Performed exploratory data analysis on Reddit narratives to uncover SES-related patterns, refining labeling guidelines and generating actionable insights to improve model accuracy.
Enhanced federated learning-based SES profiling by extracting meaningful trends from community data and recommending model improvements to boost prediction precision.
This comprehensive review distills the most powerful machine learning and deep learning techniques — from SVM to LSTM — to deliver high-accuracy web traffic forecasting.
By comparing and synthesizing leading models for predicting website traffic, this paper offers a definitive roadmap for making networks smarter, faster, and more resilient.
Applies machine learning to profile downhole temperature and pressure in geothermal wells, achieving strong agreement with field data and advancing predictive wellbore diagnostics.
Integrates thermal conductivity, skin factor, and contact resistance into predictive models to unlock more accurate subsurface profiling in challenging well environments.
