قارن الطرق

	شرح الذاكرة طويلة المدى (LSTM)	وحدة GRU القابلة للتفسير
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2017–2019	2014 (GRU); 2016–2017 (XAI integration)
صاحب الطريقة	Lundberg & Lee (SHAP); Ribeiro et al. (LIME); community synthesis	Cho, K. et al. (GRU); explainability layer via Lundberg & Lee (SHAP) and Ribeiro et al. (LIME)
النوع	Interpretable deep learning (post-hoc explainability)	Recurrent neural network with post-hoc or attention-based interpretability
المصدر التأسيسي	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. Proceedings of EMNLP 2014, 1724–1734.
الأسماء البديلة	XAI-LSTM, interpretable LSTM, LSTM with SHAP, transparent LSTM	XAI-GRU, Interpretable GRU, GRU with explainability, Transparent GRU
ذات صلة	5	5
الملخص	Explainable LSTM pairs a trained Long Short-Term Memory network with post-hoc interpretability techniques — chiefly SHAP, LIME, integrated gradients, or attention visualization — to reveal which time steps, tokens, or features drive each prediction. It bridges the accuracy of recurrent deep learning with the transparency demanded by high-stakes domains such as clinical decision support, fraud detection, and regulatory compliance.	Explainable GRU pairs the Gated Recurrent Unit, a compact and efficient recurrent architecture, with explainability techniques such as SHAP, LIME, or attention weighting to reveal which time steps and features drove each prediction. It brings interpretability to sequential modelling without sacrificing the GRU's ability to capture temporal dependencies.