1 citations · 2 across the 10 of their papers we have counts for
10 papers
Human-Centered Explainable AI for TinyML Edge Devices: A Pareto-Based Selection Framework with LLM-Guided Design
Zeinab Dehghani, Dhavalkumar Thakker, Koorosh Aslansefat +4
Edge Artificial Intelligence (Edge AI) enables the deployment of AI models directly on local edge devices, while such deployments are subject to strict resource constraints, partic…
ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI
Mohadeseh Mollapour, Koorosh Aslansefat, Zeinab Dehghani +3
Concept-based explainable artificial intelligence (AI) can make model reasoning more human-understandable, but concept-level outputs are not automatically trustworthy. We introduce…
Evaluating a Multi-Agent Voice-Enabled Smart Speaker for Care Homes: A Safety-Focused Framework
Zeinab Dehghani, Rameez Raja Kureshi, Koorosh Aslansefat +6
Artificial intelligence (AI) is increasingly being explored in health and social care to reduce administrative workload and allow staff to spend more time on patient care. This pap…
EcoFair: Energy-Efficient Inference Routing for Edge AI under Data Degradation
Mostafa Anoosha, Dhavalkumar Thakker, Kuniko Paxton +4
Medical edge-AI systems must operate under a difficult tension: delivering reliable diagnostic inference while running on devices with limited battery capacity, memory, and compute…
Advancing Symbolic Integration in Large Language Models: Beyond Conventional Neurosymbolic AI
Maneeha Rani, Bhupesh Kumar Mishra, Dhavalkumar Thakker
LLMs have demonstrated highly effective learning, human-like response generation,and decision-making capabilities in high-risk sectors. However, these models remain black boxes bec…
A Knowledge Graph and a Tripartite Evaluation Framework Make Retrieval-Augmented Generation Scalable and Transparent
Olalekan K. Akindele, Bhupesh Kumar Mishra, Kenneth Y. Wertheim
Large Language Models (LLMs) have significantly enhanced conversational Artificial Intelligence(AI) chatbots; however, domain-specific accuracy and the avoidance of factual inconsi…
Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILE
Zahra Zehtabi Sabeti Moghaddam, Zeinab Dehghani, Maneeha Rani +4
Generative AI, such as Large Language Models (LLMs), has achieved impressive progress but still produces hallucinations and unverifiable claims, limiting reliability in sensitive d…
Developing a Responsible AI Framework for Healthcare in Low Resource Countries: A Case Study in Nepal and Ghana
Hari Krishna Neupane, Bhupesh Kumar Mishra
The integration of Artificial Intelligence (AI) into healthcare systems in low-resource settings, such as Nepal and Ghana, presents transformative opportunities to improve personal…
Enhancing Trustworthiness and Minimising Bias Issues in Leveraging Social Media Data for Disaster Management Response
Samia Abid, Bhupesh Kumar Mishra, Dhavalkumar Thakker +1
Disaster events often unfold rapidly, necessitating a swift and effective response. Developing action plans, resource allocation, and resolution of help requests in disaster scenar…
Crafting Tomorrow's Evaluations: Assessment Design Strategies in the Era of Generative AI
Rajan Kadel, Bhupesh Kumar Mishra, Samar Shailendra +3
GenAI has gained the attention of a myriad of users in almost every profession. Its advancement has had an intense impact on education, significantly disrupting the assessment desi…