IDEAS home Printed from https://ideas.repec.org/a/etm/ijsrst/v11y2024i5id327.html

A Comprehensive Review of Artificial Intelligence and Machine Learning : Concepts, Trends, and Applications

Author

Listed:
  • Akanksha Mishra

Abstract

This paper presents a comprehensive review of Artificial Intelligence (AI) and Machine Learning (ML), exploring foundational concepts, emerging trends, and diverse applications. AI and ML have rapidly evolved, becoming pivotal in numerous fields including healthcare, finance, manufacturing, and autonomous systems. The review begins by outlining key concepts, including the distinctions between AI, ML, and deep learning, and delves into various learning paradigms such as supervised, unsupervised, and reinforcement learning. It highlights significant advancements, such as neural networks, natural language processing, and generative models, emphasizing their impact on industry and research. The paper also examines current trends, including the rise of ethical AI, explain ability, and the integration of AI with Internet of Things (IoT) and edge computing, which are shaping the future landscape of AI applications. Additionally, it addresses the challenges and limitations associated with AI and ML, such as data privacy concerns, model interpretability, and the need for sustainable computing solutions. By synthesizing insights from recent literature, this review provides a holistic understanding of the AI and ML domains, offering perspectives on future directions and innovations. this review aims to provide a holistic understanding of AI and ML, offering perspectives on future directions and innovations.

Suggested Citation

  • Akanksha Mishra, 2024. "A Comprehensive Review of Artificial Intelligence and Machine Learning : Concepts, Trends, and Applications," International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 11(5), pages 126-142, October.
    • Handle: RePEc:etm:ijsrst:v11:y2024:i5:id:327
    DOI: 10.32628/IJSRST2411587
    as

    Download full text from publisher

    File URL: https://ijsrst.com/home/article/view/IJSRST2411587
    File Function: Abstract page
    Download Restriction: no
    File URL: https://ijsrst.com/home/article/download/IJSRST2411587/IJSRST2411587
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/IJSRST2411587?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:etm:ijsrst:v11:y2024:i5:id:327. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (email available below). General contact details of provider: https://ijsrst.com/home .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.