Vol. 9 (2025): Journal of Artificial Intelligence for Data-Driven Discovery (JAIDDD)

The Journal of Artificial Intelligence for Data-Driven Discovery (JAIDDD) is a peer-reviewed, open-access journal dedicated to advancing research and innovation in artificial intelligence for data-driven scientific discovery. Published annually, JAIDDD serves as a platform for researchers, data scientists, and industry professionals to share cutting-edge discoveries, methodologies, and applications that drive the evolution of AI in data-driven research. With a commitment to rigorous double-blind peer review, JAIDDD ensures high-quality, impactful publications that foster global collaboration and knowledge exchange.

Published: 01-01-2025

Articles

Using Artificial Intelligence to Develop Strategies for Validating Robustness in Autonomous Cyber-Physical Defense Mechanisms

Margot Lefevre (Author)

22-35
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The Development of Scalable Artificial Intelligence Algorithms for the Protection of Kubernetes Clusters in Multiple Clouds

Lucas Ferreira (Author)

11-24
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Enhancing Cloud-Native Application Security Using AI-Driven Microservice Anomaly Detection

Natasha Volkov (Author)

32-46
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Enhancing Continuous Integration Pipelines with AI-Powered Risk Assessment for Code Merging in Large Repositories

Hae-won Shin (Author)

1-10
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Enabling Privacy-Centric AI Models for Secure Data Analysis in Federated Machine Learning Frameworks

Prof. Beatriz Gomez (Author)

47-51
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Autonomous Model Lifecycle Management: AI Systems for Self-Monitoring, Updating, and Optimization

Percy Liang (Author)

58-63
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Intent-Aware Artificial Intelligence Systems: Interpreting and Aligning with Dynamic User Objectives

Raia Hadsell (Author)

52-57
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Data-Driven Risk Management in Cybersecurity Using AI-Enhanced Bayesian Inference Models

Dr. Arvind Nair (Author)

42-46
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