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Alyoubi, A.A. and Alyoubi, B.A. (2025) Interpretable Multimodal Emotion Recognition Using Optimized Transformer Model with SHAP-Based Transparency. The Journal of Supercomputing, 81, Article No. 1044.
https://doi.org/10.1007/s11227-025-07515-0

has been cited by the following article:

• TITLE: Empathetic Agents in Generative AI Applications

  AUTHORS: Siow Yen Chong, Aishwar Gupta, Vijay K. Madisetti

  KEYWORDS: Emotion-Aware AI, Conversational Agents, Sentiment Analysis, Retrieval-Augmented Generation, Large Language Models

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.19 No.3, March 11, 2026

  ABSTRACT: We present EAGAA, an emotionally intelligent agent integrating real-time sentiment and emotion analysis with Retrieval-Augmented Generation (RAG) to deliver empathetic customer support at scale. EAGAA employs a multilayered pipeline: SentimentAgent, ManagerAgent, TaskAgents, ResponseGenerator, and SessionManager to adapt tone and personality dynamically. In extensive evaluations including 100 simulated sessions and a 200-user survey, EAGAA achieved a 77% F1 on emotion benchmarks, 98.6% top-1 retrieval accuracy, sub-second median latency, and a 4.85/5 CSAT score, outperforming baselines by over 20%. This work demonstrates the viability of human-like, scalable emotion-aware AI.