Institution profile

Phenikaa University

Academic institutionafrica · eg
Official website
Research library16linked papers
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Selected work

Representative Papers

Conversational versus Dashboard Explainable AI for UAV Intrusion Detection: An Empirical Study of Operator Trust and Reliance

Aug 10, 2026

This study addresses the challenge of insufficient explainability in machine learning–based intrusion detection for unmanned aerial vehicle networks, where the black-box nature of models and high-dimensional multimodal data hinder effective operator decision-making under traditional static visualizations. The work proposes the first integration of conversational Explainable AI (XAI) into this domain, implementing an interactive interface powered by a large language model. Through a controlled user study, it investigates how such an approach influences operators’ comprehension, trust, and reliance behaviors during post-hoc auditing. Findings indicate that while conversational XAI is perceived as more useful, it may reduce operators’ self-reliance, thereby increasing the risk of over-reliance. This reveals a critical trade-off between usability and appropriate dependence in XAI interaction design and motivates a design paradigm that balances ease of use with cognitive forcing mechanisms.

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Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs

Jul 22, 2026

Current multimodal large language models for ECG diagnosis suffer from limited interpretability, susceptibility to hallucinations, and deviations from clinical guidelines, undermining their clinical reliability. To address these issues, this work proposes a knowledge-anchored multimodal framework that, for the first time, distills authoritative ECG guidelines into structured explanatory knowledge offline and integrates this as a fixed module within the prompting pipeline. The approach combines CNN-based ECG feature extraction with Grad-CAM to produce class-specific heatmaps and factual evidence packages, guiding the model to generate structured diagnostic reports aligned with clinical standards. Evaluated on the PTB-XL test set, the method improves BERTScore for the impression section from 0.818 to 0.953, significantly enhancing guideline adherence, semantic quality, and interpretability while maintaining strong classification performance.

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Recent publications

Latest Papers

Conversational versus Dashboard Explainable AI for UAV Intrusion Detection: An Empirical Study of Operator Trust and Reliance

Aug 10, 2026

This study addresses the challenge of insufficient explainability in machine learning–based intrusion detection for unmanned aerial vehicle networks, where the black-box nature of models and high-dimensional multimodal data hinder effective operator decision-making under traditional static visualizations. The work proposes the first integration of conversational Explainable AI (XAI) into this domain, implementing an interactive interface powered by a large language model. Through a controlled user study, it investigates how such an approach influences operators’ comprehension, trust, and reliance behaviors during post-hoc auditing. Findings indicate that while conversational XAI is perceived as more useful, it may reduce operators’ self-reliance, thereby increasing the risk of over-reliance. This reveals a critical trade-off between usability and appropriate dependence in XAI interaction design and motivates a design paradigm that balances ease of use with cognitive forcing mechanisms.

0 citationsRead paper

Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs

Jul 22, 2026

Current multimodal large language models for ECG diagnosis suffer from limited interpretability, susceptibility to hallucinations, and deviations from clinical guidelines, undermining their clinical reliability. To address these issues, this work proposes a knowledge-anchored multimodal framework that, for the first time, distills authoritative ECG guidelines into structured explanatory knowledge offline and integrates this as a fixed module within the prompting pipeline. The approach combines CNN-based ECG feature extraction with Grad-CAM to produce class-specific heatmaps and factual evidence packages, guiding the model to generate structured diagnostic reports aligned with clinical standards. Evaluated on the PTB-XL test set, the method improves BERTScore for the impression section from 0.818 to 0.953, significantly enhancing guideline adherence, semantic quality, and interpretability while maintaining strong classification performance.

0 citationsRead paper