Author: Priya Kapoor

Priya Kapoor brings a distinct perspective to technology journalism through her specialized focus on industrial computing and automation systems, where she deciphers complex technical innovations for both engineering and business audiences. Her reporting delves into the convergence of IoT infrastructure and emerging technologies, examining how smart systems are reshaping manufacturing and enterprise operations. With a keen eye for practical applications, she translates technical specifications into strategic insights that help professionals navigate digital transformation. Priya's analysis stands out for bridging the gap between industrial engineering realities and future-facing technological possibilities.
EngineeringInfrastructureResearch

Generative AI Boosts Pipeline Safety Predictions Amid Data Scarcity

Advanced data augmentation techniques are revolutionizing pipeline safety assessments by generating synthetic corrosion data where real-world testing is impractical. The CopulaGAN-LightGBM combination reportedly delivers the highest predictive improvement, while SHAP analysis identifies critical failure factors. A new web platform now enables real-time residual strength calculations for industry professionals.

Breaking Through Data Barriers in Pipeline Integrity

Researchers have developed a novel approach to predicting the residual strength of corroded pipelines by combining generative artificial intelligence with machine learning, according to recent findings published in npj Materials Degradation. The method addresses a critical industry challenge: the scarcity of high-quality experimental data needed to train accurate predictive models for pipeline safety assessment.

by Priya Kapoor