Prediction of fundraising outcomes for crowdfunding projects based on deep learning: a multimodel comparative study

Comparative analysis of BERT and FastText representations on crowdfunding campaign success prediction

Crowdfunding has become a popular financing method, attracting investors, businesses, and entrepreneurs. However, many campaigns fail to secure funding, making it crucial to reduce participation risks using artificial intelligence (AI). This study investigates the effectiveness of advanced AI techniques in predicting the success of crowdfunding campaigns on Kickstarter by analyzing campaign blurbs. We compare the performance of two widely used text representation models, bidirectional encoder representations from transformers (BERT) and FastText, in conjunction with long-short term memory (LSTM) and gradient boosting machine (GBM) classifiers. Our analysis involves preprocessing campaign blurbs, extracting features using BERT and FastText, and evaluating the predictive performance of these features with LSTM and GBM models. All experimental results show that BERT representations significantly outperform FastText, with the highest accuracy of 0.745 achieved using a fine-tuned BERT model combined with LSTM. These findings highlight the importance of using deep contextual embeddings and the benefits of fine-tuning pre-trained models for domain-specific applications. The results are benchmarked against existing methods, demonstrating the superiority of our approach. This study provides valuable insights for improving predictive models in the crowdfunding domain, offering practical implications for campaign creators and investors.

Is Equity Crowdfunding the Leapfrog to Companies’ Success? Financial Performance in China

As the fastest-growing crowdfunding model, equity crowdfunding (ECF) brings high returns and uncertainty. In this context, it is crucial to understand these crowdfunding projects' actual performance. Since ECF is currently in the early stage of integration, there are still a lot of risk issues, such as the uncertainty of equity structure, capital supervision, or project management. Therefore, this paper develops a new profitability indicator, “return on registered capital,” to test its impact on the ECF project's actual return. This paper studies which factors affect the financial performance of ECF projects through the traditional statistical model and a deep neural network (DNN) model. There is evidence that return on registered capital affects the actual return of the project. At the same time, the company's operating time and the number of employees had an unexpected effect on project performance. In addition, the recognition accuracy of the DNN model in this study exceeds 97%, which affirms the applicability of the DNN model in the analysis of ECF success factors. This paper also uses tenfold cross-validation to prove that deep learning has certain advantages in this topic's accuracy and generalization error. This study explores whether company representatives' gender and knowledge level affect project performance. The results will be described in detail in the paper.

A Transformer-Based Bridge Structural Response Prediction Framework

Structural response prediction with desirable accuracy is considerably essential for the health monitoring of bridges. However, it appears to be difficult in accurately extracting structural response features on account of complex on-site environment and noise disturbance, resulting in poor prediction accuracy of the response values. To address this issue, a Transformer-based bridge structural response prediction framework was proposed in this paper. The framework contains multi-layer encoder modules and attention modules that can precisely capture the history-dependent features in time-series data. The effectiveness of the proposed method was validated with the use of six-month strain response data of a concrete bridge, and the results are also compared with those of the most commonly used Long Short-Term Memory (LSTM)-based structural response prediction framework. The analysis indicated that the proposed method was effective in predicting structural response, with the prediction error less than 50% of the LSTM-based framework. The proposed method can be applied in damage diagnosis and disaster warning of bridges.

An Intelligent Classification Method of Multisource Enterprise Financial Data Based on SAS Model

An enterprise is often faced with a large amount of financial information and data information. It is inefficient to rely solely on manual work, and the accuracy is difficult to guarantee. For the multisource data of corporate finance, it is more difficult for financial personnel to accurately analyze the connections between the data. For the multisource financial data of enterprise, this is also a time-consuming and laborious task for financial personnel. At the same time, it is difficult to find the correlation between multiple sources of data and then formulate financial data that guides the development of the enterprise. With the advancement of intelligent algorithms, an intelligent classification algorithm similar to the SAS model has emerged, which can realize the intelligent classification of enterprise financial multisource data and accurately predict the future development trend, which is extremely beneficial to the development and performance of the enterprise. This article mainly combines the financial intelligence classification model SAS with clustering and decision tree methods to classify the financial multisource information and uses the neural network method to carry out the future development trend of corporate finance. The research results show that the maximum error of enterprise financial classification after using the intelligent classification method is only 3.71% and that the forecast error of the future development trend of enterprise finance is only 1.77%. This is an acceptable error range, and this intelligent classification method is also greatly improving the efficiency of corporate financial management.

Deep Learning-Based Cervical Spine Posterior Percutaneous Endoscopic Disc Nucleus Resection for the Treatment of Cervical Spondylotic Radiculopathy

In the past 10 years, the technology of percutaneous spine endoscopy has been continuously developed. The indications have expanded from simple lumbar disc herniation to various degenerative diseases of the cervical, thoracic, and lumbar spine. Traditional surgery for the treatment of cervical radiculopathy includes anterior cervical decompression surgery, anterior cervical decompression plus fusion surgery, and posterior limited fenestration surgery. This article mainly studies the treatment of cervical spondylosis caused by radiculopathy caused by the nucleus resection of the posterior cervical spine percutaneous spinal endoscopy based on deep learning. In the PPECD group, the height of the intervertebral cavity was measured before the operation and during the final follow-up, and the height change of the intervertebral cavity was evaluated. The relative angle and relative displacement of the sagittal plane of the operation segment in the PPECD group were measured, and the stability was evaluated. Using the cervical spine X-ray Kelvin degeneration evaluation criteria, before and during the final follow-up operation, the degeneration of the adjacent segments of the two groups was evaluated. A retrospective analysis of 26 cases of cervical radiculopathy that met the criteria for diagnosis, inclusion, and exclusion was reviewed. Among them, 11 cases were treated with PPECD surgery; 15 cases were treated with ACDF surgery. According to the evaluation method of Odom, the excellent rate and the good rate of the two groups were compared. According to the location of the lesion, the nerve detection or dull tip device is exposed under the armpit or shoulder of the nerve root, and the protruding nucleus pulposus tissue is explored and removed, and annulus fibrosus is performed as needed. After hemostasis was detected, the surgical instruments were removed and the surgical incision was completely sutured. Before the operation and 3 months after the operation, the final follow-up made no significant difference in the overall average height of the intervertebral cavity (F = 2.586, P > 0.05). The results show that posterior foramen expansion is an effective surgical method for the treatment of cervical spondylotic radiculopathy, but surgical adaptation requires strict management. In order to achieve satisfactory results, appropriate cases must be selected.

Predicting Fundraising Performance in Medical Crowdfunding Campaigns Using Machine Learning

The coronavirus disease (COVID-19) pandemic has flooded public health organizations around the world, highlighting the significance and responsibility of medical crowdfunding in filling a series of gaps and shortcomings in the publicly funded health system and providing a new fundraising solution for people that addresses health-related needs. However, the fact remains that medical fundraising from crowdfunding sources is relatively low and only a few studies have been conducted regarding this issue. Therefore, the performance predictions and multi-model comparisons of medical crowdfunding have important guiding significance to improve the fundraising rate and promote the sustainable development of medical crowdfunding. Based on the data of 11,771 medical crowdfunding campaigns from a leading donation-based platform called Weibo Philanthropy, machine-learning algorithms were applied. The results demonstrate the potential of ensemble-based machine-learning algorithms in the prediction of medical crowdfunding project fundraising amounts and leave some insights that can be taken into consideration by new researchers and help to produce new management practices.