1 citations · 1 across the 10 of their papers we have counts for
10 papers
Where Should Knowledge Enter? A Layered Framework for Knowledge Infusion in Multimodal Iterative Generative Model
Renjith Prasad, Chathurangi Shyalika, Anushka Pawar +2
Multimodal generative models produce fluent outputs but remain unreliable when generation must respect structured, domain-specific, or safety-critical knowledge. Existing methods i…
Hard to See, Hard to Label: Generative and Symbolic Acquisition for Subtle Visual Phenomena
Renjith Prasad, Rishabh Sharma, Andrew E. Shao +8
Subtle visual anomalies such as hairline cracks, sub-millimeter voids, and low-contrast inclusions are structurally atypical yet visually ambiguous, making them both difficult to a…
IndustryAssetEQA: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance
Chathurangi Shyalika, Dhaval Patel, Amit Sheth
Industrial maintenance environments increasingly rely on AI systems to assist operators in understanding asset behavior, diagnosing failures, and evaluating interventions. Although…
PAL: Personal Adaptive Learner
Megha Chakraborty, Darssan L. Eswaramoorthi, Madhur Thareja +5
AI-driven education platforms have made some progress in personalisation, yet most remain constrained to static adaptation--predefined quizzes, uniform pacing, or generic feedback-…
CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturing
Chathurangi Shyalika, Aryaman Sharma, Fadi El Kalach +4
Modern manufacturing environments demand not only accurate predictions but also interpretable insights to process anomalies, root causes, and potential interventions. Existing AI s…
SmartPilot: A Multiagent CoPilot for Adaptive and Intelligent Manufacturing
Chathurangi Shyalika, Renjith Prasad, Alaa Al Ghazo +4
In the dynamic landscape of Industry 4.0, achieving efficiency, precision, and adaptability is essential to optimize manufacturing operations. Industries suffer due to supply chain…
NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelines
Chathurangi Shyalika, Renjith Prasad, Fadi El Kalach +4
In modern assembly pipelines, identifying anomalies is crucial in ensuring product quality and operational efficiency. Conventional single-modality methods fail to capture the intr…
Time Series Foundational Models: Their Role in Anomaly Detection and Prediction
Chathurangi Shyalika, Harleen Kaur Bagga, Ahan Bhatt +3
Time series foundational models (TSFM) have gained prominence in time series forecasting, promising state-of-the-art performance across various applications. However, their applica…
AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines
Renjith Prasad, Chathurangi Shyalika, Ramtin Zand +4
Anomaly detection in manufacturing pipelines remains a critical challenge, intensified by the complexity and variability of industrial environments. This paper introduces AssemAI,…
Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing
Chathurangi Shyalika, Ruwan Wickramarachchi, Fadi El Kalach +2
Rare events are occurrences that take place with a significantly lower frequency than more common regular events. In manufacturing, predicting such events is particularly important…