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Aims & Scope


Acadlore Transactions on AI and Machine Learning (ATAIML) emerges as a pivotal platform at the intersection of artificial intelligence, machine learning, and their multifaceted applications. Recognizing the profound potential of these disciplines, the journal endeavors to unravel the complexities underpinning AI and ML theories, methodologies, and their tangible real-world implications.

In a world advancing at digital light-speed, ATAIML posits that AI and ML reshape industries at their core. From the expansion of reality to the birth of synthetic data and the intricate design of graph neural networks, such advancements are at the forefront of innovation. With a mission to chronicle these paradigm shifts, ATAIML aims to serve as a beacon for researchers, professionals, and enthusiasts eager to fathom the vast horizons of AI and ML in the modern age.

Furthermore, ATAIML highlights the following features:

  • Every publication benefits from prominent indexing, ensuring widespread recognition.

  • A distinguished editorial team upholds unparalleled quality and broad appeal.

  • Seamless online discoverability of each article maximizes its global reach.

  • An author-centric and transparent publication process enhances submission experience.


ATAIML's expansive scope encompasses, but is not limited to:

  • AI-Integrated Sensory Technologies: Insights into AI's role in amplifying and harmonizing sensory data.

  • Symbiosis of AI and IoT: The collaborative dance between artificial intelligence and the Internet of Things and their cumulative impact on contemporary society.

  • Mixed Realities Shaped by AI: Probing the AI-crafted mixed-reality realms and their implications.

  • Sustainable AI Innovations: A focus on 'Green AI' and its instrumental role in shaping a sustainable future.

  • Synthetic Data in the AI Era: A deep dive into the rise and relevance of synthetic data and its AI-driven generation.

  • Graph Neural Paradigms: Exploration of the nuances of graph-centric neural networks and their evolutionary trajectory.

  • Interdisciplinary AI Applications: Delving into AI's intersections with fields such as psychology, fashion, and the arts.

  • Moral and Ethical Dimensions of AI: A comprehensive study of the ethical landscapes carved by AI's advancements and the corresponding legal challenges.

  • Diverse Learning Methodologies: Exploration of revolutionary learning techniques ranging from Bayesian paradigms to statistical approaches in ML.

  • Emergent AI Narratives: Spotlight on cutting-edge AI technologies, foundational standards, computational attributes, and their transformative use cases.

  • Holistic Integration: Emphasis on multi-disciplinary submissions that combine insights from varied fields, offering a holistic perspective on AI and ML's global resonance.