reinforcement learning

Scalable Real-Time Data Pipelines for AI and Machine Learning–Driven Enterprise Systems

The growth of enterprise data volumes across the 2000s and 2010s pushed traditional batch-oriented data processing infrastructures past their practical limits, motivating a sustained shift toward distributed, stream-based architectures capable of supporting real-time analytics and machine learning (ML). This article sy...

A Hybrid Fuzzy–Deep Reinforcement Learning Framework for Real-Time Heart Patient Prioritization in Fog–Cloud Healthcare Systems

Meta reinforcement learning for robust quadruped locomotion under joint failures

Joint failures can disrupt gait coordination, velocity tracking, and body stability in quadruped robots by reducing the available control authority. This study investigates faults in which joints remain locked at fixed angles and presents Adjudicate Reconfiguration and Evolving Regularization Meta Reinforcement Learnin...

Unified Reinforcement-learning-assisted PI Gain Adaptation for CC-CV Control of an SPS-modulated Dual-active-bridge EV Fast Charger

This paper proposes a unified reinforcement-learning-assisted PI controller for CC-CV charging of an SPS dual-active-bridge converter.A DDPG agent acts as a supervisory layer that adapts bounded current-and voltage-loop PI gains while preserving deterministic mode logic, anti-windup, phase saturation, and pulse generat...

A Systematic Review of Artificial Intelligence ( AI )‐Based Task Scheduling Techniques for Cloud‐Fog Computing Environments

ABSTRACT The rapid proliferation of latency‐sensitive and data‐intensive applications has accelerated the adoption of cloud–fog computing architectures to support real‐time processing and distributed resource management. However, efficient task scheduling in cloud–fog environments remains a challenging problem because...

CBGA-SAC: A CVaR-Constrained Deep Reinforcement Learning Framework for Portfolio Optimization

Deep Learning for Proxy Zero-Day Intrusion Detection: A Systematic Review, Taxonomy, and Evaluation Framework

Safety-Certified Hierarchical Control of DC Microgrids via Continuous Deep Reinforcement Learning and Covariance-Weighted Sliding Mode Synthesis

This paper presents a hierarchical safe control approach for DC microgrids, combining learning-based energy management with rigorous constraint enforcement. The main novelty is a covariance-aware coupling mechanism that simultaneously informs CBF constraint tightening, sliding-mode gain scheduling, and DRL state augmen...

Distinct Patterns of Adaptive Decision-Making in Mouse Models of Neurodevelopmental Disorders

Neurodevelopmental disorders (NDDs) arise from diverse genetic alterations that can affect adaptive decision-making, but quantifying specific components of cognitive processes remains challenging. We developed a sequence of deterministic and probabilistic reversal learning tasks using an automated operant platform and...

ALEX-GWO: Reinforcement-learning-driven Adaptive Grey Wolf Optimization with Landscape-aware Local-optima Escape for Large-scale Global Optimization

Large-scale global optimization exposes a structural weakness of the grey wolf optimizer: its convergence coefficient follows a schedule fixed in advance, the three leading wolves collapse into a single attraction basin long before the budget is spent, and that loss of diversity is neither detected nor repaired.Publish...

Intelligent Recognition of Joint Positions in Surrounding Rock of Tunnels Based on Interpretable Machine Learning Models

Machine Learning-Driven Optimization of Multiphase Chemical Processes for Energy-Efficient and Sustainable Manufacturing

There are many industrial chemical manufacturing processes that involve multi-phase chemistry, such as gas–liquid, liquid–liquid, gas–solid, solid–liquid and three-phase systems, and these are among the most energy-intensive and complex unit processes to optimise, due to the complex, nonlinear and often unmeasurable in...