International Journal of Intelligent Systems最新文献

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.

Deep generative models are now capable of generating synthetic images with very high visual realism, often indistinguishable from real-world photographs. Such AI-generated images (AIGIs) can pose serious security concerns if used maliciously. Conventional AIGI detection methods are based on supervised learning and may have limited generalization ability. In this paper, we build a novel universal detector of AIGIs without the need to perform training on these images. Starting with a study on the effectiveness of various pretrained image models for the AIGI detection task, we then chose to build our detector based on the features of the popular CLIP model. Unlike existing methods, we use a small number of real images and their carefully processed counterparts as AIGI proxies during training, combined with a novel margin-based loss to promote generalization. Extensive experiments demonstrate the effectiveness of our method, outperforming existing supervised methods while not using any AIGI for training.

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.

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.

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.

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.

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.

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.

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.

The rapid development of deepfake technology has led to the generation of a large amount of tampered video and image content, posing a major challenge to content authenticity verification. In particular, detecting deepfakes in image sequences (e.g., agricultural product packaging) is particularly difficult because the anomalies introduced by the tampering techniques are often subtle and temporally continuous. In this paper, we propose a new deepfake detection method based on time series, combining independent component analysis (FastICA) with anomaly detection techniques. We first apply FastICA to extract independent components from image sequences to identify anomalous visual patterns that are unique to deepfake tampering. In addition, we use an efficient anomaly detection algorithm, LSHiforest, to achieve scalable and accurate identification of suspicious sequences. Experimental results show that the proposed method can still detect deepfake content with high accuracy in challenging scenarios with complex temporal dynamics. Our work provides a promising solution for real-time and large-scale detection of deepfake content in dynamic media.