摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-人工智能的新研究是一篇报道的主题。据《新闻周刊》编辑从兰州传出的新闻报道,研究称:“药品检测有许多检测要素,旨在防止不合格药品进入市场,确保药品安全。”记者从兰州大学的研究中得到一句话:“现有的人工智能(AI)在线监测系统在使用过程中识别活性成分,由于其开放性,数据容易丢失,不能满足用户的需要,对监测系统的美国产生了特殊的影响。随着计算机和测量技术的不断发展,目前的在线监测系统在使用过程中识别活性成分。”各种生物化学数据以前所未有的速度增长,大量的数据库不断涌现,从大量已知数据和实验事实中提取模式是生物化学工作者的一项重要任务,模式识别是数据挖掘的重要技术之一,广泛应用于工业、农业、国防、生物医学、气象、天文等领域。为了提高在线药物成分识别系统的效果，本研究利用人工智能设计了一个在线药物成分识别嵌入式监控系统，并将人工智能应用于教学领域，以提高教学效率。摘要:介绍了模式识别方法,构建了模式识别系统,给出了模式识别算法和算法评价指标,并利用模式识别技术对药物成分的红外光谱进行了定性分析,介绍了定性分析的全过程。摘要:本研究运用人工智能技术对高校嵌入式系统教学进行了改革,总结了当前存在的问题。实验部分考察了药物成分识别的影响以及嵌入式系统对教育的影响。实验结果表明,该方法具有良好的准确性、敏感性、特异性和实用性。本文所建立的药物成分识别-综合监测系统的平均识别准确率在0.85.以上。
Abstract
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."
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