Intelligent Information Processing XII

Table of Contents

1. Business Intelligence and Risk Control

   1. Frontmatter

   2. A Stock Price Trend Prediction Method Based on Market Sentiment Factors and Multi-layer Stacking Ensemble Learning with Dual-CNN-LSTM Models and Nested Heterogeneous Learners

      Maoguang Wang, Jiaqi Yan, Yuxiao Chen

      Abstract

      Investor sentiment, as a factor influencing stock price volatility, has received increasing research attention in recent years. This study proposes a more comprehensive representation of sentiment by incorporating social attributes when constructing investor factors. Notably, a novel market sentiment factor, γ, is introduced in this paper, which combines investor sentiment, stock data, and policy influences to enhance prediction accuracy beyond individual models. A multi-level nested ensemble model based on stacking is constructed in this study, which integrates the sentiment-stock Dual-CNN-LSTM model with learners to improve the accuracy of stock price volatility prediction. The experimental results demonstrate that: (1) The proposed market sentiment factor γ shows improved predictive accuracy compared to using investor sentiment factors alone, with an average increase of 5.55%; (2) The Dual-CNN-LSTM model outperforms the CNN-LSTM model using stock data alone in terms of volatility prediction accuracy, with an improvement of 9.81%. (3) The proposed multi-level nested ensemble algorithm, which adopts stacking nested Learner, achieves an accuracy of 88.24% in stock trend prediction. Overall, this research constructs a better sentiment indicator factor γ and provides a new approach for predicting stock price volatility through the integrated nested model, highlighting the effectiveness of hybrid architectures in addressing financial forecasting challenges.

   3. Credit Default of P2P Online Loans Based on Logistic Regression Model Under Factor Space Theory Risk Prediction Research

      Xiaotong Liu, Haoyu Wang, Kaijie Zhang, Kaile Lin, Qiufeng Shi, Fanhui Zeng

      Abstract

      P2P, as the most representative online lending platform with a long history of personal credit development, can provide powerful data support for exploring the problem of personal credit default risk, and Logistic Regression plays an important role in machine learning, and the current research on Logistic Regression mainly stays at the application level. Therefore, based on the Factor Space theory to further deepen the interpretation of Logistic Regression, explore the obvious and hidden relationship of the factors behind it, and give a reasonable expression of Logistic Regression from the perspective of the obvious and hidden factors, take the U.S. lending club as an example, choose the lender information data of the whole year of 2019, and establish the P2P online credit default Logistic Regression prediction model. Considering that the conditional factors contain multiple value states, the One-Hot idea is introduced to improve the precision of the algorithm. The accuracy, recall and other evaluation indexes are chosen to compare and analyse the prediction effect of the model. The results of the model show that Logistic Regression can effectively predict the credit default risk of personal credit, and also provide a more in-depth explanation for the generation of personal credit default risk in the context of new personal loans.

   4. FedPV-FS: A Feature Selection Method for Federated Learning in Insurance Precision Marketing

      Chunkai Wang, Jian Feng

      Abstract

      Insurance companies always use federated learning to integrate external data sources for data analysis and improve the conversion rate of insurance precision marketing. However, due to imbalanced data distribution and the presence of null data, the joint modeling often suffers from low robustness and is prone to falling into the dilemma of under-fitting. Therefore, the feature selection for federated learning needs to be incorporated before the joint modeling to improve the accuracy of predictions. In this paper, we propose the FedPV-FS method, which includes two-party feature selection based on public verifiable covert (PVC), and multi-party federated feature selection based on verifiable secret sharing (VSS). Moreover, we iteratively optimize federated feature selection using data selection, transformation, and integration. Experiments show that our method can achieve high-quality feature selection for increasing the optimization objective to 88.4%, promote the continuous increase of insurance premiums, and has good applications in insurance precision marketing scenarios.

   5. FRBBM-Scheme: A Flexible Ratio Virtual Primary Key Generation Approach Based on Binary Matching

      Tiancai Liang, Yun Zhao, Haolin Wang, Ziwen Cai, Zhaoguo Wang, Wenchao Wang, Chuanyi Liu

      Abstract

      The protection of database watermarking techniques based on primary key (PK) is weakened by potential PK attacks and their huge impact. Using virtual primary key (VPK) schemes is an effective solution to enhance the robustness of these techniques. Pérez Gort et al. proposed the HQR-Scheme, which first considers balancing the participation rate of each attribute to improve the ability of scheme to cope with deletion attacks. However, the HQR-Scheme has limited control ability and cannot adapt to changes in relevant information in databases. Facing these challenges, we innovatively propose the FRBBM-Scheme, which allows users to fully analyze data table information and formulate flexible ratio adjustment strategies to enhance their ability to cope with deletion attacks. We verify our proposed scheme through multiple experiments, which show that it has excellent ratio control ability, can generate high-quality VPK sets, and can resist different levels of attribute deletion attacks.

   6. From Concept to Prototype: Developing and Testing GAAINet for Industrial IoT Intrusion Detection

      Siphesihle Philezwini Sithungu, Elizabeth Marie Ehlers

      Abstract

      Intrusion detection is a growing area of concern in Industrial Internet of Things (IIoT) systems. This is largely due to the fact that IIoT systems are typically used to augment the operation of Critical Information Infrastructures, the compromise of which could result in severe consequences for industries or even nations. In addition, IIoT is a relatively new technological development which introduces new vulnerabilities. Machine learning methods are increasingly being applied to IIoT intrusion detection. However, the data imbalance prevalent in IIoT intrusion detection datasets can limit the performance of intrusion detection algorithms due to the significantly smaller amount of attack samples. As such, generative models have been applied to address the data imbalance problem by modelling distributions of intrusion detection datasets in order to generate synthetic attack samples. Current work presents the implementation of a Generative Adversarial Artificial Immune Network (GAAINet) as an approach for addressing data imbalance IIoT intrusion detection. Experimental results show that GAAINet could generate synthetic attack samples for the WUSTL-IIoT-2021 dataset. The resulting balanced dataset was used to train an Artificial Immune Network classifier, which achieved a detection accuracy of 99.13% for binary classification and 98.87% for multi-class classification.

   7. Efficient and Secure Authentication Scheme for Internet of Vehicles

      Zhou Zhou, Xuan Liu, Chenyu Wang, Ruichao Lu

      Abstract

      The Internet of Vehicles (IoV) improves efficiency of transportation systems while enhancing the passenger travel experience. However, due to the open wireless communication environment, the IoV requires a reliable and secure authentication and key agreement scheme to ensure that the exchanged data in public channel cannot be forged or modified by the adversary. In most existing authentication schemes, the vehicle usually authenticates with each other by an online Trusted Authority (TA), which results in the authentication efficiency of these centralized authentication schemes are easily affected by TA’s computational and communication bottlenecks as the increase of traffic density. Therefore, this paper proposes a secure and efficient authentication and key agreement scheme for IoV, where the vehicles can authenticate with each other and build a session key through a pre-shared key. Besides, a group key is also proposed to broadcast basic safety messages in the same RSU group securely. The group key can be updated when the vehicle joins and leaves, so that a leaving group member cannot access the current communication process. By the Heuristic and BAN logic analysis, the proposed scheme is proved to be secure. Compared with existing schemes, the proposed scheme meets the security requirements and has significant advantages in terms of computation and communication overhead.

2. Pattern Recognition

   1. Frontmatter

   2. Research on Wavelet Packet Sample Entropy Features of sEMG Signal in Lower Limb Movement Recognition

      Jianxia Pan, Liu Yang, Xinping Fu, Haicheng Wei, Jing Zhao

      Abstract

      In order to extract deeper features from surface electromyography signals and improve the classification accuracy of lower limb movements, a feature extraction method combining wavelet packet and sample entropy (WPT-SampEn) is proposed to accurately identify three types of lower limb movements. The electromyographic signals are preprocessed, which includes Butterworth filtering, activity segment detection based on short-term energy, and normalization processing. A three-layer wavelet packet decomposition method is used to decompose the five electromyographic signals into eight different frequency bands. By calculating the energy proportion in each frequency band, the top four frequency bands are determined as the focus of analysis. The Kruskal-Wallis test is employed to select frequency bands with statistical differences. To validate the effectiveness of this method, the support vector machine (SVM) algorithm is used for lower limb motion classification. Experimental results show that using the wavelet packet sample entropy features of the lateral thigh, medial thigh, rectus femoris, and biceps femoris muscles, the recognition rate reaches up to 96.46%. Compared with existing methods, this approach can extract deeper features from sEMG signals and achieve higher recognition accuracy. It has great potential in areas such as rehabilitation training, wearable exoskeleton control, and daily activity monitoring.

3. Backmatter