Real‑time anomaly detection system for community pharmacies prescription processing using machine learning techniques.

A comprehensive review of emerging technologies and future trends in cyber-physical power systems, exploring innovations that will shape the next era of smart grid technologies.

A multi-level interactive approach for semi-automated metamodel/model co-evolution, addressing the challenges of maintaining consistency between metamodels and their corresponding models during evolution.

A large-scale empirical study analyzing refactoring discussions on Stack Overflow to understand what refactoring topics developers commonly discuss and the challenges they face in practice.

A novel approach for prioritizing refactorings specifically for security-critical code, addressing the challenge of improving code quality while maintaining security properties.

A novel approach that analyzes commit messages to recommend appropriate refactorings, leveraging natural language processing to understand developer intentions and code changes.

An interactive multi-objective approach for software refactoring that enables developers to explore decision and objective spaces, facilitating better understanding of trade-offs in refactoring decisions.

An investigation into the relationship between refactoring and security, proposing a security-aware refactoring approach that considers security implications while improving code quality.

An interactive approach for metamodel/model co-evolution that combines unsupervised learning with multi-objective search to automate and optimize the evolution process.

A comprehensive systematic literature review covering three decades of software refactoring research, analyzing trends, techniques, and future directions in the field.

An intelligent bot for automated software refactoring that learns from developer behavior and provides intelligent refactoring recommendations in real-time.

A novel approach that transforms multi-objective refactoring problems into mono-objective ones by extracting and incorporating developer knowledge, simplifying the decision-making process.

An interactive and dynamic approach for multi-objective software refactoring recommendations that adapts to developer preferences and provides real-time feedback during the refactoring process.

A clustering-based approach to reduce the effort required in interactive refactoring by grouping similar refactoring solutions and guiding multi-objective search more efficiently.

A dynamic and interactive search-based approach for software refactoring recommendations that adapts to developer preferences and provides real-time feedback during the refactoring process.