New Artificial-Based Automated Quality Risk Prediction Methodology for College Students with Disabilitie’s Entrepreneurial Schemes
Keywords:
Risk Assessment, Classification, College Students, Artificial Intelligence, Entrepreneurial Projects, Ranking ModelAbstract
Evaluating and predicting the risk of entrepreneurial projects among college students with disabilities is a critical endeavor that requires a multifaceted approach. This process involves assessing various factors such as the nature of the business idea, the skills and capabilities of the student, potential market demand, and external environmental factors. the issues surrounding entrepreneurial projects among college students with disabilities require a nuanced understanding of the unique challenges they face. Accessibility barriers, societal stereotypes, limited support networks, and lack of inclusive resources are among the key issues hindering their entrepreneurial endeavors. To foster an inclusive environment, it's essential to implement targeted interventions, provide accessible resources and mentorship, raise awareness, and advocate for policy changes that promote equity and accessibility in entrepreneurship for individuals with disabilities. This paper proposed an Automated Quality Risk Prediction (AQRP). The proposed AQRP model uses the Quality assessment of the project at each stage with the ranking-based classification model. The AQRP estimates the process of ranking at every stage of the project and performs the assessment and evaluation of risk. Factors such as the quality of human features in the project and practical features are examined to estimate the features through the process of ranking. With the AQRP model, the features are ranked and integrated for the extraction and classification of features in the projects. With AQRP model the deep learning model is implemented for the classification of features in the projects. Simulation analysis demonstrated that social factors contribute significantly to the project quality assessment. Through the ranking, it is observed that ranking features comprise a higher feature value of 0.98 than the other features. The classification accuracy is achieved as 99% which is 12% higher than the conventional SVM and Linear Regression Classifiers.
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