# 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.

## Key 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.