Literature review reveals gaps in matrix theory applications within digital image processing and data security.
Matrix, as a fundamental mathematical concept, have crucial applications in computer science. This study analyzes: the basic structure of matrices (definition, types, addition-multiplication-inverse operations), and their implementation in computational fields, including: digital image processing (pixel representation, image transformation), intelligent systems (matrix-based neural networks), data security (matrix encryption), and network optimization (routing algorithms). A combination of literature review and case analysis reveals that understanding matrices is the backbone of modern computing, particularly in the development of machine learning and computer vision. Findings indicate a gap between conventional matrix theory and the demands of large-scale computing in industry. This study recommends integrating examples of computer science applications into matrix education to prepare digital talent.
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Aprijal et al. (2025) studied this question.
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