Artificial Intelligence, Machine Learning and Data Science / Volume 01 Posted on: March 31, 2026 A Conceptual Framework for Neural Network Version Control: Diff, Merge, and Branch Operations in Weight Space Read More
Artificial Intelligence, Machine Learning and Data Science / Volume 01 Posted on: March 25, 2026March 25, 2026 Algorithmic Collusion Without Explicit Communication: Controlled Computational Laboratory Evidence From Repeated Q-Learning Price Competition Read More
Artificial Intelligence, Machine Learning and Data Science / Volume 01 Posted on: March 9, 2026 Scalable Variational Inference for Gaussian Process Models with Non-Gaussian Likelihoods Read More
Artificial Intelligence, Machine Learning and Data Science / Volume 01 Posted on: January 3, 2026 Bounding the Unobserved: Partial Identification of Causal Effects in Multivariate Time Series Under Latent Confounding Read More
Artificial Intelligence, Machine Learning and Data Science / Volume 01 Posted on: January 1, 2026 Democratizing Machine Learning through AutoML: A Framework for Accessibility, Quality Control, and Risk Assessment in Responsible Model Development Read More
Artificial Intelligence, Machine Learning and Data Science / Volume 01 Posted on: December 24, 2025 Adaptive Energy-Aware Task Scheduling for Edge-AI in Precision Agriculture: A Lyapunov Optimization Approach Read More
Artificial Intelligence, Machine Learning and Data Science / Volume 01 Posted on: December 22, 2025 Robust Anomaly Detection in Industrial Sensor Streams: An Adaptive Framework for Mitigating Concept Drift in Predictive Maintenance Read More
Artificial Intelligence, Machine Learning and Data Science / Volume 01 Posted on: December 19, 2025 Integrating Artificial Intelligence and Operations Research for Enhanced Decision Support: A Methodological Framework Read More
Artificial Intelligence, Machine Learning and Data Science / Volume 01 Posted on: December 19, 2025 Quantifying Typological Diversity in Multilingual Research: A Framework for Measuring Language Sample Representativeness and Addressing Sampling Bias Read More
