Abstract
In recent years, with the large and disorderly widespread use of antibiotics by humans, a large number of drug-resistant strains have appeared and spread widely, and they have been increasing year by year. Their resistance to one drug is often characterized by multiple resistance to one drug, which results in nosocomial infections. Bringing extremely adverse effects, rapid and accurate pathogen identification and drug resistance monitoring are of great significance for the effective control of nosocomial infections and the prevention of the spread of drug-resistant strains. The purpose of this article is to study microbial detection technology based on intelligent optimization algorithms. This article analyzes the basic theoretical knowledge of holographic microscopy principles, holographic imaging optical paths and intelligent optimization algorithms, and introduces intelligent optimization algorithms and embedded related principles and technologies. Then the hardware platform, software architecture and intelligent optimization algorithm of the system are designed. In this paper, a digital holographic microscope is used to collect pathogen microbial images, and intelligent optimization algorithms are introduced to perform classification calculations to achieve classification and detection of pathogen microbial holographic image data sets. Experimental research shows that when the number of hidden layers of the intelligent optimization algorithm is 2, and the neural network structure with 10 neurons in each hidden layer has the smallest error in the estimation of the number of microorganisms.
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Acknowledgements
In 2021, Qiqihar science and Technology Bureau, innovation incentive project Project Name: Study on the application effect of molecular biology technology in clinical pathogenic microorganism detection Project NO. CSFGG-2021335.
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Liu, Y., Sun, H., Dong, X. (2022). Microbial Detection Technology Based on Intelligent Optimization Algorithm. In: Sugumaran, V., Sreedevi, A.G., Xu , Z. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. ICMMIA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 138. Springer, Cham. https://doi.org/10.1007/978-3-031-05484-6_7
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DOI: https://doi.org/10.1007/978-3-031-05484-6_7
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