International Journal of Intelligent Systems最新文献_第2页

A Hybrid Deep Learning Model for E-Commerce Recommendations: Sentiment Analysis With Autoencoders and Generative Adversarial Networks 电子商务推荐的混合深度学习模型：使用自动编码器和生成对抗网络的情感分析

IF 3.7 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-10-02 DOI: 10.1155/int/3852068

Mohammad Yarjanli, Neda Mahdinasab

{"title":"A Hybrid Deep Learning Model for E-Commerce Recommendations: Sentiment Analysis With Autoencoders and Generative Adversarial Networks","authors":"Mohammad Yarjanli, Neda Mahdinasab","doi":"10.1155/int/3852068","DOIUrl":"https://doi.org/10.1155/int/3852068","url":null,"abstract":"In e-commerce, customer reviews wield significant influence over business strategies. This study proposes a robust sentiment analysis (SA) model tailored to e-commerce recommendations. It aims to address the key limitations of existing methods, including challenges in generalizability, feature extraction, class imbalance, and hyperparameter tuning. Our process uses an autoencoder (AE) to extract key features from the input sentence. We employ DistilBERT for word embedding, which performs faster than the standard BERT model (bidirectional encoder representations from transformers). The proposed architecture integrates an AE with a transductive long short-term memory (TLSTM) unit, which is trained with a modified generative adversarial network (GAN). TLSTM leverages transductive learning to emphasize training samples that closely resemble those in the test distribution, enhancing the flexibility and predictive accuracy of the model. Within the GAN, the generator is designed to exclude gradients from dominant batch instances, encouraging greater output diversity and generalization. Once the AE is trained, its compressed feature representations are fed into a multilayer perceptron (MLP) classifier. To tackle class imbalance issues during classification, we implement a reinforcement learning (RL) mechanism. This strategy prioritizes the minority class by applying a reward mechanism to balance the classification outcomes. Moreover, we use the Bayesian optimization hyperband (BOHB) algorithm to fine-tune the hyperparameters of the model. Experimental results on the AIV, AA, and Yelp datasets demonstrate superior performance, with F-measure scores of 91.603%, 89.504%, and 90.397%, respectively. These outcomes validate the robustness of the model and its potential to significantly enhance recommendation quality in dynamic e-commerce environments.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3852068","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spearman and Jaccard-Based Convolutional Deep Neural Learning for Early Parkinson’s Diagnosis 基于Spearman和jaccard的卷积深度神经学习在早期帕金森诊断中的应用

IF 3.7 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-10-01 DOI: 10.1155/int/6662826

Vinoth Murali, Rajesh Natarajan, Francesco Flammini, Badria Sulaiman Alfurhood, C. M. Naveen Kumar, Sowmya V. L.

{"title":"Spearman and Jaccard-Based Convolutional Deep Neural Learning for Early Parkinson’s Diagnosis","authors":"Vinoth Murali, Rajesh Natarajan, Francesco Flammini, Badria Sulaiman Alfurhood, C. M. Naveen Kumar, Sowmya V. L.","doi":"10.1155/int/6662826","DOIUrl":"https://doi.org/10.1155/int/6662826","url":null,"abstract":"Parkinson’s disease (PD) is a chronic neurological condition causing an assortment of motor and cognitive prodromes. Each individual’s PD symptoms develop differently due to the variability of the ailment. This study aims to introduce the KNN Imputed Spearman’s Rank and Jaccard Convolutional Deep Neural Learning (KISRJCDNL) technique for automating early PD diagnosis depending on speech analysis. This work enhances disease diagnosis performance through preprocessing and early, precise PD detection. Several information collected from the given dataset are initially taken as input. Then, the preprocessing stage converts raw data into a structured format. Afterward, Spearman’s Rank Feature Selective and Jaccard Index–based Convolutional Deep Neural Learning Classifier with four layers, one input layer, one output layer, and two hidden layers, are deployed for diagnosing PD by efficiently performing the data classification. Experimental evaluation uses the Early Biomarkers of the PD dataset by different factors. Findings support the claim that the proposed KISRJCDNL technique enhances accuracy by 14%, reducing feature selection time, error rate, overall time, and space complexity by 16%, 43%, 36%, and 22% compared to the existing deep learning methods.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6662826","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145223944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Building a Universal Detector of AI-Generated Images Without Training on Them 在未经训练的情况下，构建人工智能生成图像的通用检测器

IF 3.7 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-09-30 DOI: 10.1155/int/8530953

Ji Li, Kai Wang

引用次数: 0

Exploring Kolmogorov–Arnold Networks for Interpretable Time Series Classification 探索可解释时间序列分类的Kolmogorov-Arnold网络

IF 3.7 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-09-30 DOI: 10.1155/int/9553189

Irina Barašin, Blaž Bertalanič, Mihael Mohorčič, Carolina Fortuna

{"title":"Exploring Kolmogorov–Arnold Networks for Interpretable Time Series Classification","authors":"Irina Barašin, Blaž Bertalanič, Mihael Mohorčič, Carolina Fortuna","doi":"10.1155/int/9553189","DOIUrl":"https://doi.org/10.1155/int/9553189","url":null,"abstract":"Time-series classification is a relevant step supporting decision-making processes in various domains, and deep neural models have shown promising performance in this respect. Despite significant advancements in deep learning, the theoretical understanding of how and why complex architectures function remains limited, prompting the need for more interpretable models. Recently, the Kolmogorov–Arnold Networks (KANs) have been proposed as a more interpretable alternative to deep learning. While KAN-related research is significantly rising, to date, the study of KAN architectures for time-series classification has been limited. In this paper, we aim to conduct a comprehensive and robust exploration of the KAN architecture for time-series classification utilizing 117 datasets from UCR benchmark archive, from multiple different domains. More specifically, we investigate (a) the transferability of reference architectures designed for regression to classification tasks, (b) the hyperparameter and implementation configurations for an architecture that best generalizes across 117 datasets, (c) the associated complexity trade-offs, and (d) KANs interpretability. Our results demonstrate that (1) the Efficient KAN outperforms MLPs in both performance and training times, showcasing its suitability for classification tasks. (2) Efficient KAN exhibits greater stability than the original KAN across grid sizes, depths, and layer configurations, especially when lower learning rates are employed. (3) KAN achieves competitive accuracy compared to state-of-the-art models such as HIVE-COTE2 and InceptionTime, while maintaining smaller architectures and faster training times, highlighting its favorable balance of performance and transparency. (4) The interpretability of the KAN model, as confirmed by SHAP analysis, reinforces its capacity for transparent decision-making.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/9553189","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145224510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PKDFIN: Prior Knowledge Distillation-Based Face Image Inpainting Network for Missing Regions PKDFIN：基于先验知识提取的缺失区域人脸图像补图网络

IF 3.7 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-09-26 DOI: 10.1155/int/6897997

Guoyin Ren, Qidan Guo, Zhijie Yu, Bo Jiang, Gong Li, Dong Li, Xinsong Wang

{"title":"PKDFIN: Prior Knowledge Distillation-Based Face Image Inpainting Network for Missing Regions","authors":"Guoyin Ren, Qidan Guo, Zhijie Yu, Bo Jiang, Gong Li, Dong Li, Xinsong Wang","doi":"10.1155/int/6897997","DOIUrl":"https://doi.org/10.1155/int/6897997","url":null,"abstract":"Existing facial image inpainting methods demonstrate high reliance on the precision of prior knowledge. However, the acquisition of precise prior knowledge remains challenging, and the incorporation of predicted prior knowledge in the restoration process often leads to error propagation and accumulation, thereby compromising the reconstruction quality. To address this limitation, we propose a novel facial image inpainting framework that leverages knowledge distillation, which is specifically designed to mitigate error propagation caused by imprecise prior knowledge. More specifically, we develop a teacher network incorporating accurate facial prior information and establish a knowledge transfer mechanism between the teacher and student networks via knowledge distillation. During the training phase, the student network progressively acquires the prior information encoded in the teacher network, thus improving its restoration capability for missing or corrupted regions. Additionally, we introduce a Coordinate Attention Gated Convolution (CAG) module, which enables effective extraction of both structural and semantic features from intact regions. Experiments conducted on the public facial datasets (CelebA-HQ and FFHQ) show that our method achieves performance improvements over existing approaches in terms of multiple quantitative evaluation metrics, including PSNR, SSIM, MAE, and LPIPS. Thus, the knowledge transfer from teacher to student network via knowledge distillation significantly reduces the dependence on prior knowledge characteristic of existing methods, facilitating more precise and efficient facial image inpainting.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6897997","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning-Driven Assessment of Student Movement and Performance Using Physiological Data in Physical Education Information Systems: An S-AIoT Solution 利用体育教育信息系统中的生理数据对学生运动和表现进行深度学习驱动评估：一个S-AIoT解决方案

IF 3.7 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-09-26 DOI: 10.1155/int/9479311

Ping Liu, Elaheh Dastbaravardeh

{"title":"Deep Learning-Driven Assessment of Student Movement and Performance Using Physiological Data in Physical Education Information Systems: An S-AIoT Solution","authors":"Ping Liu, Elaheh Dastbaravardeh","doi":"10.1155/int/9479311","DOIUrl":"https://doi.org/10.1155/int/9479311","url":null,"abstract":"This study bridges a crucial gap in athletic performance analysis by introducing a novel machine learning (ML) framework that leverages integrated physiological signals (from the DB 2.0 database) towards Sport Artificial Intelligence of Things (S-AIoT). Understanding athletic performance is key to developing effective training programs and enhancing overall physical education. However, traditional methods often fall short in capturing the nuances of human movement. Our primary goal is to develop an innovative method for accurately classifying sports activities using advanced analytical techniques that consider various physiological signals. This study aims to improve classification accuracy and provide real-time analytics for sports performance. To achieve this, we employ spatial and temporal attention mechanisms to dynamically weight critical signals, enabling precise tracking of movement transitions across different sports. The model is trained on comprehensive datasets comprising respiration rate, ECG, and heart rate (HR), providing a multifaceted analysis of athletic performance. Extensive experiments validate the model, which achieves a remarkable accuracy of 90.32%. It is the first model of its kind, outperforming established models like 1D convolutional neural network (CNN), LSTM, BiLSTM, and 1D CNN-BiLSTM. The model demonstrates strong generalization ability on unseen data, proving its effectiveness in diverse scenarios, and exhibits moderate noise resilience, enhancing its practical applicability.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/9479311","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Time Series Forecasting Based on Multiscale Fusion Transformer in Finance 金融中基于多尺度融合变压器的时间序列预测

IF 3.7 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-09-24 DOI: 10.1155/int/3890049

Guangxia Xu, Han Hu, Chuang Ma, Jiahui Li

{"title":"Time Series Forecasting Based on Multiscale Fusion Transformer in Finance","authors":"Guangxia Xu, Han Hu, Chuang Ma, Jiahui Li","doi":"10.1155/int/3890049","DOIUrl":"https://doi.org/10.1155/int/3890049","url":null,"abstract":"Time series forecasting is significant in market research and decision-making in the financial sector, but the complexity and uncertainty of financial data pose challenges to accurate forecasting. Although deep learning methods, including transformers, have significantly improved the forecasting effect, these methods still have limitations in dealing with the multiscale features of financial time series and their complex serial correlation. They fail to fully utilize the frequency domain’s multiscale features and spatial relationships. For this situation, this study proposes a time series forecasting method based on the multiscale fusion transformer for financial data, which aims to extract significant periodic patterns using frequency domain analysis effectively. Besides, the multiscale attention mechanism and graph convolution module are introduced to realize the detailed modeling of the time series simultaneously, effectively capture the spatial relationship, and obtain the correlation between different series on multiple frequency scales. In this study, experimental validation is carried out on several financial time series datasets, and the findings demonstrate that the proposed approach positively impacts predicting accuracy.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3890049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Steganography Defense Network Based on Simulation of Steganography Information Distribution 基于隐写信息分布仿真的隐写防御网络

IF 3.7 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-09-24 DOI: 10.1155/int/9958912

Jinjin Liu, Sa Xue, Xinyu Zhang, Fengyuan Xiang, Yuanyuan Ma

{"title":"Steganography Defense Network Based on Simulation of Steganography Information Distribution","authors":"Jinjin Liu, Sa Xue, Xinyu Zhang, Fengyuan Xiang, Yuanyuan Ma","doi":"10.1155/int/9958912","DOIUrl":"https://doi.org/10.1155/int/9958912","url":null,"abstract":"In order to block the spread of illegal stego-image and reduce the erasing traces of steganography attacks on images, this paper proposes a steganography attack network based on simulation of steganography information distribution. First, a strategy of simulating steganography noise was adopted, and the distribution of steganography noise was learned by convolutional neural network, and a small amount of noise was added to the position of the secret message accurately to complete the attack on the steganography information, while protecting the image content to the maximum extent. In addition, different image recovery modules are designed in the deep network, such as the shallow feature extraction module, progressive attention recovery module, and detail feature reconstruction module, which collectively leverage hierarchical pixel features to mitigate discrepancies between reconstructed and original images while preserving visual fidelity before and after image attacks. Through two kinds of loss functions, the deep network model continuously optimizes the network performance to achieve the minimum degree of damage to the image content and the maximum degree of recovery of the reconstructed image. Experimental results show that the proposed method is superior to other methods in erasing secret message and restoring image quality.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/9958912","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-Object Transfer Learning-Based Few-Shot Surface Defect Detection of Lithium Batteries 基于跨目标迁移学习的锂电池小片表面缺陷检测

IF 3.7 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-09-23 DOI: 10.1155/int/4904188

Zhongsheng Chen, Bo Hu, Wang Zuo

{"title":"Cross-Object Transfer Learning-Based Few-Shot Surface Defect Detection of Lithium Batteries","authors":"Zhongsheng Chen, Bo Hu, Wang Zuo","doi":"10.1155/int/4904188","DOIUrl":"https://doi.org/10.1155/int/4904188","url":null,"abstract":"Lithium batteries are one class of key components in new-energy vehicles, and surface defects are easily generated during production, causing serious threats to safety. Most deep learning methods of surface defect detection heavily rely on lots of high-quality labeled samples. Unfortunately, it is very difficult and expensive to prepare defect datasets of lithium batteries in practice. To deal with this issue, this paper presents cross-object transfer learning (COTL)–based few-shot surface defect detection of lithium batteries by resort to massive defect samples of other objects. The COTL model is composed of image preprocessing, feature extraction, feature fusion, and contrastive learning-based defect detection modules. The ResNeXt-101 network is used as the backbone to enhance feature extraction capability. The path aggregation feature pyramid network (PAFPN) is used to realize multiscale feature fusion. The contrastive learning branch is added to improve the discrimination ability among different categories of region proposals under few defect samples and increase the generalization ability. Then, experiments are done to testify the proposed method, where base-class defect dataset from other objects and new-class defect dataset from soft-pack lithium batteries are adopted for training and testing. Furthermore, model comparison and ablation studies are performed. The results show that the recall rate, the AP50, the mAP, and the F1 values of the COTL model are much better than those of other existing models when only using few defect samples. In particular, when there are only 30 new-class defect samples, the above four metrics of the COTL model are already larger than 0.90. The results testify that the proposed COTL model provides a more effective solution for few-shot surface defect detection of lithium batteries.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/4904188","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability Analysis Based on Aleatory and Epistemic Uncertainty Using Binary Decision Diagrams 基于选择性和认知不确定性的二元决策图可靠性分析

IF 3.7 2区计算机科学

International Journal of Intelligent Systems Pub Date : 2025-09-23 DOI: 10.1155/int/6471577

Elena Zaitseva, Vitaly Levashenko

{"title":"Reliability Analysis Based on Aleatory and Epistemic Uncertainty Using Binary Decision Diagrams","authors":"Elena Zaitseva, Vitaly Levashenko","doi":"10.1155/int/6471577","DOIUrl":"https://doi.org/10.1155/int/6471577","url":null,"abstract":"The development of a mathematical model is an important step in reliability analysis. However, initial data are often not clearly defined, and some necessary information about the system’s behavior may be missing. Most mathematical models in reliability analysis primarily address aleatory uncertainty. However, recently, many problems in reliability analysis increasingly need to take into consideration epistemic uncertainty. Therefore, methods for developing mathematical models based on uncertain initial data should be refined to account for both aleatory and epistemic uncertainties. This is particularly true for models that represent a system using binary decision diagrams (BDDs). This paper proposes a new method for constructing a system’s mathematical model in the form of a BDD based on incomplete and uncertain data. Machine learning approaches and principles are employed in this method to account for the epistemic uncertainty of the initial data. A fuzzy classifier, specifically a fuzzy decision tree (FDT), is used to build a BDD from epistemically uncertain data. The use of a tree-based classifier allows simplifying the transformation between FDT and BDD.","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/6471577","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0