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International Journal of Artificial Intelligence & Machine Learning (IJAIML)

Publisher :

IAEME Publication

Scopus Profile
Peer reviewed only
Scopus Profile
Open Access
  • Machine Learning Tools
  • Mechatronics
  • Natural Language Processing

    • +6

e-ISSN :

9339-1263

Issue Frequency :

Monthly

Est. Year :

2022

Mobile :

9884798314

Country :

India

Language :

English

APC :

YES

Impact Factor Assignee :

Google Scholar

Email :

editor@iaeme.com

Journal Descriptions

The International Journal of Artificial Intelligence & Machine Learning (IJAIML) is an international peer reviewed open access journal. It publishes top-level work from all areas of artificial intelligence and machine learning. It aims to provide an international forum for researchers, professionals, and industrial practitioners on all topics related to artificial intelligence and machine learning area. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications. The Journal intends to serve as a forum for scholars from around the globe engaged in cutting-edge research on current issues in artificial intelligence and machine learning. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Artificial Intelligence & applications.


International Journal of Artificial Intelligence & Machine Learning (IJAIML) is :

International, Peer-Reviewed, Open Access, Refereed, Machine Learning Tools, Mechatronics, Natural Language Processing, ARTIFICIAL INTELLIGENCE, Programming Languages, Robotics, Data Mining, Fuzzy Logic, Parallel Processing , Online Monthly Journal

UGC Approved, ISSN Approved: P-ISSN , E-ISSN - 9339-1263, Established in - 2022, Impact Factor

Not Provide Crossref DOI

Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE

Indexing

Scopus Profile
Google Scholar

Publications of IJAIML

  • dott image mylib index
  • dott image April, 2025

INTERPRETABLE ARTIFICIAL INTELLIGENCE WITH EXPLAINABILITY AND ROBUSTNESS IN MEDICAL IMAGE CLASSIFICATION USING TOPOLOGICAL AND FRACTAL FEATURES

Deep learning models, particularly Convolutional Neural Networks (CNNs), have achieved remarkable accuracy in medical image analysis tasks like pneumonia detection from chest X-rays. However...

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