Research of Criteria for Assessing the Quality of Supervised Classification of Aerospace Images Based on a Nonlinear Scheme of Compromises

Puleko, I.; Chumakevych, Viktor; Ptashnyk, V.; Osadchuk, R.; Ischenko, I.

doi:10.3997/2214-4609.2022580043

Cited by 1 publication

(1 citation statement)

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“…Furthermore, high precision and recall do not coincide in a real scenario. The F1 score measures the balance of precision and recall, and, accordingly, it is a good candidate for a formal classifier quality evaluation metric [64]. In developing an AI model for cough recognition, it is important to consider all the metrics and not use them individually to be able to capture the strengths and weaknesses of the model.…”

Section: Specificitymentioning

confidence: 99%

Artificial Intelligence for Automatic Monitoring of Respiratory Health Conditions in Smart Swine Farming

Lagua

Mun

Ampode

et al. 2023

Animals

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Porcine respiratory disease complex is an economically important disease in the swine industry. Early detection of the disease is crucial for immediate response to the disease at the farm level to prevent and minimize the potential damage that it may cause. In this paper, recent studies on the application of artificial intelligence (AI) in the early detection and monitoring of respiratory disease in swine have been reviewed. Most of the studies used coughing sounds as a feature of respiratory disease. The performance of different models and the methodologies used for cough recognition using AI were reviewed and compared. An AI technology available in the market was also reviewed. The device uses audio technology that can monitor and evaluate the herd’s respiratory health status through cough-sound recognition and quantification. The device also has temperature and humidity sensors to monitor environmental conditions. It has an alarm system based on variations in coughing patterns and abrupt temperature changes. However, some limitations of the existing technology were identified. Substantial effort must be exerted to surmount the limitations to have a smarter AI technology for monitoring respiratory health status in swine.

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Section: Specificitymentioning

confidence: 99%