Index

Advanced Machine Learning-Based High Demand Forecasting of Household Energy Consumption for Enhancing Grid Operations

This research examines methods for predicting household energy usage to assist in the management of peak demand and the maintenance of grid stability. The focus is on forecasting when energy consumption surpasses certain critical levels and for what duration, allowing for proactive energy management. The study looks at the impact of various data aggregation techniques on prediction accuracy and explores approaches to refine altered consumption patterns for better forecasting. By evaluating different forecasting models and their effectiveness, the work aims to enhance energy management, promote automation in grid operations, and strengthen data-driven decision-making for a more resilient and efficient power distribution system.

Longitudinal Analysis of Parkinson’s Disease Patients Using Natural Language Processing Methods

Removing age bias in the context of pathological speech

Anomaly Detection of Industrial Products using Large Vision Language Models

Deep Learning–Driven Lorentzian Fitting for 31P Spectrum

To isolate different peaks in the phosphorus spectrum, several preprocessing steps are usually performed, and the final information about the different metabolites is extracted by multiple Lorentzian line fitting of the spectrum [1]; this least-squares fitting is prone to noise and also depends on preprocessing steps [2]. This study will investigate the use of the Lorentzian distribution generator as a known operator in a fully connected network to fit the phosphorus spectrum.

The thesis will include the following points:

Number of Lorentzian distributions required to fit the phosphorus spectrum
Comparison of the least square fit and the deep Lorentzain fit
Correlation of deep lorentzain fit peaks with tumor types

References:

Meyerspeer M, Boesch C, Cameron D, et al. 31 P magnetic resonance spectroscopy in skeletal muscle: Experts’ consensus recommendations. NMR Biomed. Published online February 10, 2020. doi:10.1002/nbm.4246
Rajput, J.R., et al.: Physics-informed conditional autoencoder approach for robust metabolic CEST MRI at 7T. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. LNCS, vol. 14227. Springer, Cham (2023)

Unsupervised detection

Evaluate computer vision and detection methods.

Pathology detection in medical images

This work will investigate applying computer vision detection techniques in medical images

Required: strong skills in

proogramming python
deep learning , training methods, pattern recognition, loss functions
medical imaging background – X-rays, CT scan images, DICOM processing
communication, scientific writing