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• W4385993840 abstract "In this research, resource allocation in machine learning is used to analyze how cloud computing is being applied in smart agriculture. This chapter goes over the advantages of cloud computing for farming and how machine learning can enhance resource allocation for higher agricultural yields and less negative environmental impact. The chapter also examines implementation difficulties for cloud-based agricultural solutions and speculates on potential fixes. Insights for researchers and practitioners in the area are provided by the research, which demonstrates the potential for merging cloud computing and machine learning in smart agriculture to increase productivity and sustainability. The research also assessed the efficacy of the ML-based techniques using a variety of performance indicators, including reaction time and throughput. The management of cloud workloads has shown considerable promise when using machine learning-based methods. This chapter offers a thorough overview of current developments in ML-based cloud workload management and identifies areas for further research." @default.
• W4385993840 created "2023-08-19" @default.
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• W4385993840 date "2023-11-24" @default.
• W4385993840 modified "2023-10-14" @default.
• W4385993840 title "Smart Agriculture Resource Allocation and Cost Optimization Using ML in Cloud Computing Environment" @default.
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• W4385993840 doi "https://doi.org/10.4018/979-8-3693-0200-2.ch008" @default.
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