Neena Goveas

Participatory citizenship plays a critical role in strengthening climate change resilience, particularly in the context of natural disasters such as floods and other hydrological extremes. Citizen-generated data shared through social media platforms offer valuable real-time insights that can complement traditional environmental monitoring systems. This study proposes a machine learning-based framework to analyze multilingual news data and global X (formerly known as Twitter) data that can complement street level sensor data for improved detection and understanding of extreme hydrological events: floods and landslides. The approach identifies and filters tweets related to hazards such as floods and contextualizes them with information extracted from news reports to enhance event characterization. In addition, sentiment and emotion analysis are applied to assess public reactions and perceived event intensity. By integrating physical event signals with societal responses, the method provides a broader perspective on disaster impacts and the effectiveness of emergency responses. The results highlight the potential of combining social media analytics and machine learning to support hydrological monitoring, enhance situational awareness, and contribute to more responsive disaster management strategies in the face of increasing climate-related risks.

Magnetic Resonance Imaging (MRI) plays a crucial role in detecting structural and functional abnormalities in the brains of epilepsy patients. Automating this process using advanced AI/ML techniques can reduce the burden on experts, be a training resource, and make diagnosis accessible and affordable to a larger population. However, models developed using imaging data have not achieved acceptable accuracy levels. One reason for this is the limited number of data points available and artifacts in the data capture mechanism of the available datasets. There is a need to develop a generalized model that can take as input diverse images with multiple conditions, which predicts epilepsy and identifies the impacted brain regions. In this work, we present the design of deep-learning models capable of predicting epilepsy using 3D brain Magnetic Resonance Imaging (MRI) scans. Five deep learning models were developed to classify 3D MRI scans to identify specific brain regions involved in Hippocampal Sclerosis, Dysembryoplastic Neuroepithelial Tumors, Cavernomas, Focal Cortical Dysplasia, and other epileptic-related conditions. We find that our trained models predict epilepsy with over 91% accuracy on FLAIR images and nearly 100% accuracy on T1-weighted (T1-w) images. Our results show that deep learning architectures in 3D are highly effective in detecting epilepsy and identifying relevant pathological regions. These findings highlight not only the diagnostic accuracy of the models but also their potential utility in aiding clinical decision-making.

A core challenge in AI-guided autonomy is enabling agents to navigate realistically and effectively in previously unseen environments based on natural language commands. We propose UAV-VLN, a novel end-to-end Vision-Language Navigation (VLN) framework for Unmanned Aerial Vehicles (UAVs) that seamlessly integrates Large Language Models (LLMs) with visual perception to facilitate human-interactive navigation. Our system interprets free-form natural language instructions, grounds them into visual observations, and plans feasible aerial trajectories in diverse environments. UAV-VLN leverages the common-sense reasoning capabilities of LLMs to parse high-level semantic goals, while a vision model detects and localizes semantically relevant objects in the environment. By fusing these modalities, the UAV can reason about spatial relationships, disambiguate references in human instructions, and plan context-aware behaviors with minimal task-specific supervision. To ensure robust and interpretable decision-making, the framework includes a cross-modal grounding mechanism that aligns linguistic intent with visual context. We evaluate UAV-VLN across diverse indoor and outdoor navigation scenarios, demonstrating its ability to generalize to novel instructions and environments with minimal task-specific training. Our results show significant improvements in instruction-following accuracy and trajectory efficiency, highlighting the potential of LLM-driven vision-language interfaces for safe, intuitive, and generalizable UAV autonomy.

Efficient use and timely availability of healthcare resourcesplay a critical role in today’s society. A key factor in managing theseresources is their automated and flexible allocation and deallocation. Wepresent a formal approach to model the medical resource allocation process as timed automata. This approach aims to improve treatment pro-cesses and alleviate staff workload by allocating resources strategically for a specific duration and releasing them synchronously. We ensure that the model satisfies important domain-specific requirements and is structured in a modular fashion. Further, we demonstrate the applicability of our approach in a clinical scenario involving combinations of patients,caregivers, and medical resources. In this scenario, we verify the cor-rectness of this model’s temporal properties. Finally, we show how ourmodel can be extended with new components without compromising itsessential properties.

The rapid advancement of technology has led to a significant increase in electronic waste, including discarded mobile phones. These old mobile phones can still be valuable resources for creating Internet of Things (IoT) systems through upcycling. In this survey we explore the use of upcycled mobile phones in IoT system development. We look at the sensors present on most mobile phones and their usage. These sensors are inbuilt, integrated, and ready for use. Using these saves efforts as compared to any system development from scratch, which involves procuring, assembling, and testing a bunch of sensors in isolation and together. We look at some efforts to create an operating system-based environment to aid application development on upcycled mobile phones. We look at some projects using mobile phone sensors and some of the applications developed as part of these efforts. We consider some successful deployments and look at these efforts' benefits. One benefit that arises is the possibility of crowd-sourcing-based data gathering using upcycled mobile phones. This is possible because of the availability of mobile phones with many participants. We look at use cases for the IoT systems using mobile phones in important fields like healthcare and agriculture. We examine the benefits, challenges, and potential avenues for future research on upcycled mobile phone use cases.