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Author

M. Suleiman

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Open access Sep 2026

An explainable CNN–BiLSTM–Random Forest framework for epidemic warning-signal and disease-status classification

This study evaluates an explainable ensemble in which Convolutional Neural Network and Bidirectional Long Short-Term Memory feature extractors feed a Random Forest classifier, applied under one architectural template to two independent tasks: labelling Ebola-related tweets as symptom-bearing warning signals and labelli...

F. O. Muhammed, M. Suleiman, S. Abdullahi et al. · 0 citations
Review

A Survey of Hallucinations in Multimodal Large Language Models with Mitigation Strategies

This survey provides a comprehensive treatment of the field across five interconnected dimensions, proposing a unified five-class taxonomy that organizes hallucinations by their failure mode: object, attribute, relational, factual, factual, and reasoning.

A. O. Ogar, Joshua Abah, M. Suleiman et al. · 0 citations

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