RETRACTED: An innovative efficiency of incubator to enhance organization supportive business using machine learning approach

TL;DR

This paper introduces a machine learning method called USAV to help small businesses and startups adapt quickly to changing market conditions.

Contribution

The novel contribution is the Unified Statistical Association Validation (USAV) framework for dynamic business decision-making.

Findings

USAV achieved a 0.98 improvement assessment score and 96% data association accuracy.

The method reduced incubation analysis time by 240 units and improved statistical update efficiency to 0.97.

USAV offers real-time flexibility and a 35% modification ratio for continual business improvements.

Abstract

Many small businesses and startups struggle to adjust their operational plans to quickly changing market and financial situations. Traditional data-driven techniques often miss possibilities and waste resources. Our unique approach, Unified Statistical Association Validation (USAV), allows dynamic and real-time data association and improvement assessment to address this essential issue. USAV classifies and validates critical data associations based on business features to improve startup incubation and innovation decision-making. USAV analyses different financial eras using federated learning to find performance inefficiencies using a Kaggle dataset on small business success and failure. USAV recommends actionable improvements during innovation using non-recurrent statistical patterns, unlike standard models that use prior financial data. The framework allows real-time flexibility with continual statistical updates without data redundancy. The proposed approach achieved an improvement assessment score of 0.98, data association accuracy of 96%, statistical update efficiency of 0.97, modification ratio of 35%, and incubation analysis time reduction of 240 units in experimental evaluation. These findings demonstrate USAV’s ability to help strategic decision-making in dynamic corporate situations.