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Point-cloud-based 3D object detection for autonomous navigation in unmanned ground vehicles
(Brac University, 2024-11)
Autonomous navigation for UGVs faces significant challenges in detecting objects
accurately in complex environments. Despite advancements in 2D object detection,
the absence of robust 3D object detection models leave a ...
Criminal activity detection from videos under low light condition using deep neural network
(BRAC University, 2024-10)
Criminal activity detection from footage, especially in low-light circumstances, offers
a considerable problem due to reduced visibility, noise, and detail loss. In
this research, we present an approach for detecting ...
A comprehensive hybrid framework for Parkinson’s disease detection: integrating handcraft features along with deep learning-based feature extraction with variational autoencoder and traditional machine learning techniques for classification
(BRAC University, 2024-10)
Neurodegenerative disorders, such as Parkinson’s disease, present a significant medical
challenge, necessitating innovative approaches for detection. This thesis introduces
a comprehensive hybrid framework that combines ...
Detection of pulmonary diseases from chest X-ray images using deep learning model
(BRAC University, 2024-10)
Deep learning models are important in efficiently identifying different pulmonary
diseases from Chest X-ray Images (CXRs). Pneumonia is one of the most common
lung diseases that cause death. Especially, stage 4 pneumonia ...
TRI-FED-RKD: integrating forward-reverse distillation with SNN and CNN within federated learning using tri layer hierarchical aggregation based architecture
(BRAC University, 2024-10)
Federated Learning (FL) is a decentralized machine learning paradigm that enables
training a global model across numerous edge devices while preserving data privacy.
However, FL faces significant challenges, particularly ...
Loan approval prediction using machine learning algorithms
(BRAC University, 2024-10)
This research describes the potential of several classifiers of classical machine learning
and architecture of deep neural networks when predicting the status of a loan
application. The data set of 613 observations and ...