About the Journal

Advanced in Artificial Intelligent and Machine Learning (AAIML) is a peer-reviewed open-access journal that publishes original research papers, review articles, and case studies in the field of artificial intelligence (AI) and machine learning (ML). The journal aims to advance theoretical foundations, innovative methodologies, and real-world applications of intelligent systems.

 

AAIML serves as an interdisciplinary platform for academics, researchers, and practitioners to exchange ideas, foster collaboration, and disseminate cutting-edge findings in areas such as deep learning, natural language processing, computer vision, robotics, data analytics, and intelligent decision support systems.

 

The journal is published four times a year (quarterly) — in March, June, September, and December — by Goodwood Publishing. All articles undergo a double-blind peer-review process to ensure scientific rigor and publication quality.

 

AAIML welcomes global contributions aimed at promoting the ethical and sustainable development of artificial intelligence and machine learning for technological and societal advancement.


Aims and Scope

The Advanced in Artificial Intelligent and Machine Learning (AAIML) journal aims to:

  • Promote the exchange of scientific knowledge and innovations in artificial intelligence and machine learning.
  • Encourage interdisciplinary research that integrates AI/ML with various domains such as business, education, healthcare, and engineering.
  • Foster critical discussions on emerging trends, challenges, and ethical issues in AI and intelligent automation.
  • Support collaboration between academia, industry, and policymakers to accelerate technological progress.


The scope of the journal includes, but is not limited to:

  • Artificial Intelligence Theory and Applications
  • Machine Learning Algorithms and Optimization
  • Deep Learning and Neural Networks
  • Natural Language Processing (NLP)
  • Computer Vision and Image Recognition
  • Data Mining and Big Data Analytics
  • Robotics and Automation Systems
  • Fuzzy Logic and Expert Systems
  • Reinforcement Learning and Predictive Modeling
  • Ethical AI and Responsible Innovation