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Neural Computing for Advanced Applications
Neural Computing for Advanced Applications
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This volume constitutes the refereed proceedings of the 7th International Conference on Neural Computing for Advanced Applications, NCAA 2026, held in Osaka, Japan during July 9-12, 2026.The 45 full papers presented in these proceedings were carefully reviewed and selected from 99 submissions.The papers are organized in the following topical sections: Computational intelligence, nature-inspired optimizers, and their engineering applications; Deep learning-driven pattern recognition, computer vi…

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This volume constitutes the refereed proceedings of the 7th International Conference on Neural Computing for Advanced Applications, NCAA 2026, held in Osaka, Japan during July 9-12, 2026.

The 45 full papers presented in these proceedings were carefully reviewed and selected from 99 submissions.

The papers are organized in the following topical sections: Computational intelligence, nature-inspired optimizers, and their engineering applications; Deep learning-driven pattern recognition, computer vision and its industrial applications; Machine learning and deep learning for data mining and data-driven applications; Multimodal Deep Learning for Represeantation, Fusion and Applications; Natural language processing, knowledge graphs, recommender systems, and their applications and Neural network (NN) theory, NN-based control systems, neuro-system integration and engineering applications

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This volume constitutes the refereed proceedings of the 7th International Conference on Neural Computing for Advanced Applications, NCAA 2026, held in Osaka, Japan during July 9-12, 2026.

The 45 full papers presented in these proceedings were carefully reviewed and selected from 99 submissions.

The papers are organized in the following topical sections: Computational intelligence, nature-inspired optimizers, and their engineering applications; Deep learning-driven pattern recognition, computer vision and its industrial applications; Machine learning and deep learning for data mining and data-driven applications; Multimodal Deep Learning for Represeantation, Fusion and Applications; Natural language processing, knowledge graphs, recommender systems, and their applications and Neural network (NN) theory, NN-based control systems, neuro-system integration and engineering applications

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