Case Study
A hybrid deep learning approach for predictive maintenance, combining time-frequency feature extraction, advanced AI architectures, and innovative bearing analysis to optimize Remaining Useful Life (RUL) predictions, tackling data sparsity and complex degradation dynamics for industrial reliability.
Case Study
An advanced NLP pipeline for structured topic and sentiment analysis, leveraging large language models (LLMs), zero-shot labeling, and hierarchical clustering to decode emotional triggers and semantic patterns in online communities for scalable insights.
Machine Learning
An in-depth exploration of standard deviations and effect sizes in Linear Mixed Models (LMMs), clarifying distinctions between descriptive and inferential statistics, and addressing challenges in analyzing clustered data with hierarchical dependencies.
Machine Learning
A comprehensive explanation of Linear Mixed Models (LMMs), including their formulation and functionality, exploring how they handle hierarchical or correlated data structures through fixed and random effects to account for non-independence in datasets.