2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 1 , PP: 114-125, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Muna Al-Saadi 1 , Bushra Al-Saadi 2 , Dheyauldeen Ahmed Farhan 3 , Oday Ali Hassen 4 *
Doi: https://doi.org/10.54216/JISIoT.180108
Deep studying architectures face fundamental demanding situations in balancing overall performance optimization, computational scalability, and operational interpretability. Current strategies show off an essential fragmentation: neural architecture search (NAS) techniques perform independently of interpretability requirements, while scalability answers remain detached from structure optimization pipelines. This disconnect hinders the improvement of a unified workflow from architecture layout to interpretable deployment. We endorse DeepOptiFrame, a TensorFlow/Keras-primarily based Python framework that combines three middle capabilities: (1) superior optimization algorithms (BOHB, Hyperband) with useful resource-restrained multi-objective search, (2) distributed training acceleration across GPU/GPU clusters via Horovod integration and blended-precision strategies, and (3) GPU-increased interpretability gear (SHAP, LIME) incorporated without delay into the education pipeline. Our framework demonstrates large experimental improvements: a 15-20% accuracy growth at the CIFAR-a hundred and ImageNet benchmarks compared to today's baselines, a 65% education speedup whilst scaled to eight GPUs with close to-linear performance, and a 30% development in interpretability reliability, as measured via the Mean Confidence Decrease metric. This implementation additionally reduces reminiscence intake via forty% throughout gradient checkpoints even as keeping numerical balance. These advances establish a new paradigm for coherent deep learning development, simultaneously improving overall performance, scalability, and transparency inside unified workflow surroundings.
Neural Architecture Search , Explainable AI , Distributed Deep Learning , Model Optimization , Interpretability Metrics
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