Predictive Analysis of Aircraft Engine Lifespan: Leveraging Neural Networks on NASA's C-MAPSS Dataset

This research paper explores the application of neural networks for predictive maintenance of aircraft engines, focusing on the estimation of their Remaining Useful Life (RUL).

This research paper explores the application of neural networks for predictive maintenance of aircraft engines, focusing on the estimation of their Remaining Useful Life (RUL). Using the NASA C-MAPSS dataset, a rich resource in predictive maintenance, the study explores the effectiveness of linear regression and neural network models in forecasting engine RUL. The motivation behind this project was to enhance aircraft safety, extend engine lifespan, and optimize maintenance schedules, thereby reducing operational costs. The initial phase involved linear regression to establish a baseline understanding, followed by the development and iterative enhancement of a neural network model. The results indicated satisfactory performance on the initial dataset, such that it performed with little loss, but revealed challenges in generalizing across different datasets, such that it performed with exceedingly more loss compared to the first dataset, pointing towards the need for more adaptive modeling and feature engineering strategies.

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Using Machine Learning to identify the Habitability of Exoplanets from TESS Transit Data

With so many new tools and methods to detect exoplanets, it is time to start determining which are deserving of more research in the hunt for other habitable planets.

Eve B.

Mentored by Tomer Arnon

Smart Agriculture: Optimizing Soil pH and Crop Recommendations Using Machine Learning

This study explores how hyperparameter tuning enhances the performance of machine learning models, achieving a 99.5% accuracy in crop classification and highlighting the potential of AI-driven optimization for sustainable precision agriculture.