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

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An innovative efficiency of incubator to enhance organization supportive business using machine learning approach. / Li, Xin; Zhang, Qian; Gu, Hanjie et al.
In: PLoS ONE, Vol. 20, No. 7, e0327249, 07.2025.

Research output: Journal contributionsJournal articlesResearchpeer-review

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Li X, Zhang Q, Gu H, Othmen S, Asklany S, Lhioui C et al. An innovative efficiency of incubator to enhance organization supportive business using machine learning approach. PLoS ONE. 2025 Jul;20(7):e0327249. doi: 10.1371/journal.pone.0327249

Bibtex

@article{2cb978a45fe7492795575d02c3c5a605,
title = "An innovative efficiency of incubator to enhance organization supportive business using machine learning approach",
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{\textquoteright}s ability to help strategic decision-making in dynamic corporate situations.",
keywords = "Engineering",
author = "Xin Li and Qian Zhang and Hanjie Gu and Salwa Othmen and Somia Asklany and Chahira Lhioui and Ali Elrashidi and Paolo Mercorelli",
note = "Publisher Copyright: {\textcopyright} 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.",
year = "2025",
month = jul,
doi = "10.1371/journal.pone.0327249",
language = "English",
volume = "20",
journal = "PLoS ONE",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "7",

}

RIS

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 -