3 citations · 3 across the 5 of their papers we have counts for
6 papers
Class-Conditioned Gaussian Mixture Modeling for Imbalanced Time Series Quantification
Md Shahriar Kabir, Mayesha Maliha R. Mithila, Anne H. H. Ngu +2
Quantification, estimating class prevalences in bags of unlabeled instances is vital in domains where aggregate statistics are more important than individual instance labels, such…
MA-RAG: Multi-Agent Retrieval-Augmented Generation for Query-Driven Summarization of Longitudinal Parkinson's Disease Assessments
Sana Alamgeera, Denise Goberta, Muhammad Irshad +1
Accurate interpretation of single-visit and longitudinal clinical assessments for Parkinson's disease is time-consuming and often depends on specialist expertise. Although large la…
You Don't Need Attention: Gated Convolutional Modeling for Watch-Based Fall Detection
Sana Alamgeer, Ronish Kumar, Awatif Yasmin +2
Existing deep learning approaches for wearable fall detection systems rely on self-attention mechanisms that impose quadratic computational overhead, distributing weights across al…
Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning
Awatif Yasmin, Tarek Mahmud, Sana Alamgeer +1
Personalized fall detection models can significantly improve accuracy by adapting to individual motion patterns, yet their effectiveness is often limited by the scarcity of real-wo…
TransConv-DDPM: Enhanced Diffusion Model for Generating Time-Series Data in Healthcare
Md Shahriar Kabir, Sana Alamgeer, Minakshi Debnath +1
The lack of real-world data in clinical fields poses a major obstacle in training effective AI models for diagnostic and preventive tools in medicine. Generative AI has shown promi…
AI-Generated Fall Data: Assessing LLMs and Diffusion Model for Wearable Fall Detection
Sana Alamgeer, Yasine Souissi, Anne H. H. Ngu
Training fall detection systems is challenging due to the scarcity of real-world fall data, particularly from elderly individuals. To address this, we explore the potential of Larg…