Engineering Applications of Artificial Intelligence最新文献

{"title":"Unveiling the hidden patterns: A novel semantic deep learning approach to fake news detection on social media","authors":"","doi":"10.1016/j.engappai.2024.109240","DOIUrl":"10.1016/j.engappai.2024.109240","url":null,"abstract":"<div><p>The rise of social media as a source of news consumption has led to the spread of fake news, posing serious consequences for both individuals and society. The detection and prevention of fake news are essential, and previous research has shown that incorporating news content along with its associated headlines and user comments can improve detection performance. However, the semantic relationships between these elements have not been fully explored. This paper proposes a novel approach that models the relationships between news bodies and associated headlines/user comments using deep learning techniques, such as fine-tuned Bidirectional Encoder Representations from Transformers (BERT) and cross-level cross-modality attention sub-networks. In our proposed model, we utilize two different configurations of BERT: pool-based representation, which provides a representation of the entire document, and sequence representation, which represents each token within the document (i.e., at the word and text levels). The approach also encodes user-posting behavioural features and fuses the output of these components to detect fake news using a classification layer. Our experiments on benchmark datasets demonstrate the superiority of the proposed method over existing state-of-the-art (SOTA) approaches, highlighting the importance of utilizing semantic relationships for improved fake news detection (FND). These findings have significant implications for combating the spread of fake news and protecting society from its negative effects.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0952197624013988/pdfft?md5=35ff487d94f224d57ee34d786da2a54b&pid=1-s2.0-S0952197624013988-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137198","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}

{"title":"Federated learning with comparative learning-based dynamic parameter updating on glioma whole slide images","authors":"","doi":"10.1016/j.engappai.2024.109233","DOIUrl":"10.1016/j.engappai.2024.109233","url":null,"abstract":"<div><p>The rapid advancements in artificial intelligence have profoundly impacted various societal domains, particularly in healthcare. In computational pathology, deep learning techniques have shown remarkable abilities in classifying, segmenting, and recognizing pathology images. However, acquiring large-scale, high-quality medical datasets has become challenging due to increased privacy concerns and data protection awareness among institutions and patients. We propose utilizing federated learning to address this data privacy issue in this study. Our research focuses on classifying glioma whole slide images. To enhance the privacy of sensitive data, we incorporate Laplace noise into the model parameters of each local client. This technique guarantees the protection of patients’ data while allowing collaborative learning. Moreover, we introduce a novel method called Federated Learning with Comparative Learning-based Dynamic Parameter Updating. We select a local model with the optimal performance before all local model parameters are aggregated into global model parameters. Other local models then learn to update parameters from this selected model. By incorporating the Comparative Learning-based Dynamic Parameter Updating, we enhance the learning effect and improve the overall model performance for classifying glioma data. To assess our proposed method, we perform assessments on two separate classification tasks. The results of our experiments show that our privacy-preserving federated learning framework effectively utilizes multi-center data while maintaining good privacy protection performance. Additionally, compared to the commonly used federated averaging baseline method, our approach significantly outperforms glioma data classification tasks. Our research offers a promising framework that achieves high classification accuracy and ensures the protection of sensitive medical data, thus showcasing its potential in advancing computational pathology research and practice. Our code is free at <span><span>https://github.com/jiaxian-hlj/FL-Dpu</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Local–global normality learning and discrepancy normalizing flow for unsupervised image anomaly detection","authors":"","doi":"10.1016/j.engappai.2024.109235","DOIUrl":"10.1016/j.engappai.2024.109235","url":null,"abstract":"<div><p>The unsupervised detection and localization of image anomalies hold significant importance across various domains, particularly in industrial quality inspection. Despite its widespread utilization, this task remains inherently challenging due to its reliance solely on defect-free normal knowledge. This paper presents the local–global normality learning and discrepancy normalizing flow, a new state-of-the-art model for unsupervised image anomaly detection and localization. In contrast to existing methods, It adopts a two-stream approach that considers both local and global semantics, ensuring stable detection of abnormalities. The framework comprises two key components: the dual-branch Transformer and the discrepancy normalizing flow, facilitating reconstruction and discrimination. The proposed framework leverages pre-trained convolutional neural networks to extract multi-scale feature embeddings, followed by a novel dual-branch transformer that achieves feature reconstruction from local and global perspectives. The local reconstruction employs self-attention, while the global reconstruction incorporates global prototype tokens and semantic query tokens by the aggregation-cross attention mechanism. Moreover, discrepancy normalizing flow is developed to estimate the likelihood of anomalies based on the discrepancy between pre-trained features and local/global reconstruction results. Extensive validation on established public benchmarks confirms that our method achieves state-of-the-art performance with the proposed local–global reconstruction and discrimination dual-stream framework.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142128744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid network via key feature fusion for image restoration","authors":"","doi":"10.1016/j.engappai.2024.109236","DOIUrl":"10.1016/j.engappai.2024.109236","url":null,"abstract":"<div><p>In the field of artificial intelligence, combining transformers and convolutional neural networks (CNNs) to improve performance has become a popular solution for various image restoration tasks. However, the hyperparameters related to feature levels are empirical, leading to the inevitable presence of redundant features that hinder effective image restoration. Additionally, the current method of fusing global and local information is simple and direct, failing to fully exploit the potential of hybrid architectures. To address this issue, we propose a key feature fusion hybrid network (KF2H-Net) that reduces redundancy and dynamically fuses key features. On one hand, we create different learnable selection mechanisms within the hybrid network’s various units to choose global key features and local key features, enhancing the depth perception and selection capabilities for different features. On the other hand, through a parameter fusion module for dynamic feature fusion, we refine the multi-feature fusion method to emphasize the more critical features for image restoration. In order to verify the general performance of the proposed KF2H-Net, we specially selected three typical scenarios (underwater, low-light, and haze) for testing. KF2H-Net represents a novel approach to hybrid models addressing practical applications in the field of artificial intelligence. Extensive experiments show that KF2H-Net achieves state-of-the-art performance across different scenarios.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142128742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer framework for depth-assisted UDA semantic segmentation","authors":"","doi":"10.1016/j.engappai.2024.109206","DOIUrl":"10.1016/j.engappai.2024.109206","url":null,"abstract":"<div><p>Unsupervised domain adaptation (UDA) plays a crucial role in transferring models trained on synthetic datasets to real-world datasets. In semantic segmentation, UDA can alleviate the requirement of a large number of dense semantic annotations. Some UDA semantic segmentation approaches have already leveraged depth information to enhance semantic features for improved segmentation accuracy. Building on this, we introduce a UDA multitask Transformer framework called Multi-former. Multi-former contains a semantic-segmentation and a depth-estimation network. Depth-estimation network extracts more informative depth features to estimate depth and assist in semantic segmentation. In addition, considering the issue of imbalanced class pixel distributions in the source domain, we present a rare class mix strategy (RCM) to balance domain adaptability for all classes. To further enhance the UDA semantic segmentation performance, we design a mixed label loss weight strategy (MLW), which employs different types of weights to comprehensively utilize the features of pseudo-label. Experimental results demonstrate the effectiveness of the proposed approach, which achieves the best mean intersection over union (mIoU) of 56.1% and 76.3% on the two UDA benchmark tasks of synthetic datasets to real-world datasets, respectively. The code and models are available at <span><span>https://github.com/fz-ss/Multi-former</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142128864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on reflective clothing recognition algorithm based on combining omni-dimensional dynamic convolution and partial convolution","authors":"","doi":"10.1016/j.engappai.2024.109180","DOIUrl":"10.1016/j.engappai.2024.109180","url":null,"abstract":"<div><p>Currently, in construction sites, road maintenance, airports, and other special scenarios, the process of checking whether workers are wearing reflective clothing for safety is overly reliant on manual operations, and this manual screening method is not only inefficient but also has huge labor costs. To address this problem, this paper proposes a new method for reflective clothing wear recognition. Firstly, by replacing some traditional convolutions in the neck network of the YOLOv7-tiny(You Only Look Once vertion 7 - tiny) algorithm with the ODConv(Omni-dimensional Dynamic Convolution) module, the four dimensions of the kernel space can be endowed with convolutional dynamics attributes, which improves the detection accuracy of the model. Secondly, the PConv(Partial Convolution) module is used to replace some other traditional convolutions in the neck network, aiming to ensure detection accuracy while reducing computational redundancy and memory access. Then, a new SPPC(Spatial Pyramid Pooling Curtail) module is proposed and replaces the SPPCSPC(Spatial Pyramid Pooling Cross Stage Partial Concat) module of the original neck network, which guarantees accuracy and reduces the number of model parameters at the same time. Finally, the algorithm model proposed in this paper is ported to the Jetson Nano edge computing device, which can well meet the demand for real-time detection of reflective clothing and lay the foundation for subsequent practical applications.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142128743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hypervolume fraction-based adaptive evolutionary algorithm for many-objective optimization and the application to electromagnetic device design","authors":"","doi":"10.1016/j.engappai.2024.109060","DOIUrl":"10.1016/j.engappai.2024.109060","url":null,"abstract":"<div><p>Performance of many-objective evolutionary algorithms (MaOEAs) heavily depends on the environmental selection strategy which determines the offspring for next generations. One kind of selection strategy may only suit certain kinds of optimization problems. Moreover, one single strategy might not always work well at different evolutionary stages. To adaptively adjust the environmental selection strategy, this paper proposes a hypervolume fraction-based adaptive evolutionary algorithm (HFAEA). First, a hypervolume fraction-based estimation method is proposed to address the difficulty in detecting the feature of Pareto front. It calculates the ratio of the hypervolume of population coverage to the hypervolume of coordinate axis coverage. With a small or large hypervolume fraction, Pareto front is regarded as irregular or regular respectively and an adaptive switching strategy adaptively selects a proposed vector angle-based strategy or an improved reference vector-based strategy. HFAEA is compared with five state-of-the-art algorithms on 24 problems with a large hypervolume fraction and 24 problems with a small hypervolume fraction. Experimental results show that HFAEA is the most competitive in handling different kinds of problems. It outperforms algorithms that designed for irregular problems as well as algorithms that use uniformly distributed reference vectors in irregular problems. These findings highlight the effectiveness of the proposed hypervolume fraction-based estimation method. The superior performance is also demonstrated in two electromagnetic device optimization problems, including the designs of a compact single-layer butler matrix and a broadband filtering power divider, where better results than original ones are achieved and HFAEA also outperforms state-of-the-art MaOEAs.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital-analog driven multi-scale transfer for smart bearing fault diagnosis","authors":"","doi":"10.1016/j.engappai.2024.109186","DOIUrl":"10.1016/j.engappai.2024.109186","url":null,"abstract":"<div><p>Self-diagnosis and self-decision are crucial to smart bearing, where intelligent and robust models should be built and deployed on the smart bearing chip for an on-line edge effect. Whereas, this process requires a large amount of labeled prior data to train the fault identification model. Although the existing digital-analog driven transfer learning methods can realize fault identification under small samples, these algorithms mainly focus on how to reduce the difference between the two domains. These algorithms do not form a complete and applicable method for smart bearing fault diagnosis. Focusing on these issues, a digital-analog driven multi-scale transfer (DaD-MsT) method was proposed for smart bearing fault diagnosis. Different from the conventional methods, it can be achieved through end-side and edge-side cooperation, and the effect of transfer diagnosis is further improved by the proposed deep branch transfer network (DBTN) model. First, the smart bearing dynamic model is established, and the dynamic model response is obtained for use as source domain data in end-side. Then, a DBTN model was proposed to realize more effective digital-analog driven transfer learning. Finally, the trained model is deployed on the edge chip of the smart bearing for real-time fault identification and parameter fine-tuning. Experiments and comparisons verify the effectiveness of the proposed method in the case of small-sample data. Specifically, an online edge intelligent diagnosis system is also built to illustrate the ability in actual application of smart bearing intelligent diagnosis.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142128863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural networks in closed-loop systems: Verification using interval arithmetic and formal prover","authors":"","doi":"10.1016/j.engappai.2024.109238","DOIUrl":"10.1016/j.engappai.2024.109238","url":null,"abstract":"<div><p>Machine Learning approaches have been successfully used for the creation of high-performance control components of cyber–physical systems, where the control dynamics result from the combination of many subsystems. However, these approaches may lack the trustworthiness required to guarantee their reliable application in a safety-critical context. In this paper, we propose a combination of interval arithmetic and theorem-proving verification techniques to analyze safety properties in closed-loop systems that embed neural network components. We show the application of the proposed approach to a model-predictive controller for autonomous driving comparing the neural network verification performance with other existing tools. The results show that open-loop neural network verification through interval arithmetic can outperform existing approaches proving properties with a smaller time overhead. Furthermore, we demonstrate the capability of combining the two approaches to construct a formal model of the network in higher-order logic of the controlled system in a closed-loop.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0952197624013964/pdfft?md5=b6516cb377ac30878211183c4317c0a3&pid=1-s2.0-S0952197624013964-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142128745","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}

{"title":"An intelligent decision support framework for nursing home resource planning with enhanced heterogeneous service demand modeling","authors":"","doi":"10.1016/j.engappai.2024.109221","DOIUrl":"10.1016/j.engappai.2024.109221","url":null,"abstract":"<div><p>Demand-based nursing home resource planning is of great importance to ensure adequate resources (e.g., beds and staffs) available to provide care services with desired quality, yet challenging. The challenge mainly lies in modeling heterogeneous demand of nursing home residents, reflected by various individual characteristics, diverse dwelling duration with multiple competing discharge dispositions, and diverse daily service need. Existing studies often assumed a homogeneous population of patients and neglected the complexity of demand heterogeneity and uncertainty, leading to biased demand estimation and misguided decisions. The objective of this work is to improve nursing home resource planning decisions in response to the complex demand heterogeneity and uncertainty. To address the challenges, we propose a novel knowledge-guided and data-driven decision support framework. This is the first work of integrating domain knowledge with predictive and decision analytics to enhance modeling fidelity and decision performance for nursing home resource planning. Specifically, to effectively capture different aspects of heterogeneous demand, we develop a novel knowledge-guided demand modeling module with predictive models, including a length-of-stay model with competing risk for duration analysis, a tree-based system for learning daily service need variations, and a demand simulator for capturing uncertainty of fluctuating demand. Moreover, to determine optimal capacity and staffing decisions under demand heterogeneity and uncertainty, we develop a demand-based decision-making module with effective optimization models and solution algorithms, ensuring satisfactory quality of care at reduced costs. Furthermore, to demonstrate the improved prediction and decision performances of the proposed framework, we provide a proof-of-the-concept case study using real data from our industrial collaborator and investigate how demand heterogeneity and uncertainty will impact resource planning decisions. The proposed framework also demonstrates its appealing adaptability under changing resident census compositions.</p></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":7.5,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}