TY - M-10447
AU - Gotur, Goutam
AU - Saravana Kumar, Dr
TI - A Comprehensive Analysis of Linear Algebra-Based Performance Modeling and Enterprise Invoice Processing
T2 - Scientific Research Journal of Science, Engineering and Technology

PY - 2026
VL - 4
IS - 1
SN - 2584-0584

AB - 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.

KW - Hybrid AI systems
KW - Linear Algebra
KW - Neural Networks
KW - Large Language Models
KW - OCR
KW - Performance Modeling
KW - Automated data extraction
KW - E-commerce
KW - Interpretability
KW - Scalability
DO -