Sustainable Development in Education Using Quantum-Classical Synergistic Fibroblast Dense Nested Convolutional Attention Network

Sustainable development in education is crucial for advancing global educational standards and addressing disparities. Existing methods for predicting sustainable development in education often struggle with data quality and complexity, which can limit their accuracy and applicability. This study aims to overcome these limitations. This study presents a novel approach to predicting Sustainable Development in Education using the quantum-classical synergistic fibroblast dense nested convolutional attention network (Qua-CSFib-DNCANet). In this, input educational data is sourced from the Europe and Central Asia dataset. Then, these educational data are pre-processed using the preprocessing methods within new adjusted min–max with decimal scaling and statistical column normalization (NAMDSSCN) include normalization of statistical column, normalization of decimal scaling, normalization of adjusted decimal scaling, min–max normalization, normalization of adjusted 1-min–max, normalization of adjusted 2-min–max, normalization of adjusted 3-min–max, and normalization of adjusted 4-Min–Max. Multiple discrete orthonormal S-transforms extract features by providing an efficient method to represent complex educational metrics. The Qua-CSFib-DNCANet mechanism is applied to accurately predict indicators of sustainable development in education. The performance of the suggested system is assessed using academic information from the Europe and Central Asia databases, and it runs on a Python platform. With an astounding 99.9% correctness and 99.8% recall, the Qua-CSFib-DNCANet model outperforms current techniques in terms of efficiency and shows promise for future development in the sector. This approach aims to provide deeper insights and improved predictions, addressing the challenges associated with educational data analysis and supporting strategic initiatives for educational sustainability.