MS
Mostafa Farouk Senussi
cs.IRDeep learningAttention mechanismsAutomated livestock monitoringCattle disease diagnosisConvNeXtDefect classificationEfficientNetFeature Pyramid Network (FPN)Industrial inspection
On Valency
published · living versionsW_mxqaheb8·v1 · currentpublished
MEMORA: A Memory-Enhanced Multimodal Committee Reranking Agent for Reasoning-Intensive Retrieval
with Mohamed Mahmoud, Mahmoud Abdalla, Mahmoud SalahEldin Kasem, Abdelrahman Abdallah +1
1 version
Preprints & journals
7 papers in the corpus · 2025–2026MEMORA: A Memory-Enhanced Multimodal Committee Reranking Agent for Reasoning-Intensive Retrieval2610.25282v1 · Mohamed Mahmoud, Mostafa Farouk Senussi, Mahmoud Abdalla et al.2026 · 0 citationsarXivon Valency
Robust multi-class lumpy skin disease diagnosis for practical livestock applications.42642454 · Senussi, Mostafa Farouk, Heikal, Abdelrahman S, Abdelbasset, Asmaa Gamal et al.2026 · 0 citationsScientific reports. 2026;16(1)
MARVEL: Multimodal Adaptive Reasoning-intensiVe Expand-rerank and retrievaL2604.07079v1 · Mahmoud SalahEldin Kasem, Mohamed Mahmoud, Mostafa Farouk Senussi et al.2026 · 0 citationsarXiv
HIVE: Query, Hypothesize, Verify An LLM Framework for Multimodal Reasoning-Intensive Retrieval2604.07220v1 · Mahmoud Abdalla, Mahmoud SalahEldin Kasem, Mohamed Mahmoud et al.2026 · 0 citationsarXiv
MM-BRIGHT: A Multi-Task Multimodal Benchmark for Reasoning-Intensive Retrieval2601.09562v3 · Abdelrahman Abdallah, Mohamed Darwish Mounis, Mahmoud Abdalla et al.2026 · 0 citationsarXiv
Attention-guided hybrid learning for accurate defect classification in manufacturing environments.41381637 · Kasem, Mahmoud SalahEldin, Mahmoud, Mohamed, Senussi, Mostafa Farouk et al.2026 · 9 citationsScientific reports. 2025;16(1):2018
Learning to remove occlusions in light field images using multiscale receptive fields and feature pyramid networks.41125707 · Senussi, Mostafa Farouk, Abdalla, Mahmoud, Kasem, Mahmoud SalahEldin et al.2026 · 20 citationsScientific reports. 2025;15(1):36978
Career total: 24 works. 7 are in this corpus.
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