This study tackles the unsolved Capset Problem through an unconventional analysis of attribute distributions, aiming to determine the size of capsets with ‘n’ attributes and proposing that identifying specific distribution patterns could lead to a breakthrough.
This project focuses on leveraging machine learning techniques and the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset to preprocess sensor data and develop a sophisticated model for accurately predicting the Remaining Useful Life (RUL) of aircraft engines, thereby enhancing aviation safety and operational efficiency through timely maintenance or replacement of engine components.
This project explores the dynamics of idea sharing by introducing a system of differential equations, incorporating SIR-like and population growth models to simulate the spread of shared ideas, analyzing factors such as user flow rates, identifying potential cyclical patterns, and incorporating a capacity limit to simulate a more realistic model within a community.
Explore the application of Markov chains in predicting wind speed behavior using a dataset from Chièvres, Belgium. The project includes data preparation, transition matrix computation, simulation, and autocorrelation analysis, highlighting the model’s strengths and limitations in capturing the complex dynamics of wind speed fluctuations
This project focuses on recreating the YOLO v3 algorithm from scratch using PyTorch, incorporating various data augmentation techniques and evaluating its performance on the MS COCO dataset, revealing challenges and insights into the model’s training and inference capabilities.
This project employs statistical analysis to explore the correlation between crime count, weather conditions, and weekends, aiming to determine if these factors can be statistically significant reasons for canceling outdoor plans with friends.
This study explores the application of Sinkhorn balancing, a probabilistic algorithm with lower computational complexity, robustness, and ability to handle diverse problems, as an effective technique in solving Sudoku puzzles compared to traditional back propagation methods.
This paper explores the intricacies of tetrahedral mesh generation from medical volumetric data, highlighting the challenges posed by numerical errors and presenting a Delaunay triangulation-based approach for creating high-quality and subject-specific meshes.