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Lightweight Artificial Intelligence Technology for Health Diagnosis of Agriculture Vehicles: Parallel Evolving Artificial Neural Networks by Genetic Algorithm

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Abstract

This paper focuses on developing a computationally economic lightweight artificial intelligence (AI) technology for smartphones. Until date, no commercial system is available on this technology. Thus the developed breakthrough technology can enhance the capability of users on the field for monitoring the agricultural vehicles (AgV)s health by analyzing the acoustic noise using smartphone‘s app. This paper can enable the user of AgVs to optimize their farming by management at edge devices: smartphones. Since smartphones use a small integrated computing unit with computational limited resources, thus lighter the system, more favorable to work on. Artificial neural network (ANN) is one of the most favorite AI techniques, but its lightweight architecture—attributed by the number of inputs and hidden layers and neurons—, is one of the most important issues in the context of smartphones. Under the framework of bi-level optimization, we aim to analyze the tournament selection operator based genetic algorithm with hybrid crossover operators, at level-I, to evolve ANN, at level-II to design the lightweight edge device enabled AI technique. The obtained results and numerical evaluative analysis indicate that the optimized design of the lightweight fault detection system responds well to the soundtracks received via microphones. We present the evaluation of the proposed technology on serial programming, parallel programming (PP), GPU programming, and GPU with PP. The results show that PP is enough efficient for the proposed technology and can save the cost of GPU for large scale implementation of the technology.

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Correspondence to Rubén González Crespo.

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Gupta, N., Khosravy, M., Gupta, S. et al. Lightweight Artificial Intelligence Technology for Health Diagnosis of Agriculture Vehicles: Parallel Evolving Artificial Neural Networks by Genetic Algorithm. Int J Parallel Prog 50, 1–26 (2022). https://doi.org/10.1007/s10766-020-00671-1

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