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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

F. O. Muhammed

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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 Open access Aug 2026

Multimodal Large Language Models for Autonomous Driving: A Comprehensive Survey of Perception, Reasoning, Planning, and Safety Assurance

This 2026 survey synthesises 39 primary sources selected from an initial corpus of 274 candidate records screened over 2020-2026, organises the field around a five-role pipeline taxonomy (perception, prediction, planning, control, and human-machine interaction), and compares six representative driving MLLMs on accuracy...

A. O. Ogar, Joshua Abah, Ali Muhammad et al. · 0 citations
Review Open access Jul 2026

Optimising Domain-Specific Neuron Activation for Efficient Multimodal Language Understanding In Cloud AI Systems

A unified four-quadrant taxonomy of efficiency strategies is proposed, an integrated future-research agenda built on three converging innovations: adaptive cross-modal attention re-weighting, knowledge-injection pathways, and sparse domain-conditioned neuron gating are outlined, and the cloud-aware evaluation framework...

Olom Ogar Austin, Joshua Abah, Ali Muhammad 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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