Peng, Haixia, Qiang Ye, and Xuemin Sherman Shen. Intelligent Resource Management in Vehicular Networks. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96507-5.

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2

Ye, Qiang, and Weihua Zhuang. Intelligent Resource Management for Network Slicing in 5G and Beyond. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88666-0.

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3

Singh, Deepmala, Anurag Singh, Amizan Omar, and SB Goyal. Business Intelligence and Human Resource Management. Productivity Press, 2022. http://dx.doi.org/10.4324/9781003184928.

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4

Agrawal, Vidhi. Human Resource Management and Artificial Intelligence. Routledge, 2025. https://doi.org/10.4324/9781003474265.

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5

1935-, Lasker G. E., International Institute for Advanced Studies in Systems Research and Cybernetics., and International Conference on Systems Research, Informatics, and Cybernetics. (10th : 1998 : Baden-Baden, Germany), eds. Advances in support systems research: Innovative management systems methodologies and decision support tools, support systems in cyberspace, modeling the effects of resource constraints on economic growth, evaluating transnational decision support systems, corporate management of risk, anticipation in the enterprise, predicative stabilization & control policies, marketing information systems (MKIS), marketing support systems (MKSS), business reengineering: process redesign and product innovation, intelligent management of change, sustainable development of regional entrepreneurship, information resources management. International Institute for Advanced Studies in Systems Research and Cybernetics, 1998.

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6

Elkins, Dan. Managing intelligence resources. DWE Press, 2004.

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7

Zhang, Haixia, Dongyang Li, and Tong Xue. Intelligent Resources Management for Vehicular Social Networks. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80169-3.

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8

Singh, Gurinder, Hardeep Singh Dhanny, Vikas Garg, and Silky Sharma. Financial Intelligence in Human Resources Management. Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003083870.

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9

Ghosh, Soumi, Soumi Majumder, and Santosh Kumar Das. Artificial Intelligence Techniques in Human Resource Management. Apple Academic Press, 2023. http://dx.doi.org/10.1201/9781003328346.

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10

Tursunbayeva, Aizhan. Augmenting Human Resource Management with Artificial Intelligence. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-75266-7.

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Частини книг з теми "Intelligent Resource Management"

1

Feng, Zhiyong, Qixun Zhang, and Ping Zhang. "Intelligent Resource Management." In SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15768-9_5.

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2

Yao, Haipeng, Chunxiao Jiang, and Yi Qian. "Intelligent Network Resource Management." In Wireless Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15028-0_5.

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3

Virtanen, Petri, and Jari Stenvall. "Leadership and Human Resource Management." In Intelligent Health Policy. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69596-9_6.

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4

Hengxi, Zhang, Li Chunlin, Shi Zhengjun, and Zhang Xiaoqing. "Resource Pool-Oriented Resource Management for Cloud Computing." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27966-9_110.

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5

Tagirova, K. F., I. I. Zinatullin, and A. M. Vulfin. "Intelligent Virtual Machine Resource Management System." In Lecture Notes in Electrical Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71119-1_76.

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6

Gąsior, Dariusz. "Resource Management for SD-WANs." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30440-9_28.

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7

Yun, David Y. Y. "Intelligent Resource Management Through the Constrained Resource Planning Model." In Future Directions for Intelligent Systems and Information Sciences. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1856-7_18.

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8

Liu, Min, Ling Li, and Feng Yan. "IoT-Based Perception Resource Management Framework and Model." In Intelligent Predictive Maintenance. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2677-6_4.

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Mocanu, Mariana, Marian Muste, Vasile Lungu, and Radu Drobot. "Composite Application for Water Resource Management." In Advances in Intelligent Systems and Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32548-9_21.

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10

Niewiadomska-Szynkiewicz, Ewa, and Piotr Arabas. "Resource Management System for HPC Computing." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77179-3_5.

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Тези доповідей конференцій з теми "Intelligent Resource Management"

1

Atimati, Ehinomen, David Crawford, and Robert Stewart. "Intelligent Joint Resource Management of Shared Spectrum HetNets." In 2024 IEEE Future Networks World Forum (FNWF). IEEE, 2024. https://doi.org/10.1109/fnwf63303.2024.11028798.

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2

Song, Xuan, Xiaomei Li, and Mei Tang. "Intelligent Information Management and Resource Allocation Optimization Model Based on Artificial Intelligence." In 2025 International Conference on Electrical Drives, Power Electronics & Engineering (EDPEE). IEEE, 2025. https://doi.org/10.1109/edpee65754.2025.00252.

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3

Zhang, Qingwei, Caixing Shao, Yang Yang, and Ruiqi Zhang. "Efficient Resource Management in IoV through Task Offloading Optimization." In 2024 4th International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM). IEEE, 2024. https://doi.org/10.1109/aiim64537.2024.10934438.

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4

Wu, Guilong, Xiaofang Feng, Zhimin Yang, Yu Huang, Yitong Chen, and Jingwei Yang. "Intelligent Optical Cable Resource Management Based on ACO-RF Method." In 2025 8th International Conference on Artificial Intelligence and Big Data (ICAIBD). IEEE, 2025. https://doi.org/10.1109/icaibd64986.2025.11081966.

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5

Benabou, Adil, Fatima Touhami, and Lamiae Demraoui. "Artificial Intelligence and the Future of Human Resource Management." In 2024 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2024. http://dx.doi.org/10.1109/iscv60512.2024.10620146.

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6

Mushtaq, Muhammad Irfan, Omer Chughtai, and Muhammad Naeem. "Intelligent Priority-Driven Resource Allocation for Efficient Management in Heterogeneous Networks." In 2024 International Conference on Frontiers of Information Technology (FIT). IEEE, 2024. https://doi.org/10.1109/fit63703.2024.10838431.

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7

Elwakil, Mohamed, Mohamed A. Sobh, Samy El-Hennawey, and Ayman M. Bahaa-Eldin. "Latency Enhancement Using Efficient Resource Management Scheme Load Balancing." In 2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). IEEE, 2024. https://doi.org/10.1109/miucc62295.2024.10783493.

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8

Bi, Xian. "Research on Human Resource Management System Based on Genetic Algorithm." In 2024 Asian Conference on Intelligent Technologies (ACOIT). IEEE, 2024. https://doi.org/10.1109/acoit62457.2024.10941127.

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Jia, Binyan. "Thinking on the digital transformation of human resource management." In 2024 9th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). IEEE, 2024. https://doi.org/10.1109/iciibms62405.2024.10792855.

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10

Zhang, Zihao, Haixia Peng, Zhou Su, Tom H. Luan, and Nan Cheng. "Priority-Oriented Intelligent Resource Management in Space-Air-Ground Integrated IoT Networks." In GLOBECOM 2024 - 2024 IEEE Global Communications Conference. IEEE, 2024. https://doi.org/10.1109/globecom52923.2024.10901678.

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Звіти організацій з теми "Intelligent Resource Management"

1

Tabassi, Elham. AI Risk Management Framework. National Institute of Standards and Technology, 2023. http://dx.doi.org/10.6028/nist.ai.100-1.

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As directed by the National Artificial Intelligence Initiative Act of 2020 (P.L. 116-283), the goal of the AI RMF is to offer a resource to the organizations designing, developing, deploying, or using AI systems to help manage the many risks of AI and promote trustworthy and responsible development and use of AI systems. The Framework is intended to be voluntary, rights-preserving, non-sector specific, and use-case agnostic, providing flexibility to organizations of all sizes and in all sectors and throughout society to implement the approaches in the Framework. The AI RMF is intended to be pr

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2

Williams, Penny, Tyge Kummer, Mazlan Maskor, and Catherine Kennon. The State of AI in Australian Human Resources. Australian Human Resource Institute, 2024. https://doi.org/10.5204/rep.eprints.254106.

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Alongside work of all forms, the human resources profession is undergoing a transformation fuelled by rapid developments in artificial intelligence (AI) and the automation of HR and Management processes. This report presents findings from research undertaken as a collaboration between Queensland University of Technology (QUT) and the Australian Human Resources Institute (AHRI), to understand how these transformations are shaping the experiences of HR professionals in Australia, including their concerns and perspectives on the use of Generative AI in the workplace.

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3

Brian, Dominikus. Next-Generation Smart-City Energy and Resource Management Powered by Exascale Green Computing Infrastructure and Artificial Intelligence System. ResearchHub Technologies, Inc., 2024. http://dx.doi.org/10.55277/researchhub.rv1s6td0.

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Spirin, Oleg, and Mariia Shyshkina. Artificial Intelligence in Education and Educational Research: Challenges, Risks, and Prospects for Integration. Institute for Digitalisation of Education of the NAES of Ukraіne, 2025. https://doi.org/10.33407/lib.naes.id/eprint/745119.

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Artificial intelligence (AI) is playing an increasingly important role in education, contributing to the personalization of learning, the automation of administrative processes and educational research. The use of AI allows for increased learning efficiency, improved management of educational resources and improved analytics of educational data. However, the integration of AI into education is accompanied by challenges related, in particular, to technical limitations, pedagogical risks, ethical aspects and security issues. Future research should focus on the integration of AI and augmented and

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Rihm, Alfredo, Carolina Piamonte, Eduardo Antonio Restrepo Lagos, Magda Correal, and Paula Gabriela Guerra Morán. Digital Transformation of Solid Waste Management: Waste Collection Innovation, Business Intelligence, and Digital Technologies to Transition Waste Management Towards Circularity in Latin America and the Caribbean. Edited by Claudia M. Pasquetti. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013169.

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If you are interested in technology and innovation, and have been wondering what are the new trends in technological and digital innovation in the solid waste sector in Latin America and the Caribbean, this publication is for you! The transition to the circular economy, climate action, the fourth industrial revolution bring new challenges to operators in the sector. Key challenges highlighted include the need for robust data and the digitization of waste management systems to meet the objectives of the circular economy. The text details the efforts of organizations such as the IDB to develop d

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Bigl, Matthew, Caitlin Callaghan,, Jacqueline Willan, Paulina Lintsai, and Jamie Potter. Energy Atlas—mapping energy-related data for DoD lands : Phase 3—data and portal expansion : Northeast CONUS. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47823.

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The DoD is a significant land user in northeast United States overseeing approximately 375 k acres of land with a total value of $113 B. The Department of Energy has found that major impacts from climate change will threaten energy infrastructure in the northeast US moving into the future. Current spatial information related to the energy resources and infrastructure on and adjacent to DoD installations can play a vital role in decision-making for sustainable and resilient installation planning in the region. The Energy Atlas (EA) portal provides a secure value-added resource to inform the dec

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Latorre, Lucia, Valeria Lovaisa, Lorenzo De Leo, Alexander Riobó, Eduardo Riego, and Mariana Gutierrez. Tech Report: Edge Computing. Inter-American Development Bank, 2024. https://doi.org/10.18235/0013021.

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The adoption of edge computing in Latin America and the Caribbean (LAC) underscores its ability to address the regions unique challenges, including limited connectivity and the need to process data in remote locations. Innovative projects in smart public lighting, urban security, and sustainable resource management highlight the transformative impact of edge computing in improving quality of life and operational efficiency. Looking ahead, edge computing stands at the forefront of technological innovation. Deeper integration with artificial intelligence and the rise of 5G will unlock fresh oppo

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Baxter, W., Amanda Barker, Samuel Beal, et al. A comprehensive approach to data collection, management, and visualization for terrain characterization in cold regions. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48212.

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As global focus shifts to northern latitudes for their enhanced access to newly viable resources, US Army operational readiness in these extreme environments is increasingly important. Rapid and accurate intelligence on the conditions influencing operations in these regions is essential to mission success and warfighter safety. Arctic and boreal environments are highly heterogeneous, including changing extents of frozen versus thawing ground, snow, and ice that affect ground trafficability and visibility, terrain physics, and physicochemical properties of water and soil. Furthermore, projected

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Doo, Johnny. Beyond Aviation: Embedded Gaming, Artificial Intelligence, Training, and Recruitment for the Advanced Air Mobility Industry. SAE International, 2024. https://doi.org/10.4271/epr2024028.

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<div class="section abstract"><div class="htmlview paragraph">Recent advancements in electric vertical take-off and landing (eVTOL) aircraft and the broader advanced air mobility (AAM) movement have generated significant interest within and beyond the traditional aviation industry. Many new applications have been identified and are under development, with considerable potential for market growth and exciting potential. However, talent resources are the most critical parameters to make or break the AAM vision, and significantly more talent is needed than the traditional aviation ind

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Semerikov, Serhiy, Viacheslav Osadchyi, and Olena Kuzminska. Proceedings of the 1st Symposium on Advances in Educational Technology - Volume 2: AET. SciTePress, 2022. http://dx.doi.org/10.31812/123456789/7011.

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Symposium on Advances in Educational Technology (AET) is a peer-reviewed international conference focusing on research advances and applications of combined use of computer hardware, software, and educational theory and practice to facilitate learning. Today, AET is the premier interdisciplinary forum for learning scientists, academicians, researchers, professionals, policymakers, postgraduate students, and practitioners to present their latest research results, ideas, developments, and applications. AET topics of interest are: • Artificial intelligence in education • Augmented reality in educ

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