Priyanka Kumar

Priyanka Kumar

Fault detection in automotive engine systems is one of the most promising research areas. Several works have been done in the field of model-based fault diagnosis. Many researchers have discovered more advanced statistical methods and algorithms for better fault detection on any automotive dynamic engine system. The gas turbines/diesel engines produce highly complex and huge data which are highly non-linear. So, researchers should come up with an automated system that is more resilient and robust enough to handle this huge, complex data in highly non-linear dynamic automotive systems. Here, I present an AI-based fault classification and prediction model in the diesel engine that can be applied to any highly non-linear dynamic automotive system. The main contribution of this paper is the AI-based Transformer fault classification and prediction model in the diesel engine concerning the worldwide harmonic light vehicle test procedure (WLTP) driving cycle. This model used 27 input dimensions, 64 hidden dimensions with 2 layers, and 9 heads to create a classifier with 12 output heads (one for fault-free data and 11 different fault types). This model was trained on the UTSA Arc High-Performance Compute (HPC) cluster with 5 NVIDIA V100 GPUs, 40-core CPUs, and 384GB RAM and achieved 70.01 % accuracy on a held test set.

Bryan Lim, Roman Huerta, Alejandro Sotelo et al.

Sophisticated phishing attacks have emerged as a major cybersecurity threat, becoming more common and difficult to prevent. Though machine learning techniques have shown promise in detecting phishing attacks, they function mainly as "black boxes" without revealing their decision-making rationale. This lack of transparency erodes the trust of users and diminishes their effective threat response. We present EXPLICATE: a framework that enhances phishing detection through a three-component architecture: an ML-based classifier using domain-specific features, a dual-explanation layer combining LIME and SHAP for complementary feature-level insights, and an LLM enhancement using DeepSeek v3 to translate technical explanations into accessible natural language. Our experiments show that EXPLICATE attains 98.4 % accuracy on all metrics, which is on par with existing deep learning techniques but has better explainability. High-quality explanations are generated by the framework with an accuracy of 94.2 % as well as a consistency of 96.8\% between the LLM output and model prediction. We create EXPLICATE as a fully usable GUI application and a light Chrome extension, showing its applicability in many deployment situations. The research shows that high detection performance can go hand-in-hand with meaningful explainability in security applications. Most important, it addresses the critical divide between automated AI and user trust in phishing detection systems.

David Mike-Ewewie, Panhapiseth Lim, Priyanka Kumar

Accurate and automated sea ice classification is important for climate monitoring and maritime safety in the Arctic. While Synthetic Aperture Radar (SAR) is the operational standard because of its all-weather capability, it remains challenging to distinguish morphologically similar ice classes under severe class imbalance. Rather than claiming a fully validated multimodal system, this paper establishes a trustworthy SAR only baseline that future fusion work can build upon. Using the AI4Arctic/ASIP Sea Ice Dataset (v2), which contains 461 Sentinel-1 scenes matched with expert ice charts, we combine full-resolution Sentinel-1 Extra Wide inputs, leakage-aware stratified patch splitting, SIGRID-3 stage-of-development labels, and training-set normalization to evaluate Vision Transformer baselines. We compare ViT-Base models trained with cross entropy and weighted cross-entropy against a ViT-Large model trained with focal loss. Among the tested configurations, ViT-Large with focal loss achieves 69.6% held-out accuracy, 68.8% weighted F1, and 83.9% precision on the minority Multi-Year Ice class. These results show that focal-loss training offers a more useful precision-recall trade-off than weighted cross-entropy for rare ice classes and establishes a cleaner baseline for future multimodal fusion with optical, thermal, or meteorological data.

Phillip Driscoll, Priyanka Kumar

AI, especially Large Language Models (LLMs) like ChatGPT, have rapidly developed and gained widespread adoption in the past five years, shifting user preference from traditional search engines. However, the generative nature of LLMs raises concerns about presenting misinformation as fact. To address this, we developed a web-based application that helps K-12 students enhance critical thinking by identifying misleading information in LLM responses about major historical figures. In this paper, we describe the implementation and design details of the DoYouTrustAI tool, which can be used to provide an interactive lesson which teaches students about the dangers of misinformation and how believable generative AI can make it seem. The DoYouTrustAI tool utilizes prompt engineering to present the user with AI generated summaries about the life of a historical figure. These summaries can be either accurate accounts of that persons life, or an intentionally misleading alteration of their history. The user is tasked with determining the validity of the statement without external resources. Our research questions for this work were:(RQ1) How can we design a tool that teaches students about the dangers of misleading information and of how misinformation can present itself in LLM responses? (RQ2) Can we present prompt engineering as a topic that is easily understandable for students? Our findings highlight the need to correct misleading information before users retain it. Our tool lets users select familiar individuals for testing to reduce random guessing and presents misinformation alongside known facts to maintain believability. It also provides pre-configured prompt instructions to show how different prompts affect AI responses. Together, these features create a controlled environment where users learn the importance of verifying AI responses and understanding prompt engineering.