Academia Open
Vol 10 No 1 (2025): June (In Progress)
Computer Science

Machine Learning: Algorithms, Real World Applications and Research Directions
Pembelajaran Mesin: Algoritme, Aplikasi Dunia Nyata, dan Arah Penelitian


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  • Muthna Nehad Al-Tameemi*
*

(*) Corresponding Author
DOI: https://doi.org/10.21070/acopen.9.2024.10451
Published January 2, 2025
Keywords
  • Machine learning,
  • Deep learning,
  • Artificial intelligence,
  • Data science,
  • Data-driven decision-making,
  • Predictive analytics,
  • Intelligent applications
    • ...More
    Less
How to Cite
Al-Tameemi, M. N. (2025). Machine Learning: Algorithms, Real World Applications and Research Directions. Academia Open, 10(1), 10.21070/acopen.9.2024.10451. https://doi.org/10.21070/acopen.9.2024.10451

Copyright (c) 2025 Muthna Nehad Al-Tameemi

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

In the era of the Fourth Industrial Revolution (4IR), vast amounts of data are generated from sources such as IoT, cybersecurity, mobile, business, social media, and healthcare. Artificial intelligence (AI), particularly machine learning (ML), is essential for analyzing this data and developing smart, automated applications. Various machine learning algorithms, including supervised, unsupervised, semi-supervised, and reinforcement learning, are used to enhance application intelligence. Additionally, deep learning, a subset of ML, can process large-scale data effectively. This paper provides an overview of these algorithms and their applications across fields like cybersecurity, smart cities, healthcare, e-commerce, and agriculture. It also discusses the challenges and research opportunities in the field, aiming to be a valuable resource for both academics and industry professionals, as well as decision-makers in various real-world scenarios.

Highlights:

  1. Post-modern ceramics reflect features and connotations of body language.
  2. Research explores theoretical framework, variables, and applications in ceramic sciences.
  3. Results reveal key indicators, conclusions, and supporting references.

Keywords: Machine learning, Deep learning, Artificial intelligence, Data science, Data-driven decision-making, Predictive analytics, Intelligent applications

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