An innovative efficiency of incubator to enhance organization supportive business using machine learning approach
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In: PLoS ONE, Vol. 20, No. 7, e0327249, 07.2025.
Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - An innovative efficiency of incubator to enhance organization supportive business using machine learning approach
AU - Li, Xin
AU - Zhang, Qian
AU - Gu, Hanjie
AU - Othmen, Salwa
AU - Asklany, Somia
AU - Lhioui, Chahira
AU - Elrashidi, Ali
AU - Mercorelli, Paolo
N1 - Publisher Copyright: © 2025 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2025/7
Y1 - 2025/7
N2 - 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.
AB - 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.
KW - Engineering
UR - http://www.scopus.com/inward/record.url?scp=105010960580&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0327249
DO - 10.1371/journal.pone.0327249
M3 - Journal articles
C2 - 40680072
AN - SCOPUS:105010960580
VL - 20
JO - PLoS ONE
JF - PLoS ONE
SN - 1932-6203
IS - 7
M1 - e0327249
ER -