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相关概念视频

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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通过使用基于机器学习的方法和物联网的情绪分析来增强电子商务决策.

Yasser Filahi1, Omer Melih Gul2,3, Ali Elghirani4

  • 1Department of Artificial Intelligence Engineering, Bahcesehir University, Istanbul, Turkey.

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概括

这项研究使用物联网和机器学习 (ML) 来增强电子商务. AdaBoosting ML模型显著提高了准确性和预测,从而提高了客户满意度和适应性零售策略.

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科学领域:

  • 电子商务技术 电子商务技术
  • 应用机器学习应用机器学习
  • 物联网 (IoT) 的整合.

背景情况:

  • 电子商务对于全球贸易和消费者方便至关重要.
  • 整合物联网和机器智能可以显著改善电子商务运营和决策.
  • 通过分析客户数据以获得个性化体验和运营效率,可以增强当前的电子商务策略.

研究的目的:

  • 通过先进的数据分析提升在线购物体验.
  • 利用物联网设备和机器学习 (ML) 来改善电子商务决策.
  • 通过客户行为分析和需求预测开发适应性零售策略.

主要方法:

  • 利用物联网 (IoT) 设备收集客户行为和偏好数据.
  • 应用了各种机器学习 (ML) 算法,包括后勤回归,天真贝叶斯,支持矢量机 (SVM),随机森林 (RF),AdaBoosting,门式反复单元 (GRU) 和长短期内存 (LSTM).
  • 使用F1得分,准确性,精度和回忆等指标评估模型性能,将传统的ML与深度学习技术进行比较.

主要成果:

  • 在深度学习模型 (LSTM,GRU) 和其他ML技术中,AdaBoosting表现出优越的性能.
  • 通过AdaBoosting实现了88%的准确性,F1得分为0.927,精度-1为0.908,回忆-1为0.947.
  • 大多数ML技术与计数向量化器和TD-IDF向量化器表现相似,除了SVM.

结论:

  • 物联网和机器学习的整合,特别是AdaBoosting,显著提高了电子商务的能力.
  • 这种方法带来了更高效的运营,更好的需求预测和更高的客户满意度.
  • 这项研究为电子商务中以客户为中心,适应性和高效的零售策略的新时代铺平了道路.