Pattern recognition with limited data: an AI model inspired by BCR-Net and SwitchNet
Ali Sever · Applied Computing and Informatics · 2025
Purpose This research aims to develop a novel deep learning framework that effectively operates with limited data by integrating principles from inverse problem mathematics and neural networks. Inspired by BCR-Net and SwitchNet, the study introduces a pattern recognition system (PRS) that enhances traditional deep learning approaches through a data-driven, flexible architecture. By combining inverse problem methodologies with advanced neural network modules, this model seeks to overcome the challenges posed by small datasets, providing superior performance in various pattern recognition tasks compared to existing methods. Design/methodology/approach This study introduces a deep learning model tailored for small datasets by integrating inverse problem (IP) frameworks with neural network modules. Drawing on principles from physics and mathematics, the approach combines BCR-Net and SwitchNet architectures to enhance data augmentation in deep neural networks (DNNs). The model leverages IP’s ability to represent high-dimensional functions flexibly, using data to guide the selection of IP parameters. The effectiveness of the proposed Pattern Recognition System (PRS) is validated through a series of experiments on diverse pattern recognition tasks. The PRS performance is then benchmarked against traditional methods to demonstrate its superior capability in handling limited data scenarios. Findings The research demonstrates that integrating inverse problem (IP) frameworks with deep neural networks (DNNs) significantly enhances their performance on small datasets. The proposed Pattern Recognition System (PRS), inspired by BCR-Net and SwitchNet, effectively utilizes IP for flexible data augmentation, guiding parameter selection based on available data. Experimental validation across various pattern recognition tasks reveals that PRS outperforms traditional deep learning methods, showcasing superior accuracy and robustness in limited data scenarios. This novel approach underscores the potential of combining advanced neural architectures with mathematical principles to overcome the challenges posed by small datasets in deep learning. Research limitations/implications While the proposed Pattern Recognition System (PRS) demonstrates significant improvements in handling small datasets, it may face limitations in generalizability across highly diverse or significantly larger data contexts. The reliance on inverse problem (IP) frameworks and their parameterization might also introduce computational complexity and require domain-specific expertise for optimal implementation. Additionally, the integration of IP with neural networks, though beneficial for data augmentation, might limit scalability when transitioning to real-time applications or larger-scale datasets. Future research should explore broader applicability, the simplification of the model for practical deployment and further refinement to enhance efficiency and scalability across different domains. Practical implications The research offers a transformative approach for organizations dealing with small datasets by providing a robust deep learning framework that excels in limited data environments. The integration of inverse problem (IP) frameworks with neural network modules allows the proposed Pattern Recognition System (PRS) to achieve superior performance in tasks that typically struggle with insufficient data. This advancement enables businesses and institutions to leverage their existing small-scale datasets more effectively, enhancing predictive accuracy and decision-making processes. Additionally, the model’s flexibility and enhanced data augmentation capabilities make it suitable for a wide range of applications, from healthcare diagnostics to financial forecasting, where data scarcity is a common challenge. Social implications This research offers significant social benefits by democratizing the power of deep learning for communities and organizations with limited data resources. The proposed Pattern Recognition System (PRS) enables effective AI solutions even in data-scarce environments, fostering inclusivity and equal access to advanced technological capabilities. This can empower small businesses, non-profits and underserved sectors to leverage AI for social good, improving outcomes in healthcare, education and community services where data is often limited. By reducing the dependency on large datasets, this approach can help bridge the digital divide, ensuring more equitable access to the benefits of AI advancements across diverse social landscapes. Originality/value This research introduces a groundbreaking approach by seamlessly integrating principles from physics and inverse problem (IP) mathematics into deep learning (DL) models, specifically designed to thrive in small data environments. Unlike traditional DL methods that depend on large datasets, the proposed model – drawing inspiration from BCR-Net and SwitchNet – offers a novel architecture that enhances data augmentation and parameter selection through IP frameworks. This innovative combination not only expands the applicability of DL to data-scarce contexts but also demonstrates superior performance in pattern recognition tasks. The study’s originality lies in its unique fusion of advanced mathematical principles with cutting-edge neural network design, providing significant value to fields constrained by limited data.