The correlation between scientific and technological innovation and the financial performance
of seed enterprises is a critical area of study in the realm of entrepreneurship and innovation management.
Moreover, innovations in seed technology can enhance product quality, yield, and resilience, thereby
influencing customer satisfaction and brand reputation. The Principal Component Analysis (PCA)
algorithm is a fundamental technique used in data analysis and dimensionality reduction. This study
investigates the correlation between scientific and technological innovation and the financial performance
of seed enterprises, employing the Principal Component Analysis (PCA) algorithm for performance
evaluation. By analyzing a comprehensive dataset encompassing R&D investments, technological
advancements, and financial metrics of seed enterprises, the study aims to uncover underlying patterns
and relationships. Additionally, the Principal Component Statistical Probabilistic Network (PCA-SPN)
model is utilized to further explore the complex interactions between innovation factors and financial
performance indicators. Through this integrated approach, the research seeks to provide valuable insights
into the drivers of financial success in the seed industry, offering actionable recommendations for
enhancing innovation strategies and maximizing financial outcomes. The study demonstrated that R&D
investment levels are positively correlated with revenue growth, with an average annual growth rate of 12%
observed in enterprises with high R&D expenditure (exceeding $500,000 annually). Additionally,
technological advancements, quantified by patent filings and adoption rates of innovative seed varieties,
exhibit a strong positive association with profitability metrics, such as gross margin percentages. For
instance, seed enterprises introducing patented varieties experienced an average increase of 20% in gross
margins compared to non-patenting counterparts
Keywords : Technological Assessment; principal component analysis (PCA); probabilistic model; deep
learning; statistical features.
Citation : P. Brundavani, “A Principal Component Analysis Algorithm for Seed Enterprise Financial Performance and Scientific and Technological Innovation”,
Journal of Computer Allied
Intelligence (JCAI), vol.02, no.02, pp.49-62,2024.
DOI:
https://doi.org/10.69996/jcai.2024010
