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Evolutionary Machine Learning Powered by Genetics Algorithm for IoT-Specific Health Monitoring of Agriculture Vehicles

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Frontiers in Nature-Inspired Industrial Optimization

Abstract

This chapter illustrates the role of evolutionary optimization in designing AI end-devices to monitor the efficiency of agriculture vehicles (AgVs) mainly on the field via economic sound-based IoT sensors. Due to the application of AI on end-devices, there is a certain limitation for memory and complexity of the deployed algorithms. In such a condition, a machine learning model with optimal structure is of favorite. Lightweight is an aspect of the model as its model structure is optimized for operation of minimum complexity but an acceptable efficiency. The chapter explains that how this target can be achieved by the deployment of metaheuristic evolutionary optimizers. The AI model with optimum complexity and structure is suitable especially for deployment on smartphones. The optimization assists the designer in achieving not only the lightweight structure but also a maximized efficiency for recognition via built-in economic sensors such as smartphone microphones.

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Gupta, N., Gupta, S., Patel, N. (2022). Evolutionary Machine Learning Powered by Genetics Algorithm for IoT-Specific Health Monitoring of Agriculture Vehicles. In: Khosravy, M., Gupta, N., Patel, N. (eds) Frontiers in Nature-Inspired Industrial Optimization. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-3128-3_12

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