Introduction
The Journal of Artificial Intelligence, Big Data and Statistics (JAIBS) aims to provide a premier platform for researchers, academicians, and industry experts to disseminate cutting-edge research in the fields of big data, statistical methodologies, and artificial intelligence (AI). The journal will serve as an interdisciplinary medium fostering innovation, discussion, and advancements in data-driven research and applications.

Scope and Objectives
JAIBS will publish high-quality research in the broad, but interconnected areas of applications of Big Statistics review articles, case studies, and technical notes covering topics including, but not limited to:

• Big Data: Data storage, processing frameworks, analytics, cloud computing, edge computing, and real-time data processing.
• Statistics: Statistical learning, probabilistic models, Bayesian inference, time-series analysis, and regression techniques.
• Artificial Intelligence: Machine learning, deep learning, reinforcement learning, generative AI, and AI ethics.
• Applications: AI in healthcare, finance, social sciences, cybersecurity, and business intelligence.
• Integration of Disciplines: Intersections between big data, statistics, and AI in solving complex real-world problems.

Editorial Board and Peer Review Process
To ensure high-quality contributions, JAIDS will establish a distinguished editorial board composed of leading experts from academia and industry. The journal will follow a rigorous double-blind peer review process where submissions undergo evaluation by at least two independent reviewers before acceptance.

Publication Format and Frequency
JAIDS will be published on a semi-annual basis, with both online and print editions available. Special issues will be curated periodically to highlight emerging trends and breakthroughs.

Indexing and Impact Measurement
JAIDS will aim for indexing in reputed databases such as Scopus, Web of Science, IEEE Xplore, and Google Scholar to maximize research visibility and impact. Citation metrics and altimetric will be used to assess journal performance.

Submission Guidelines
Authors will be required to follow structured submission guidelines, including manuscript formatting, citation styles (APA/IEEE), and data-sharing policies. The journal will also support supplementary materials like datasets and code repositories for reproducibility.

Conclusion
JAIDS aspires to be a leading academic journal bridging the gap between theoretical advancements and practical implementations in big data, statistics, and AI. By fostering interdisciplinary collaboration, it will contribute significantly to the evolution of data-driven decision-making and intelligent systems.