@inproceedings{yassin-a-hakim-khalil-2026-free,
title = "Free-{G}aza at {N}akba{A}rchive{C}lassifier Shared Task: Towards Distinguishing the Destructive Effect of Nakba: {N}akba{I}mage Classification Using Artificial Intelligence Techniques",
author = "Yassin, Nisreen I. R. and
A. Hakim Khalil, Enas",
editor = "Jarrar, Mustafa and
El-Haj, Mo and
Haddad, Amal and
Atiani, Serin and
Abudalfa, Shadi and
Regier, Terry and
Rayson, Paul and
Sima{'}an, Khalil and
Mansour, Camille",
booktitle = "Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.nakbanlp-1.17/",
doi = "10.63317/59i3kmpytngj",
pages = "133--136",
abstract = "The accounts of the continuing Palestinian Nakba encompass considerable significance. Over the course of the three years of the conflict, millions of photos from social media have been preserved. The preservation and classification of these data through artificial intelligence tools are essential to guarantee their availability, accessibility, and applicability. This paper presents a highly optimized, resource-constrained machine learning pipeline for binary image classification. The system is designed for the NakbaArchiveClassifier Shared Task 2026, which aims to distinguish between destroyed infrastructural images and intact infrastructural images. The system depends on two lightweight EfficientNetB0 networks to build a weighted ensemble system. Using strict hardware limitations of 2GB GPU VRAM, the system achieves an F1-score of 84.16{\%}, which ranked 9th on the leaderboard."
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%0 Conference Proceedings
%T Free-Gaza at NakbaArchiveClassifier Shared Task: Towards Distinguishing the Destructive Effect of Nakba: NakbaImage Classification Using Artificial Intelligence Techniques
%A Yassin, Nisreen I. R.
%A A. Hakim Khalil, Enas
%Y Jarrar, Mustafa
%Y El-Haj, Mo
%Y Haddad, Amal
%Y Atiani, Serin
%Y Abudalfa, Shadi
%Y Regier, Terry
%Y Rayson, Paul
%Y Sima’an, Khalil
%Y Mansour, Camille
%S Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F yassin-a-hakim-khalil-2026-free
%X The accounts of the continuing Palestinian Nakba encompass considerable significance. Over the course of the three years of the conflict, millions of photos from social media have been preserved. The preservation and classification of these data through artificial intelligence tools are essential to guarantee their availability, accessibility, and applicability. This paper presents a highly optimized, resource-constrained machine learning pipeline for binary image classification. The system is designed for the NakbaArchiveClassifier Shared Task 2026, which aims to distinguish between destroyed infrastructural images and intact infrastructural images. The system depends on two lightweight EfficientNetB0 networks to build a weighted ensemble system. Using strict hardware limitations of 2GB GPU VRAM, the system achieves an F1-score of 84.16%, which ranked 9th on the leaderboard.
%R 10.63317/59i3kmpytngj
%U https://aclanthology.org/2026.nakbanlp-1.17/
%U https://doi.org/10.63317/59i3kmpytngj
%P 133-136
Markdown (Informal)
[Free-Gaza at NakbaArchiveClassifier Shared Task: Towards Distinguishing the Destructive Effect of Nakba: NakbaImage Classification Using Artificial Intelligence Techniques](https://aclanthology.org/2026.nakbanlp-1.17/) (Yassin & A. Hakim Khalil, NakbaNLP 2026)
ACL