Tootfinder

Advancing Digital Accessibility: Integrating AR/VR and Health Tech for Inclusive Healthcare Solutions
Vishnu Ramineni, Shivareddy Devarapalli, Balakrishna Pothineni, Prema Kumar Veerapaneni, Aditya Gupta, Pankaj Gupta
https://arxiv.org/abs/2505.24039

Advancing Digital Accessibility: Integrating AR/VR and Health Tech for Inclusive Healthcare Solutions
Modern healthcare domain incorporates a feature of digital accessibility to ensure seamless flow of online services for the patients. However, this feature of digital accessibility poses a challenge particularly for patients with disabilities. To eradicate this issue and provide immersive and user-friendly experiences, evolving technologies like Augmented Reality (AR) and Virtual Reality (VR) are integrated in medical applications to enhance accessibility. The present research paper aims to stu…

Bridging the Gap: Enhancing Digital Accessibility for Medicaid Populations in Telehealth Adoption
Vishnu Ramineni, Aditya Gupta, Balakrishna Pothineni, Isan Sahoo, Shivareddy Devarapalli, Balaji Shesharao Ingole
https://arxiv.org/abs/2505.24035

Bridging the Gap: Enhancing Digital Accessibility for Medicaid Populations in Telehealth Adoption
The swift evolution of telehealth has revolutionized how medical professionals deliver healthcare services and boost convenience and accessibility. Yet, the Medicaid population encounters several impediments in utilizing facilities especially owing to poor internet connectivity, less awareness about digital platforms, and a shortage of assistive technologies. The paper aims to explicate key factors behind digital accessibility for Medicaid populations and expounds robust solutions to eradicate …

Advancing Digital Accessibility In Digital Pharmacy, Healthcare, And Wearable Devices: Inclusive Solutions for Enhanced Patient Engagement
Vishnu Ramineni, Balaji Shesharao Ingole, Nikhil Kumar Pulipeta, Balakrishna Pothineni, Aditya Gupta
https://arxiv.org/abs/2505.24042

Advancing Digital Accessibility In Digital Pharmacy, Healthcare, And Wearable Devices: Inclusive Solutions for Enhanced Patient Engagement
Modern healthcare facilities demand digital accessibility to guarantee equal access to telemedicine platforms, online pharmacy services, and health monitoring devices that can be worn or are handy. With the rising call for the implementation of robust digital healthcare solutions, people with disabilities encounter impediments in their endeavor of managing and getting accustomed to these modern technologies owing to insufficient accessibility features. The paper highlights the role of comprehen…

Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation?
Ayush Roy, Samin Enam, Jun Xia, Vishnu Suresh Lokhande, Won Hwa Kim
https://arxiv.org/abs/2507.19575

Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation?
Data scarcity is a major challenge in medical imaging, particularly for deep learning models. While data pooling (combining datasets from multiple sources) and data addition (adding more data from a new dataset) have been shown to enhance model performance, they are not without complications. Specifically, increasing the size of the training dataset through pooling or addition can induce distributional shifts, negatively affecting downstream model performance, a phenomenon known as the "Data Ad…

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes
Jun Kang Yap, Vishnu Monn Baskaran, Wen Shan Tan, Ze Yang Ding, Hao Wang, David L. Dowe
https://arxiv.org/abs/2507.15307

Joint Optimisation of Electric Vehicle Routing and Scheduling: A Deep Learning-Driven Approach for Dynamic Fleet Sizes
Electric Vehicles (EVs) are becoming increasingly prevalent nowadays, with studies highlighting their potential as mobile energy storage systems to provide grid support. Realising this potential requires effective charging coordination, which are often formulated as mixed-integer programming (MIP) problems. However, MIP problems are NP-hard and often intractable when applied to time-sensitive tasks. To address this limitation, we propose a deep learning assisted approach for optimising a day-ah…

Near-Discovery Observations of Interstellar Comet 3I/ATLAS with the NASA Infrared Telescope Facility
Theodore Kareta (Villanova U.), Chansey Champagne (NAU), Lucas McClure (NAU), Joshua Emery (NAU), Benjamin N. L. Sharkey (UMD), James M. Bauer (UMD), Michael Connelly (UH/IRTF), John Rayner (UH/IRTF), Cristina Thomas (NAU), Vishnu Reddy (UAz), Megan Firgard (UCF)

Near-Discovery Observations of Interstellar Comet 3I/ATLAS with the NASA Infrared Telescope Facility
Interstellar Objects are comets and asteroids that formed around other stars but were ejected before they could accrete into exoplanets. They therefore represent a rare opportunity to compare the building blocks of planets in the Solar System to those in other stellar systems. The third Interstellar Object, 3I/ATLAS, is the newest, brightest, potentially largest, and fastest member of this population. We report observations of 3I/ATLAS taken on 2025 July 3 and 4 with the NASA Infrared Telescope…

Transient segregation of bi-disperse granular mixtures in a periodic chute flow
Soniya Kumawat, Vishnu Kumar Sahu, Anurag Tripathi
https://arxiv.org/abs/2507.04025

Transient segregation of bi-disperse granular mixtures in a periodic chute flow
Transient size segregation of a bi-disperse granular mixture flowing over a periodic chute is studied using the Discrete Element Method and continuum simulations. A recently developed particle force-based size segregation model is used to predict the time-dependent flow properties of binary mixtures starting from rest. A two-way coupled continuum model that solves the momentum balance and convection-diffusion equations by incorporating the mixture segregation model along with the generalized in…

Rapid cardiac activation prediction for cardiac resynchronization therapy planning using geometric deep learning
Ehsan Naghavi, Haifeng Wang, Vahid Ziaei Rad, Julius Guccione, Ghassan Kassab, Vishnu Boddeti, Seungik Baek, Lik-Chuan Lee
https://arxiv.org/abs/2506.08987

Rapid cardiac activation prediction for cardiac resynchronization therapy planning using geometric deep learning
Cardiac resynchronization therapy (CRT) is a common intervention for patients with dyssynchronous heart failure, yet approximately one-third of recipients fail to respond due to suboptimal lead placement. Identifying optimal pacing sites remains challenging, largely due to patient-specific anatomical variability and the limitations of current individualized planning strategies. In a step towards constructing an in-silico approach to help address this issue, we develop two geometric deep learnin…

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers
Jun Kang Yap, Vishnu Monn Baskaran, Wen Shan Tan, Ze Yang Ding, Hao Wang, David L. Dowe
https://arxiv.org/abs/2507.15385

Transformer-based Deep Learning Model for Joint Routing and Scheduling with Varying Electric Vehicle Numbers
The growing integration of renewable energy sources in modern power systems has introduced significant operational challenges due to their intermittent and uncertain outputs. In recent years, mobile energy storage systems (ESSs) have emerged as a popular flexible resource for mitigating these challenges. Compared to stationary ESSs, mobile ESSs offer additional spatial flexibility, enabling cost-effective energy delivery through the transportation network. However, the widespread deployment of …