Miguel Realpe
Papers
1
Total Citations
9
H-Index
1
About
Miguel Realpe is a researcher at the forefront of applied artificial intelligence, with a primary focus on deep learning methodologies for sensor data analysis and time series forecasting. His most influential work, "Time Series in Sensor Data Using State-of-the-Art Deep Learning Approaches: A Systematic Literature Review" (2021), has garnered 9 citations, establishing a foundational reference for scholars and practitioners navigating the rapidly evolving landscape of neural network architectures in IoT and industrial monitoring contexts. Realpe’s systematic review critically evaluates cutting-edge deep learning models—including recurrent networks, transformers, and hybrid architectures—providing a comprehensive taxonomy of their strengths, limitations, and optimal use cases for sensor-driven time series. This contribution is particularly valuable for researchers seeking to deploy robust predictive models in resource-constrained or noisy sensor environments. By synthesizing disparate advances into a coherent framework, Realpe has helped bridge the gap between theoretical deep learning research and practical sensor data applications. His work continues to influence the design of intelligent monitoring systems, anomaly detection pipelines, and real-time decision-support tools across engineering and environmental domains.
Research Focus
Key Achievements
Top Papers
- 1