多代劳动力市场:使用机器学习对多视角系统参数的数据驱动发现
Abeer Abdullah Alaql1, Fahad Alqurashi1, Rashid Mehmood2
1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Science progress
|November 29, 2023
概括
这项研究使用大数据和机器学习来确定28个关键参数,以了解多代劳动力市场. 这些见解对于在全球挑战中发展可持续的经济和社会至关重要.
科学领域:
- 经济学 经济学 经济学
- 社会学 社会学 社会学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 侵略性资本主义影响着社会,经济和地球的可持续性.
- 持续的经济问题 (通货膨胀,能源成本,战争,流行病) 和劳动力变化 (大消耗,多样性) 需要转型型方法.
- 现有的经济模式与当前的全球挑战作斗争.
研究的目的:
- 通过大数据和机器学习,发现多代劳动力市场的多视角参数.
- 开发一个数据驱动的软件工具,用于参数发现.
- 增强基于人工智能的知识发现理论和实践,用于自主系统和新型劳动经济学.
主要方法:
- 利用大数据和机器学习进行参数发现.
- 分析了3.5万篇学术文章摘要 (科学网,1958-2022) 和5.7万篇LinkedIn帖子 (2022).
- 应用定量和可视化方法,提取多种分类学,用于多代劳动力市场的探索.
主要成果:
- 发现了28个参数,分为五个宏观参数:学习和技能,就业部门,消费行业,学习和就业问题以及特定世代的问题.
- 开发了一个完整的机器学习软件工具,用于数据驱动的参数发现.
- 提供了多代劳动力市场的知识结构和文献综述.
结论:
- 这些发现增强了基于人工智能的知识和自主能力的系统参数发现.
- 促进对劳动力经济和市场的新方法,促进可持续的社会和经济.
- 为理解和导航复杂的多代劳动力市场提供数据驱动的框架.
关键词:
机器学习是机器学习.大数据分析大数据分析第一个世代是X代,X代是X代.这一代是Y世代,Y世代是Y世代.这一代人,就是Z世代.这一代的阿尔法代.劳动经济学 劳动经济学劳动力市场 劳动力市场隐藏的迪里克莱特分配.自然语言处理自然语言处理.智慧城市的智慧城市更多相关视频
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