Crowdfunding is increasingly favoured by entrepreneurs for online financing. Predicting crowdfunding success can provide valuable guidance for stakeholders. It is a new attempt to evaluate the relative performance of different machine learning algorithms for crowdfunding prediction.
This study aims to identify the key factors of crowdfunding, and find the different performance and usage of machine learning algorithms for crowdfunding prediction.
We crawled data from MoDian.com, a Chinese crowdfunding platform, and predicted the crowdfunding performance using four machine learning algorithms, which is a new exploration in this area. Most of the existing literature focuses on empirical analysis. This work solves the problem of predicting crowdfunding performance using a dataset with a minimal number of highly contributive features, which has higher accuracy compared to the regression analysis.
The experiment results show that feature-selection-based machine learning models are effective and beneficial in crowdfunding prediction.
Feature selection can significantly improve the prediction performance of the machine learning models. KNN achieved the best prediction results with five features: number of backers, target amount, number of project likes, number of project comments, and sponsor fans. The prediction accuracy was improved by 16%, the precision was improved by 13.23%, the recall was improved by 22.66%, the F-score was improved by 18.48%, and the AUC was improved by 14.9%.
The threat of side-channel attacks poses a significant risk to the security of cryptographic algorithms. To counter this threat, we have designed an AES system capable of defending against such attacks, supporting AES-128, AES-192, and AES-256 encryption standards. In our system, the CPU oversees the AES hardware via the AHB bus and employs true random number generation to provide secure random inputs for computations. The hardware implementation of the AES S-box utilizes complex domain inversion techniques, while intermediate data is shielded using full-time masking. Furthermore, the system incorporates double-path error detection mechanisms to thwart fault propagation. Our results demonstrate that the system effectively conceals key power information, providing robust resistance against CPA attacks, and is capable of detecting injected faults, thereby mitigating fault-based attacks.
Facial emotion recognition (FER) represents a significant outcome of the rapid advancements in artificial intelligence (AI) technology. In today's digital era, the ability to decipher emotions from facial expressions has evolved into a fundamental mode of human interaction and communication. As a result, FER has penetrated diverse domains, including but not limited to medical diagnosis, customer feedback analysis, the automation of automobile driver systems, and the evaluation of student comprehension. Furthermore, it has matured into a captivating and dynamic research field, capturing the attention and curiosity of contemporary scholars and scientists. The primary objective of this paper is to provide an exhaustive review of FER systems. Its significance goes beyond offering a comprehensive resource; it also serves as a valuable guide for emerging researchers in the FER domain. Through a meticulous examination of existing FER systems and methodologies, this review equips them with essential insights and guidance for their future research pursuits. Moreover, this comprehensive review contributes to the expansion of their knowledge base, facilitating a profound understanding of this rapidly evolving field. In a world increasingly dependent on technology for communication and interaction, the study of FER holds a pivotal role in human-computer interaction (HCI). It not only provides valuable insights but also unlocks a multitude of possibilities for future innovations and applications. As we continue to integrate AI and facial emotion recognition into our daily lives, the importance of comprehending and enhancing FER systems becomes increasingly evident. This paper serves as a stepping stone for researchers, nurturing their involvement in this exciting and ever-evolving field.
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