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Cite this DOI

10.46243/jst.2025.v10.i12.pp01-10 · The Logic and the Ledger: Tracing the DNA of AI

APA (7th edition)

Rajendran Swamidurai, & Uma Kannan (2025). The Logic and the Ledger: Tracing the DNA of AI. *Journal of Science & Technology*, *10*(12), 01. https://doi.org/10.46243/jst.2025.v10.i12.pp01-10

⬇ text Italics are shown as *asterisks* in plain text — the journal or book title and the volume.

BibTeX

@article{rajendranswamidurai2025logic,
  author    = {Rajendran Swamidurai and Uma Kannan},
  title     = {{The Logic and the Ledger: Tracing the DNA of AI}},
  journal   = {Journal of Science \& Technology},
  year      = {2025},
  month     = {dec},
  volume    = {10},
  number    = {12},
  pages     = {01},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2025.v10.i12.pp01-10},
  url       = {https://doi.org/10.46243/jst.2025.v10.i12.pp01-10},
  language  = {en},
  abstract  = {The development of intelligent systems is fundamentally and inseparably linked to a sophisticated mathematical framework. Modern artificial intelligence (AI), particularly its subfields of machine learning (ML) and deep learning, is not a new discipline of computer science but rather a highly advanced application of classical and novel mathematical principles. The models and algorithms that enable systems to process data, learn intricate patterns, and optimize predictions are built upon a bedrock of abstract mathematical theories. This paper systematically deconstructs this relationship, demonstrating how core mathematical disciplines serve as the language, the engine, and the conceptual framework for all intelligent systems. The analysis will traverse from foundational principles to their application in cutting-edge architectures and conclude with a discussion of the theoretical and practical challenges that are currently shaping the future of the field.}
}

⬇ .bib

RIS (EndNote, Zotero, Mendeley)

TY  - JOUR
TI  - The Logic and the Ledger: Tracing the DNA of AI
AU  - Rajendran Swamidurai
AU  - Uma Kannan
JO  - Journal of Science & Technology
PY  - 2025
DA  - 2025/12/15/
VL  - 10
IS  - 12
SP  - 01
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - The development of intelligent systems is fundamentally and inseparably linked to a sophisticated mathematical framework. Modern artificial intelligence (AI), particularly its subfields of machine learning (ML) and deep learning, is not a new discipline of computer science but rather a highly advanced application of classical and novel mathematical principles. The models and algorithms that enable systems to process data, learn intricate patterns, and optimize predictions are built upon a bedrock of abstract mathematical theories. This paper systematically deconstructs this relationship, demonstrating how core mathematical disciplines serve as the language, the engine, and the conceptual framework for all intelligent systems. The analysis will traverse from foundational principles to their application in cutting-edge architectures and conclude with a discussion of the theoretical and practical challenges that are currently shaping the future of the field.
DO  - 10.46243/jst.2025.v10.i12.pp01-10
UR  - https://doi.org/10.46243/jst.2025.v10.i12.pp01-10
ER  -

⬇ .ris

CSL-JSON

{
    "type": "article-journal",
    "id": "10.46243/jst.2025.v10.i12.pp01-10",
    "DOI": "10.46243/jst.2025.v10.i12.pp01-10",
    "URL": "https://doi.org/10.46243/jst.2025.v10.i12.pp01-10",
    "title": "The Logic and the Ledger: Tracing the DNA of AI",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
        {
            "family": "Rajendran Swamidurai"
        },
        {
            "family": "Uma Kannan"
        }
    ],
    "issued": {
        "date-parts": [
            [
                2025,
                12,
                15
            ]
        ]
    },
    "volume": "10",
    "issue": "12",
    "page": "01",
    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "The development of intelligent systems is fundamentally and inseparably linked to a sophisticated mathematical framework. Modern artificial intelligence (AI), particularly its subfields of machine learning (ML) and deep learning, is not a new discipline of computer science but rather a highly advanced application of classical and novel mathematical principles. The models and algorithms that enable systems to process data, learn intricate patterns, and optimize predictions are built upon a bedrock of abstract mathematical theories. This paper systematically deconstructs this relationship, demonstrating how core mathematical disciplines serve as the language, the engine, and the conceptual framework for all intelligent systems. The analysis will traverse from foundational principles to their application in cutting-edge architectures and conclude with a discussion of the theoretical and practical challenges that are currently shaping the future of the field.",
    "ISSN": "2456-5660"
}

⬇ .json What citeproc and reference managers read; the DOI system hands it out for Accept: application/vnd.citationstyles.csl+json, and so does this registry's resolver.

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