In the era of fintech services computing, the convergence of advanced technologies has become pivotal for driving innovation. The seamless integration of IIoT-enabled data streams with artificial intelligence (AI) systems presents unprecedented opportunities for revolutionizing financial risk assessment. Efficient AI-to-AI business platforms are crucial for sustainable and transparent fintech credit risk analysis. However, current practices rely on single AI models, leading to inefficiencies and regulatory challenges. This paper proposes a novel Blockchain Regulatory Framework to address these issues, particularly in lending to small and micro enterprises. The key novelty of our work, compared to existing state-of-the-art, is fourfold: 1) a multi-level guarantee strategy that combines blockchain with AI-AI models to enable distributed risk-sharing among guarantee agencies; 2) a dual-chain blockchain structure for secure and efficient storage of credit data and processes; 3) the integration of Intel SGX with Oblivious RAM (ORAM) to ensure unprecedented confidentiality, integrity, and availability in credit data sharing; and 4) the incorporation of game theory to mathematically validate the anti-collusion properties of the AI-AI decision mechanism. Our findings indicate that the proposed framework offers a comprehensive solution for achieving safe, efficient, and transparent credit assessments. This approach holds significant promise for developing effective financing solutions while ensuring data security, regulatory compliance, and robust credit evaluation methodologies.

In medical risk prediction, such as predicting heart disease, machine learning (ML) classifiers must achieve high accuracy, precision, and recall to minimize the chances of incorrect diagnoses or treatment recommendations. However, real-world datasets often have imbalanced data, which can affect classifier performance. Traditional data balancing methods can lead to overfitting and underfitting, making it difficult to identify potential health risks accurately. Early prediction of heart attacks is of paramount importance, and researchers have developed ML-based systems to address this problem. However, much of the existing ML research is based on a single dataset, often ignoring performance evaluation across multiple datasets. As the demand for interpretable ML models grows, model interpretability becomes central to revealing insights and feature effects within predictive models. To address these challenges, we present a novel data balancing technique that uses a divide-and-conquer strategy with the K-Means clustering algorithm to segment the dataset. The performance of our approach is highlighted through comparisons with established techniques, which demonstrate the superiority of our proposed method. To address the challenge of inter-dataset discrepancies, we use two different datasets. Our holistic pipeline, strengthened by the innovative balancing technique, effectively addresses performance discrepancies, culminating in a significant improvement from 81% to 90%. Furthermore, through advanced statistical analysis, it has been determined that the 95% confidence interval for the AUC metric of our method ranges from 0.8187 to 0.8411. This observation serves to underscore the consistency and reliability of our approach, demonstrating its ability to achieve high performance across a range of scenarios. Incorporating Explainable AI (XAI), we examine the feature rankings and their contributions within the best performing Random Forest model. While the domain expert feedback is consistent with the explanatory power of XAI, some differences remain. Nevertheless, a remarkable convergence in feature ranking and weighting is observed, bridging the insights from XAI tools and domain expert perspectives.

Carbon emissions trading has become an increasingly hot topic nowadays, due to the fact that how to reduce carbon emissions has been a common effort of different countries. However, traditional methods are plagued by issues, such as inadequate privacy protection mechanisms and the challenge of representing data assets in a comprehensive form using blockchain data models. In this article, we propose carbon emissions trading scheme (CETS), a secure carbon emissions trading system using blockchain combined with digital assets transactions. The proposed CETS scheme enhances the performance of models for carbon emissions trading by prioritizing the efficiency, privacy, and traceability of carbon emissions trading. Simultaneously, it improves the consistency of digital asset trading throughout the chain. First, we propose a dual-blockchain-based method for storing and tracing carbon emission data, which ensures the privacy of the data. Next, we propose algorithms for transaction of digital assets in carbon emission trading scheme, which include digital asset uniqueness algorithm, serializable mechanism, and cross-chain algorithm of digital assets. Finally, we propose an automated machine learning pipeline approach based on the carbon trading price forecasting model construction method, which can provide efficient, automatic price forecasting model construction and training. The experimental results prove that our proposed carbon emission trading system can provide an efficient and stable carbon emission trading solution.

In this article, we aim to design an architecture for privacy-preserved credit data and model sharing to guarantee the secure storage and sharing of credit information in a distributed environment. The proposed architecture optimizes the data privacy by sharing the data model instead of revealing the actual data. This article also proposes an efficient credit data storage mechanism combined with a deletable Bloom filter to guarantee a uniform consensus for the training and computation process. In addition, we propose authority control contract and credit verification contract for the secure certification of credit sharing model results under federated learning. Extensive experimental results and security analysis demonstrate that our proposed credit model sharing system based on federated learning and blockchain is of high accuracy, efficiency, as well as stability. In particular, the findings of this article could alleviate the potential credit crisis under financial pressure that assist to economic recovery after the global COVID-19 pandemic. Our approach has further boosted up the demand for efficient, secure credit models for Industry 4.0.

Due to the increasing diversification of urban transportation modes, many urban areas have the problem of unbalanced traffic demand, which makes accurate prediction of traffic demand very important. However, most of the existing studies focus on improving the prediction accuracy of traffic demand on the single spatial relationship of a single traffic mode, ignoring the diversity of spatial relationships and the heterogeneity of transportation stations in the traffic network. In this paper, we propose a Co-Modal Graph Attention neTwork(CMGAT) framework to uncover the impact of different spatial relationships and traffic mode interactions on traffic demand. Specifically, we first utilize a feature embedding block to capture the semantic information from several features. Then, a multiple traffic graphs-based spatial attention mechanism and a multiple time periods-based temporal attention mechanism are proposed to capture spatial and temporal dependencies in multi-mode traffic demands. Moreover, an output layer is provided to incorporate the hidden states and raw time sequences to predict future traffic demand. Finally, we conduct experiments on two real-world datasets, NYC Bike and NYC Taxi, and the results not only demonstrate the superiority of our model, but also indicate the necessity of considering multiple spatial relationships and traffic modes.

Precise traffic demand prediction could help government and enterprises make better management and operation decisions by providing them with data-driven insights. However, it is a nontrivial effort to design an effective traffic demand prediction method due to the spatial and temporal characteristics of traffic demand distributions, dynamics of human mobility, and impacts of multiple environmental factors. To handle these problems, a Dynamic Transition Convolutional Neural Network (DTCNN) is proposed for the purpose of precise traffic demand prediction. Particularly, a transition network is first constructed according to the citiwide historical departure and arrival records, where the nodes are virtual stations discovered by a density-peak based clustering algorithm and the edges of two nodes correspond to transition flows of two stations. Then, a dynamic transition convolution unit is designed to model the spatial distributions of the traffic demands, and to capture the evolution of the demand dynamics. Last, a unifying learning framework is provided to incorporate the spatiotemporal states of the traffic demands with environmental factors. Experiments have been conducted on NYC taxi and bike-sharing data, and the results validate the effectiveness of the proposed method.