Social Commerce Intention, Social Interaction, and Social Support

Wu, Chih‐Lun; Horng, Shwu-Min

doi:10.4018/joeuc.307565

Cited by 4 publications

(3 citation statements)

References 103 publications

(150 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ROC curve judges the acceptable degree of accuracy obtained in the BP neural network, that is, confidence. The closer the curve is to the upper left corner, the higher the accuracy and the higher the acceptability; otherwise, the lower the acceptability (Wu & Horng, 2022).…”

Section: Acceptable Degree Of Accuracy Of Prediction Results: Roc Curvementioning

confidence: 99%

Empirical Test of Credit Risk Assessment of Microfinance Companies Based on BP Neural Network

2023

International Journal of Information Technologies and Systems Approach

View full text Add to dashboard Cite

In recent years, the chaos of internet finance has occurred frequently, especially P2P, with high risks. As a kind of financial innovation, small loan companies are challenging to avoid alone, and the issue of credit risk is also highly valued. This study selects the loan records of a small loan company (a daily loan record from September 1, 2016 to July 1, 2021 has seven indicators, each of which has 21299 data). It uses MATLAB programming to test the correctness of risk indicator selection and the accuracy of BP neural network classification and identification results. This study obtains the corresponding risk value. According to the corresponding risk value, the newly applied loans are classified, that is, rated, to verify the effectiveness and applicability of this method. Therefore, BP neural network has strong applicability, generalization ability, and portability and is an effective method for small loan companies to guide credit risk assessment.

show abstract

Section: Acceptable Degree Of Accuracy Of Prediction Results: Roc Curvementioning

confidence: 99%

Empirical Test of Credit Risk Assessment of Microfinance Companies Based on BP Neural Network

2023

International Journal of Information Technologies and Systems Approach

View full text Add to dashboard Cite

show abstract

“…The focus of research is mainly on improving the naturalness of interaction, context understanding ability, and intelligent task execution of AI virtual assistants (Wu & Horng, 2022). Taking dialogue systems as an example, the trend is towards multi-round dialogue, emotion recognition, and personalized recommendations.…”

Section: Ai Virtual Assistantmentioning

confidence: 99%

Intelligent Productivity Transformation

Liu,

Li,

et al. 2024

Journal of Organizational and End User Computing

View full text Add to dashboard Cite

With the penetration of deep learning technology into forecasting and decision support systems, enterprises have an increasingly urgent need for accurate forecasting of time series data. Especially in fields such as finance, retail, and production, immediate and accurate predictions of market trends are the key to maintaining a competitive advantage. This study aims to address the limitations of traditional time series forecasting methods, such as the difficulty in adapting to the nonlinearity and non-stationarity of the data, through an innovative deep learning framework. The authors propose a Prophet model that combines deep learning with LSTNet and statistics. In this way, they combine the ability of LSTNet to handle complex time dependencies and the flexibility of the Prophet model to handle trends and periodicity. The particle swarm optimization algorithm (PSO) is responsible for tuning this hybrid model, aiming to improve the accuracy of predictions. Such a strategy not only helps capture long-term dependencies in time series, but also models seasonality and holiday effects well.

show abstract

“…Specifically, CNNs obtain low-level features with rich geometric information during feature extraction, but their semantic information representation ability is weak. The high-level features obtained have rich semantic information but lack spatial details or positional information features (Wu, 2022;Wu & Horng, 2022).…”

Section: Fuse Transformermentioning

confidence: 99%

An Empirical Study of Portrait Rights on Emotional Evolution in Virtual Social Scenarios by Transformer and Cloud Computing

Jia,

Gao

2024

Journal of Organizational and End User Computing

View full text Add to dashboard Cite

Facial expression recognition technology can improve the possibility of fugitives and persons subject to execution being discovered by public security and judicial personnel in the virtual world, thereby improving the actual effectiveness and credibility of the law. There may also be a risk of portrait rights infringement with this technology, and the user of the technology needs to inform the person whose facial expression is being extracted in advance and obtain permission. With the rapid progression of deep learning and artificial intelligence, 3D facial expression modeling has garnered increased significance in computer vision and graphics. We propose an innovative approach combining Transformer models with cloud computing to simulate facial expression evolution in virtual social environments. Leveraging Transformer-based feature extraction, our model integrates emotional cues from various modalities to accurately capture subtle changes over time.

show abstract

Social Commerce Intention, Social Interaction, and Social Support

Cited by 4 publications

References 103 publications

Empirical Test of Credit Risk Assessment of Microfinance Companies Based on BP Neural Network

Empirical Test of Credit Risk Assessment of Microfinance Companies Based on BP Neural Network

Intelligent Productivity Transformation

An Empirical Study of Portrait Rights on Emotional Evolution in Virtual Social Scenarios by Transformer and Cloud Computing

Contact Info

Product

Resources

About