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981
Federated Synergy: Hierarchical Multi-Agent Learning for Sustainable Edge Computing in IIoT
Published 2025-01-01“…The Industrial Internet of Things (IIoT) presents significant challenges in task offloading, resource allocation, and energy efficiency, necessitating intelligent, scalable, and adaptive solutions. …”
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982
Toward 6G: Latency-Optimized MEC Systems with UAV and RIS Integration
Published 2025-03-01“…This paper addresses the critical challenge of task offloading and resource allocation in an MEC-based system, where a massive MIMO base station, serving multiple macro-cells, hosts the MEC server with support from a UAV-equipped RIS. …”
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983
Fairness-Aware Computation Offloading for Mobile Edge Computing With Energy Harvesting
Published 2025-01-01“…Comprehensive simulations demonstrate that the method effectively balances fairness, throughput, and energy use, making it a workable way to improve resource allocation in MEC systems.…”
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984
Research on Machine Learning-Based Extraction and Classification of Crop Planting Information in Arid Irrigated Areas Using Sentinel-1 and Sentinel-2 Time-Series Data
Published 2025-05-01“…The newly developed framework exhibits exceptional precision in categorization while maintaining impressive adaptability, offering crucial insights for optimizing agricultural operations and sustainable resource allocation in irrigation-dependent arid zones.…”
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985
Energy- and Spectral-Efficiency Trade-Off in OFDMA-Based Cooperative Cognitive Radio Networks
Published 2014-02-01“…Optimum transmit power of user is got by analysis; validity of theory is verified by simulation, and the proposed algorithm can adaptively allocate resource for CCRN.…”
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986
Lossless Hyperspectral Image Compression in Comet Interceptor and Hera Missions with Restricted Bandwith
Published 2025-03-01“…This study compares two wavelet-based image compression algorithms, CCSDS 122.0 and JPEG 2000, used in the European Space Agency Comet Interceptor and Hera missions, respectively, in varying scenarios. …”
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987
LEO computing satellite constellation design for heterogeneous QoS requirements
Published 2025-03-01“…A two-stage constellation design algorithm was proposed, which firstly optimized the resource allocation strategy of computational satellites and then determined the constellation parameters under optimal QoS conditions. …”
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988
Deep Reinforcement Learning-Based Energy Management Strategy for Green Ships Considering Photovoltaic Uncertainty
Published 2025-03-01“…The numerical results demonstrate that, compared to those obtained with the Double DQN algorithm, the PPO algorithm, and the DDPG algorithm without considering the PV system, the proposed DDPG algorithm reduces the total economic cost by 1.36%, 0.96%, and 4.42%, while effectively allocating power between the hydrogen fuel cell and the lithium battery and considering the uncertainty of on-board PV generation. …”
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989
Grouping control of electric vehicles based on improved golden eagle optimization for peaking
Published 2025-04-01Get full text
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990
Optimizing role assignment for scaling innovations through AI in agricultural frameworks: An effective approach
Published 2025-06-01“…By leveraging advanced algorithms and machine learning techniques, the research aims to streamline the allocation of tasks and responsibilities among various stakeholders, including farmers, agronomists, technicians, and AI systems. …”
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991
Optimization of emergency logistics for urban flooding with consideration of rainfall effects
Published 2025-08-01“…Second, a vehicle routing model accounting for rainfall and dynamic vehicle speeds is established, solved using a hybrid Genetic Simulated Annealing algorithm to enhance computational efficiency. Ultimately, through simulation with randomly generated calculation examples, it was found that for the supply distribution model, the allocation model that takes into account both the resource scarcity index and the disaster index is more suitable for scenarios with an uneven distribution of disaster severity. …”
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992
Mechanism for green unicast routing protection in multi-granularity transport networks
Published 2014-10-01“…The mechanism for green unicast routing protection is designed with the constraint of quality of service (QoS),energy consumption and the survivability of single link or node failure considered.The path is calculated under the constraint of QoS based on K-shortest path algorithm.The resource allocation is done on multi-layer auxiliary graph considering the minimum times of wavelength conversion.Three protection levels are provided according to the service demands.The mechanism is implemented over EON (Europe optical network) topology by simulation.Performance evaluation has been done on the blocking probability,the protecting/working resource ratio and the load balance degree by comparing with certain existent mechanism.It has been shown that the proposed mechanism is both feasible and effective.…”
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993
Mechanism for green unicast routing protection in multi-granularity transport networks
Published 2014-10-01“…The mechanism for green unicast routing protection is designed with the constraint of quality of service (QoS),energy consumption and the survivability of single link or node failure considered.The path is calculated under the constraint of QoS based on K-shortest path algorithm.The resource allocation is done on multi-layer auxiliary graph considering the minimum times of wavelength conversion.Three protection levels are provided according to the service demands.The mechanism is implemented over EON (Europe optical network) topology by simulation.Performance evaluation has been done on the blocking probability,the protecting/working resource ratio and the load balance degree by comparing with certain existent mechanism.It has been shown that the proposed mechanism is both feasible and effective.…”
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994
Research on deep reinforcement learning in Internet of vehicles edge computing based on Quasi-Newton method
Published 2024-05-01“…To address the issues of ineffective task offloading decisions caused by multitasking and resource constraints in vehicular networks, the Quasi-Newton method deep reinforcement learning dual-phase online offloading (QNRLO) algorithm was proposed. …”
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995
Leveraging AI for early cholera detection and response: transforming public health surveillance in Nigeria
Published 2025-02-01“…By integrating AI into Nigeria’s public health infrastructure, early detection and response can be improved, resource allocation optimized, and disease transmission minimized. …”
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996
Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model.
Published 2025-01-01“…This would ensure optimal patient care and resource allocation and help initiate adequate patient treatment after being transferred from the recovery room to the ward. …”
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997
Multihop cost awareness task migration with networking load balance technology for vehicular edge computing
Published 2025-08-01“…This paper investigates dynamic resource allocation and task migration mechanisms for cooperative vehicular edge computing (VEC) servers to expand computing capabilities of local server. …”
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998
Predicting Intensive Care Unit Admissions in COVID-19 Patients: An AI-Powered Machine Learning Model
Published 2025-01-01“…Intensive Care Units (ICUs) have been in great demand worldwide since the COVID-19 pandemic, necessitating organized allocation. The spike in critical care patients has overloaded ICUs, which along with prolonged hospitalizations, has increased workload for medical personnel and lead to a significant shortage of resources. …”
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999
Robustness Maximization of Parallel Multichannel Systems
Published 2012-01-01“…Simulation results confirm the asymptotic convergence of both resource allocation policies. In nonasymptotic regime the resource allocation policies can be interchanged depending on the robustness measure and on the operating point of the communication system. …”
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1000
A Machine Learning Approach for Quantifying Academic Misconduct
Published 2024-12-01“…To deal with this problem effec tively, a clear understanding of its magnitude is necessary for planning and resource allocation. This paper proposes a machine learning algorithm to quantify the mag nitude of academic misconduct among undergraduate students. …”
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