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In 2015, Taiwan introduced an exchange platform for equity crowdfunding called the Go Incubation Board for Startup and Acceleration (GISA) which is supervised by the OTC Taipei Exchange organization.Equity crowdfunding provides another channel for startups to access capital and allows for a new mechanism for start-up firms to establish their reputation with investors. However, the risks to investors from equity crowdfunding are high. The high-risk nature of equity crowdfunding has the potential to act as a contagion, and further erode confidence in the startup capital market by retail investors --and this lingers over the GISA platform in Taiwan. Therefore, this study applies the of Random Forest (RF) algorithm to evaluate the market reaction for start-up firms on the GISA in Taiwan. The RF algorithm is proposed to be integrated into an AI model to forecast the market reaction to start-up firms as they get listed on the GISA equity crowdfunding platform. The results not only fulfill the gap of detecting market reaction in equity crowdfunding, but the proposed RF model can replace the traditional statistics analytical technique to evaluate the market reaction. In proposed model applied AI algorithms to predict the market reaction on Taiwan GISA platform which can provide a useful ensemble tool for start-up firms and entrepreneurs to evaluate the degree of market reaction more efficiently before listing on the Taiwan GISA platform.

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Secret image sharing technology is a strategy for jointly protecting secret images. The (n, n) secret image sharing problem can be solved by conventional Boolean calculation easily. However, how to recover secret images with progressive steps is not addressed. In this study, we proposed an XOR-based (m, t, Ti) multi-secret image sharing scheme that shares m secret images among m participants and recovers m shared images progressively with t thresholds. The proposed secret images partition strategy (SIPS) partitions m secret images to generate intermediate images for different thresholds in the sharing procedure. Based on progressive recovery property, the proposed recovery method recovers parts of the secret images by gathering consecutive shared images. Moreover, gathering all shared images can perfectly recover all secret images. The experimental results show that the proposed XOR-based multi-secret image sharing method has high security and efficiency.