@Article{M-10447,
AUTHOR = {Gotur, Goutam and Saravana Kumar, Dr},
TITLE = {A Comprehensive Analysis of Linear Algebra-Based Performance Modeling and Enterprise Invoice Processing},
JOURNAL = {Scientific Research Journal of Science, Engineering and Technology},
VOLUME = {4},
YEAR = {2026},
NUMBER = {1},
ARTICLE-NUMBER = {M-10447},
URL = {https://isrdo.org/journal/SRJSET/currentissue/a-comprehensive-analysis-of-linear-algebra-based-performance-modeling-and-enterprise-invoice-processing},
ISSN = {2584-0584},
ABSTRACT = {Hybrid artificial
intelligence architectures combining traditional computational methods with
neural network residuals represent a paradigm shift in addressing complex
real-world challenges. This report synthesizes two complementary approaches: (1) linear algebra-based digital system performance modeling that leverages
matrix-vector operations enhanced with neural network approximators, and (2) optical character recognition (OCR) integrated with large language models
(LLMs) for automated invoice processing in e-commerce environments. Both
methodologies exemplify the principle of interpretability-efficiency trade-offs
in modern AI systems. This work demonstrates how decomposing complex
problems into interpretable baselines with neural residuals yields superior
performance in accuracy, inference speed, and scalability compared to
monolithic deep learning approaches. The report presents mathematical
formulations, implementation strategies, empirical validation, and practical
deployment considerations across diverse application domains.},
DOI = {}
}