Search
144 articles for “Noise Power”
-
Designing and Modeling of Giant Magnetoresistance (GMR) Materials based Devices in Electrical Power and Biomedical Systems
Abstract: This paper presents Designing and Modeling of Giant Magnetoresistance (GMR) Devices in Electrical Power and Biomedical Systems and the related materials. The GMR, inverse GMR, and Spin valve using exchange bias have been analytically derived and discussed from the designing point of view for optimizing the performance of the GMR based Devices in Electrical systems. More recently, GMR has been used in sensors, magnetic memory chips, and hard-disk read-heads. The …
Published in Research & Reviews : Journal of Physics · Vol. 14, Issue 1, 2025 · pp. 22–32 Read article
-
Networked Embedded Systems Eliminate Safety Hazards in Rail Journey
Abstract: ABSTRACT Introducing R&D into Rail transport is a good way to make sure that Railway decisions reflect the needs of passengers. Putting in place a foolproof security architecture and best brand components in the design of a safety system can ensure railway commutation in a high security environment. Managing security through proactive monitoring and event management is the key for achieving desired results. Safety and Punctuality — the two watch …
Published in Journal of Communication Engineering & Systems · Vol. 1, Issue 1-3, 2011 · pp. 34–43 Read article
-
Support Vector Machine Inspired Load Forecasting of a State University in Haryana
Abstract: Estimating the possible environmental impact and determining probable capital requirements are made easier with a solid grasp of electricity demand. Beginning in the middle of the 20th century, demand forecasting for electric power networks was studied theoretically. Prior to that, the study of demand forecasting had not developed because of the small scale of power networks. With the use of statistical prediction techniques, plans for the electric power industry have …
Published in Trends in Electrical Engineering · Vol. 15, Issue 2, 2025 · pp. 33–40 Read article
-
A Comprehensive Investigation of Bagging-Based Ensemble Methods for Improving Machine Learning Model Robustness
Abstract: Machine learning models such as Decision Trees, Logistic Regression, and K-Nearest Neighbors are widely used for classification tasks due to their simplicity and interpretability. However, these models often suffer from high variance, overfitting, and poor generalization when applied to real-world datasets, particularly those that are small, noisy, or imbalanced, as commonly encountered in healthcare, finance, and cybersecurity applications. To address these limitations, this research proposes a Bagging (Bootstrap Aggregating)-based ensemble …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 24–34 Read article