Momotaz Begum

Diagnostic Performance of Magnetic Resonance Imaging (MRI) in The Diagnosis of Intracranial Meningioma

Nasrin Sultana, Momotaz Begum, Shiek Md Abul Fazal, Aliza Mamataz et autres

Background: Meningiomas are the most common non-glial tumours of the central nervous system, arise from the arachnoid cap cells of the arachnoid villi compressing the brain. It is generally considered slowly growing lesions, often with an indolent clinical course depending on the …

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TAIL-Safe: Task-Agnostic Safety Monitoring for Imitation Learning Policies

Riad Ahmed, Momotaz Begum

Recent imitation learning (IL) algorithms -such as flow-matching [16] and diffusion [6] policies -demonstrate remarkable performance in learning complex manipulation tasks.However, these policies often fail even when operating within their training distribution due to extreme sensitivity to initial conditions and irreducible approximation …

An Efficient Metric for Data Quality Measurement in Imitation Learning

Noushad Sojib, Sajay Arthanat, Momotaz Begum

Imitation learning (IL) has seen remarkable progress, yet field deployment of IL-powered robots remains hindered by the challenge of out-of-distribution (OOD) scenarios. Fine-tuning pre-trained policies with end-user demonstrations collected in deployment environments is a promising strategy to address this challenge. However, end-user …

To Do or Not to Do: Ensuring the Safety of Visuomotor Policies Learned from Demonstrations

Riad Ahmed, Moniruzzaman Akash, Momotaz Begum

Task success has historically been the primary measure of policy performance in imitation learning (IL) research. This characteristics strictly limits the ubiquitous applications of IL algorithms in field robotics where safety assurance, in addition to task-success, is of paramount importance. It is …

Good in Bad (GiB): Sifting Through End-user Demonstrations for Learning a Better Policy

Noushad Sojib, Ola Ghattas, Sajay Arthanat, Momotaz Begum

Imitation learning offers a promising framework for enabling robots to acquire diverse skills from human users. However, most imitation learning algorithms assume access to high-quality demonstrations an unrealistic expectation when collecting data from non-expert users, whose demonstrations often contain inadvertent errors. Naively …

A Principled Approach for Creating High-fidelity Synthetic Demonstrations for Imitation Learning

Moniruzzaman Akash, Momotaz Begum

Recent advances in 3D Gaussian Splatting (3DGS) have enabled visually realistic demonstration generation from a single expert trajectory and a short multi-view scan. However, existing 3DGS-based synthesis pipelines typically generate new motions using sampling-based planners or trajectory optimization, which often deviate substantially …

Ethical considerations in home monitoring technologies for persons living with cognitive impairment: a scoping review

Jing Wang, Sajay Arthanat, Eugenia Opuda, Dain Patrick LaRoche et autres

BACKGROUND AND OBJECTIVES: As the global population of people living with cognitive impairment grows, Home Monitoring Technologies (HMTs), such as cameras, motion sensors, wearable trackers, and artificial intelligence enabled ones are increasingly used to enhance safety and support aging in place. However, …

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Probabilistic Safety Guarantee for Stochastic Control Systems Using Average Reward MDPs

Saber Omidi, Marek Petrik, Se Young Yoon, Momotaz Begum

Safety in stochastic control systems, which are subject to random noise with a known probability distribution, aims to compute policies that satisfy predefined operational constraints with high confidence throughout the uncertain evolution of the state variables. The unpredictable evolution of state variables …

Abstract 224: Rare Cerebral Foreign Body Reaction to FDS Responsive to IV Steroids

Momotaz Begum, Ronald A. Miller, Adam R. Blanden, Grahame Gould

Here we present a case of cerebral foreign body reaction (CFBR) with resultant punctate strokes in a middle‐aged man caused by a flow diverting stent (FDS). The patient initially presented due to spontaneous SAH with extensive intraventricular involvement and severe hydrocephalus [Hunt …

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ECG-HO-Net: A Hybrid Deep Learning Framework for Automated Arrhythmia Detection Using Hippopotamus Optimization and Liquid Neural Networks

Momotaz Begum, N. Padmaja

With the increasing incidence of cardiovascular disorders and the limitations of existing ECG classification methods in handling signal variability and feature redundancy, there is a pressing need for a hybrid model that ensures both accuracy and adaptability in arrhythmia detection. The proposed …

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Practitioner Evaluation Of Machine Learning Therapeutic Exercise Robot

Dain Patrick LaRoche, Momotaz Begum, Paul Gesel, Sajay Arthanat

Novel technological approaches to home exercise programs are needed to offset the supply-demand gap for older adults, people with disability, rural residents and others who have limited access to physical and occupational therapy services. PURPOSE: The purpose of this study was to …

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HSTViT: A Hybrid Approach of Vision Transformers and Superimposing For Brain Stroke Classification and Lesion Area Quantification

Momotaz Begum, Md. Alamgir Hossain, Sahelee Sultana

Accurate diagnosis of brain stroke-like Intracerebral Hemorrhagic Stroke (ICH-S) classification and area quantification of the stroke are tremendously significant for physicians to focus on precise points of the brain and apply the accurate treatment to patients. This paper proposes a hybrid approach …

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