2 publications

• Published Subscription Original Research 31 Dec 2025

  DC Motor Control using Deep Reinforcement Learning for Enhanced Robustness and Precision

  By N. N. Shaikh, I.R Kazi Kutubuddin Sayyad Liyakat

  Abstract: DC motors remain the workhorse of industrial automation and mobile robotics, but achieving simultaneous high-speed transient response and negligible steady-state error under variable load conditions continues to challenge classical Proportional-Integral-Derivative (PID) controllers. These model-dependent systems often require extensive tuning and struggle to maintain optimal performance when confronted with parametric uncertainties, non-linear friction, or sudden voltage fluctuations. This study presents a novel, model-free control paradigm utilizing Deep Reinforcement Learning (DRL)—specifically, a …

  Published in International Journal of Electrical Machine Analysis and Design · Vol. 3, Issue 2, 2025 · pp. 22–29 Read article →

• Published Subscription Review Article 27 Dec 2025

  A study in Leveraging Deep Learning and IoT Arrays for Dynamic, Hyper-Local Atmospheric Intelligence

  By Milind Shivaji Kadam, Vaishnavi Gopal Shirsikar, N. N. Shaikh, Aditi Dinanath Shahane, Dr. Kazi Kutubuddin Sayyad Liyakat

  Abstract: The critical demand for high-resolution, actionable atmospheric data is challenged by the high cost and sparse coverage of traditional regulatory monitoring stations. This paper explores the synergistic paradigm shift enabled by integrating low-cost, dense Internet of Things (IoT) sensor arrays with advanced Artificial Intelligence (AI) methodologies, specifically Deep Learning (DL) models. We address the primary limitations of low-cost sensors—inherent bias, sensitivity to environmental drift (temperature/humidity), and calibration inconsistency—by utilizing AI …

  Published in International Journal of Atmosphere · Vol. 2, Issue 2, 2025 · pp. 50–62 Read article →