首页|Lanzhou University Reports Findings in Artificial Intelligence (Embedded monitor ing system and teaching of artificial intelligence online drug component recogni tion)
Lanzhou University Reports Findings in Artificial Intelligence (Embedded monitor ing system and teaching of artificial intelligence online drug component recogni tion)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting out of Lanzhou, People 's Republic of China, by NewsRx editors, research stated, "Drug testing has many test elements. It aims to prevent unqualified drugs from entering the market an d ensure drug safety." Our news journalists obtained a quote from the research from Lanzhou University, "The existing artificial intelligence (AI) online monitoring system identifies active ingredients in the process of use. Owing to their openness, data are easy to be lost, failing to meet user needs and inducing a specific impact on the us e of the monitoring system. With the continuous development of computer and meas urement technologies, various biochemical data are increasing at an unprecedente d speed, and numerous databases are emerging. Extracting patterns from considera ble known data and experimental facts is an essential task for a wide range of b iological and chemical workers. Pattern recognition is one of the essential tech nologies for data mining. It is widely used in industry, agriculture, national d efense, biomedicine, meteorology, astronomy, and other fields. To improve the ef fect of the online drug ingredient recognition system, this study used AI to des ign an online drug ingredient recognition-embedded monitoring system and applied AI to the teaching field to improve teaching efficiency. First, this study cons tructed the framework of the AI online drug ingredient recognition-embedded moni toring system and introduced the process of online drug ingredient recognition. Then, it introduced the pattern recognition method, constructed the pattern reco gnition system, and presented the pattern recognition algorithm and the algorith m evaluation index. Afterward, it used pattern recognition to conduct a qualitat ive analysis of the infrared spectrum of drug components and introduced the over all process of the qualitative analysis. In addition, this study employed AI to implement changes to the embedded system instruction in colleges and universitie s, summarizing the current issues. The impact of drug component recognition and the educational impact of embedded systems were investigated in the experimental portion. The experimental findings demonstrated the excellent accuracy, sensiti vity, specificity, and Matthew correlation coefficient of the online drug compon ent recognition-integrated monitoring system in this work. Compared with that of other systems, its average drug component recognition accuracy was above 0.85."
LanzhouPeople's Republic of ChinaAsi aArtificial IntelligenceDrugs and TherapiesEmbedded SystemsEmerging Tech nologiesMachine LearningTechnology
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