title: Literature - Namangan Institute of Engineering and Technology nammti uz
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  Namangan Institute of Engineering and Technology nammti uz Pdf ko'rish
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  bet | 465/693 | Sana | 13.05.2024 | Hajmi | 15,56 Mb. | | #228860 |
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  Literature
  1.
  Smith, J., and Johnson, M. (2022). "IoT in Urban Infrastructure: Foundations and
  Applications". Urban Technology Review.
  2.
  Patel, R. et al. (2023). "Enhancing Urban Services through IoT". Journal of Smart City
  Development.
  3.
  Lee, H., and Kim, J. (2021). "Challenges in Smart City IoT Implementations". International
  Journal of Urban Planning and Technology.
  4.
  Zhang, Y. (2024). "Future Trends in IoT and Smart City Integration". Advanced Urban
  Technology Journal.
  Breakthroughs in machine learning and big data for business intelligence
  Sany Izan Ihsan
  International Islamic University of Malaysia, Malaysia
  Joelle Menant
  Université de La Reunion, France
  Annotation. This article explores the significant breakthroughs in machine learning and big
  data analytics, focusing on their transformative impact on business intelligence (BI). It highlights
  how these technological advancements enable businesses to harness complex datasets for strategic
  decision-making, improve operational efficiency, and gain competitive advantages.
  Keywords. Machine Learning, Big Data, Business Intelligence, Data Analytics, Predictive
  Analytics, AI in Business, Data-Driven Decision Making, Business Strategy.
  Introduction. In the age of digital transformation, machine learning and big data have become
  pivotal in redefining business intelligence. These technologies not only enhance data processing
  capabilities but also empower businesses to extract actionable insights from vast and complex
  datasets. This article examines the synergy of machine learning and big data in BI, illustrating their
  role in optimizing business strategies and operations.
  Literature Review
  1.
  The Evolution of Machine Learning in BI: Smith and Johnson's (2022) comprehensive
  review discusses the integration of machine learning algorithms in business analytics, from
  traditional data analysis to advanced predictive models.
  2.
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