Applied Intelligence最新文献

{"title":"Real-time pickup and delivery scheduling for inter-island logistics using waterborne AGVs","authors":"Huarong Zheng,&nbsp;Jianpeng Tian,&nbsp;Anqing Wang,&nbsp;Dongfang Ma","doi":"10.1007/s10489-025-06570-7","DOIUrl":"10.1007/s10489-025-06570-7","url":null,"abstract":"<div><p>Archipelagic areas are in urgent need of efficient logistics systems to replace the limited bridges and fixed liners. This paper addresses the dynamic pickup and delivery challenge in inter-island logistics by utilizing waterborne autonomous guided vessels (AGVs). Specifically, the waterborne inter-island logistics problem is precisely expressed as a mixed integer programming (MIP) model. The model considers a variety of practical system constraints that could arise for the waterborne AGVs, e.g., capacity, time windows, berth allocation, and loading constraints. Moreover, in order to solve the possible large-scale scheduling problem dynamically and efficiently, we design an improved variable neighborhood search heuristic method. The approach is featured with four local search strategies and an effective perturbation heuristic to deal with the local minima issue. Extensive comparison experiments are carried out using real-world datasets. The results demonstrate that our algorithm outperforms baseline algorithms in 98% of cases, achieving improvements of over 10% compared to greedy rule-based methods and more than 5% over state-of-the-art heuristic algorithms, such as VNSME. These findings highlight the substantial benefits of the proposed technique, offering significant cost savings when effectively implemented. Comprehensive ablation experiments and parameter sensitivity analyses also demonstrate that the proposed algorithm has superior capabilities in space exploration and exploitation, provided that the step size operator is properly set. The proposed modeling and solution algorithms show great potential in enhancing the efficiency of the inter-island logistics systems.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861314","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":"DifNet: Difference-based multi-resolution decomposition for time series anomaly detection","authors":"Honglan Wang,&nbsp;Jing Li,&nbsp;Yu Chen,&nbsp;Xuxi Zou,&nbsp;Zeng Zeng,&nbsp;Jinlong Wu,&nbsp;Chenlin Pan,&nbsp;Yuqi Lu,&nbsp;Rongbin Gu,&nbsp;Xudong He,&nbsp;Rui Zhang","doi":"10.1007/s10489-025-06551-w","DOIUrl":"10.1007/s10489-025-06551-w","url":null,"abstract":"<div><p>Time series anomaly detection is crucial for Internet of Things (IoT) management and system security. Mainstream methods typically treat time series as an indivisible whole to capture insights into normal patterns. However, time series contain deterministic components such as seasonality, periodicity, and trends. Non-decomposition-based methods often average out these components during training, leading to the over-smoothing of normal time series features. This deviates from the core principle of unsupervised time series anomaly detection: to establish a clear boundary between normal and anomalous patterns. We propose DifNet, a comprehensive unsupervised time series anomaly detection method that effectively mitigates feature over-smoothing (FOS). DifNet adopts the concept of time series decomposition and utilizes Fast Fourier Transform (FFT) to analyze the periodic components of time series, guiding the decomposition and fusion process. Additionally, we design a difference-based multi-resolution decomposition network to thoroughly extract complex periodic dependencies within the time series. For multivariate time series data, DifNet follows the channel independence principle and disregards inter-channel dependencies that introduce redundant information in the data. As a more lightweight alternative, we introduce single-channel autoencoder and cross-channel periodicity adjustment. Meanwhile, DifNet integrates contrastive learning at a fine-grained level to prevent FOS and facilitate the extraction of more distinguishable representations. Extensive experimentation conducted on six multivariate and two univariate time series datasets validates the efficacy of DifNet in time series anomaly detection, DifNet achieved an average improvement in the best F1-score of 14.67% across eight datasets.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856436","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":"Context-dependent probabilistic linguistic multi-attribute decision-making methods","authors":"Yaojia Zhang,&nbsp;Zhinan Hao,&nbsp;Zaiwu Gong,&nbsp;Ren Zhang","doi":"10.1007/s10489-024-06059-9","DOIUrl":"10.1007/s10489-024-06059-9","url":null,"abstract":"<p>In the field of decision-making, the accurate assessment and integration of multiple attributes, particularly in scenarios characterized by uncertainty and subjectivity, pose a substantial challenge. Traditional decision-making methods within the probabilistic linguistic framework typically treat these as a series of independent single-attribute evaluations, thereby neglecting the crucial contextual information present within the attribute space. This paper introduces a context-dependent multi-attribute decision-making method, specifically designed for environments characterized by uncertainty and linguistic ambiguity. Our primary aim is to establish a decision-making framework that not only recognizes but also effectively utilizes the interdependencies and contextual subtleties among various attributes. To facilitate easier quantification of uncertainty in practical data, we initially define the Gaussian probabilistic linguistic term set and its corresponding generation algorithm. We then establish matrices that elucidate the dominant and dominated relationships between options across different attribute sets. These matrices are then incorporated into prospect theory, providing a comprehensive approach to multi-attribute decision-making. The effectiveness of our proposed method is demonstrated through a case study focusing on investment decision-making for countries participating in the Belt and Road Initiative.</p>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856481","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":"FedFAA: knowledge filtering for adaptive model aggregation in federated learning","authors":"Zihao Lu,&nbsp;Junli Wang,&nbsp;Mingjian Guang","doi":"10.1007/s10489-025-06530-1","DOIUrl":"10.1007/s10489-025-06530-1","url":null,"abstract":"<div><p>In federated learning, performing knowledge distillation on unlabeled proxy data is an effective way to aggregate local models into a global model. Most distillation-based methods assume that all knowledge from local models is contributory, and thereby indiscriminately transfer it to the global model. However, this assumption does not hold in data heterogeneity scenarios. Incorporating noisy knowledge during the distillation can negatively impact the performance of the global model. While filtering the knowledge be transferred is an intuitive solution, performing such filtering in federated learning is challenging due to the lack of available proxy-sample labels for knowledge validation. To address this issue, we propose a knowledge filtering approach for adaptive local model aggregation (FedFAA), which filters the knowledge before distillation based on its relevance. Specifically, we design a scoring method that exploits the representation space of a model to measure the relevance between the model knowledge and each proxy sample, without relying on validation labels. With these relevance scores, we further introduce an adaptive teacher model selection scheme that maintains an appropriate distribution of knowledge-providing teacher models across proxy samples, balancing the precision and diversity of the transferred knowledge after filtering. Theoretical analysis and extensive experiments demonstrate the effectiveness of our approach and its superior performance over six state-of-the-art methods.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143861271","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":"Dual-channel context-aware contrastive learning graph neural networks for session-based recommendation","authors":"Jiawei Cao,&nbsp;Yumin Fan,&nbsp;Tao Zhang,&nbsp;Jiahui Liu,&nbsp;Weihua Yuan,&nbsp;Xuanfeng Zhang,&nbsp;Zhijun Zhang","doi":"10.1007/s10489-024-06140-3","DOIUrl":"10.1007/s10489-024-06140-3","url":null,"abstract":"<div><p>Session-based recommendation (SR) aims to predict the next most likely interaction item based on the current sequence of anonymous behaviors. How to learn short- and long-term user preferences is the key to SR research. However, current research ignores the impact of contextual information on users’ short- and long-term preferences when obtaining user preferences. Herein, we propose a Dual-Channel Context-aware Contrastive Learning Graph Neural Networks (DCC-GNN) model for SR. DCC-GNN constructs a time-aware session graph representation learning channel, modeling sessions with temporal context information to learn users’ short-term preferences. To better capture users’ long-term preferences, it also constructs a position correction global graph representation learning channel and uses global session information to learn users’ long-term preferences. To address the issue of data sparsity, contrastive learning techniques are employed to both channels for data augmentation. Finally, a linear combination of the dual-channel session representations serves as the user’s ultimate preference for accurate recommendations. Herein, we performed extensive experiments on three real-world datasets. Experimental results reveal that the performance of the proposed DCC-GNN model demonstrates a considerable improvement compared to baseline models.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856670","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":"Deep learning based computer aided diagnosis (CAD) tool supported by explainable artificial intelligence for breast cancer exploration","authors":"Marwa Naas,&nbsp;Hiba Mzoughi,&nbsp;Ines Njeh,&nbsp;Mohamed Ben Slima","doi":"10.1007/s10489-025-06561-8","DOIUrl":"10.1007/s10489-025-06561-8","url":null,"abstract":"<div><p>Breast cancer (BC) is a leading cause of death among women, with breast ultrasound (BUS) commonly used for early detection. However, BUS images are often affected by speckle noise, low tissue contrast, and artifacts, which can compromise image analysis tasks like segmentation and classification. Nowadays, Deep Learning (DL)-based Computer-Aided Diagnosis (CAD) systems could significantly enhance clinical diagnosis by leveraging self-learning capabilities to extract a sophisticated hierarchy of features from images. However, DL models often lack transparency in their internal decision-making processes, which is critical for sensitive applications like breast imaging. To address this, Explainable Artificial Intelligence (XAI) has emerged as a key approach to make DL models more transparent and interpretable for clinicians. This paper presents an efficient and fully automated DL-based CAD tool enhanced by XAI techniques for the precise exploration and diagnosis of BC using ultrasound images. The proposed CAD involves four key-steps: preprocessing, segmentation, XAI-based explainability, and feature extraction. In the preprocessing phase, an Autoencoder-based architecture is explored to effectively reduce speckle noise. For segmentation, our approach introduces an optimized architecture inspired by the DeepLabV3 + model. To ensure transparency in the model's predictions, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to provide interpretable insights into the decisions made by the deep neural network. Lastly, relevant features are extracted using the Gray-Level Co-occurrence Matrix (GLCM) technique. The proposed approach was rigourously evaluated on two publicly available benchmark datasets. For the first dataset (A), the evaluation metrics achieved were as follows: Dice coefficient (0.979), accuracy (0.935), intersection over union (0.955), precision (0.984), F1 score (0.981), and recall (0.980). Similarly, for the second dataset (B), the model showed notable improvements, achieving a Dice coefficient (0.981), accuracy (0.974), intersection over union (0.963), precision (0.986), F1 score (0.985), and recall (0.983).These results highlight the exceptional performance of the optimized DeepLabV3 + model in segmentation tasks, outperforming both U-Net and ResidualUnet architectures.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856543","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":"Multistage decomposition transformer network for predicting complex long time series of heavy oil parameters","authors":"Xingpeng Zhang,&nbsp;Hongwei Liu,&nbsp;Bin Xiao,&nbsp;Min Wang,&nbsp;Bing Wang","doi":"10.1007/s10489-025-06413-5","DOIUrl":"10.1007/s10489-025-06413-5","url":null,"abstract":"<div><p>The primary indicators of heavy oil production include temperature, pressure, and various other factors, which exhibit rapid trend changes, erratic wave patterns, and irregular long-term behaviors, severely hindering accurate predictions. To effectively capture the long-term nature and complexity of heavy oil parameters, we propose a multistage decomposing transformer network (MDTN). The MDTN consists of two non-autoregressive decoders, an encoding structure with time encoding, and a time series parser. In this paper, we introduce a time series decomposition (TSD) strategy that breaks down complex long-time series into two simpler trend components and residual components. For the long-term analysis, we employ a local sensitive hash attention mechanism to further decompose these two components into multiple subsequences, followed by self-attention calculations for each subsequence. Additionally, to enable the model to fully leverage the temporal information of the sequence, we embed time, value, and position into each input layer of the encoder. To achieve rapid predictions and minimize error accumulation, we have designed a novel non-autoregressive decoder. Finally, the two sequences are combined through a convolution layer. A substantial number of experiments conducted on heavy oil parameter datasets and publicly available datasets demonstrate that the proposed method yields optimal results. For instance, in the complex long-term prediction of boiler temperature, the MAE value of the proposed method reaches 0.715 at the 1008 prediction step, which is nearly 0.1 lower than that of alternative methods.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852600","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":"Causal graph convolution neural differential equation for spatio-temporal time series prediction","authors":"Qipeng Wang,&nbsp;Shoubo Feng,&nbsp;Min Han","doi":"10.1007/s10489-025-06287-7","DOIUrl":"10.1007/s10489-025-06287-7","url":null,"abstract":"<div><p>Multivariate time series prediction has attracted wide research interest in recent decades. However, implicit spatial topology information and rich temporal evolution information bring many challenges to multivariate time series prediction. In this paper, a novel graph convolution module based on Granger causality is introduced to adaptively learn the causality between nodes. In detail, the ordinary differential equation (ODE) of a graph is used to model the propagation of spatial information between its nodes, and a temporal neural differential equation (NDE) is used to model the temporal evolution of the given nonlinear system. The Granger causality between multivariate time series is revealed by applying a multilayer perceptron (MLP) while imposing the <span>(L )</span>2 regularization constraint on the weights. A long short-term memory (LSTM)-based network is used as the nonlinear operator to reveal the underlying evolution mechanism of the input spatio-temporal time series. Furthermore, the forward Euler integration method is used to solve the graph ODE, which aims to enhance the representation ability of the proposed model while solving over-smoothing when the graph convolutional network (GCN) becomes too deep. The Euler trapezoidal integration method is used to simulate the evolution processes of dynamical systems and obtain the high-dimensional states of the medium and long-term prediction by solving the temporal NDE. The proposed model can explicitly discover the spatial correlations through its GCN-based causality module. We also combine the graph ODE module and the temporal NDE module to model the spatial information aggregation and temporal dynamic evolution processes, respectively, thus making the proposed model more interpretable. The experimental results demonstrate the effectiveness of our method in terms of spatio-temporal dynamic discovery and prediction performance.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852599","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":"Big data fusion with knowledge graph: a comprehensive overview","authors":"Jia Liu,&nbsp;Ruotian Lan,&nbsp;Yajun Du,&nbsp;Xipeng Yuan,&nbsp;Huan Xu,&nbsp;Tianrui Li,&nbsp;Wei Huang,&nbsp;Pengfei Zhang","doi":"10.1007/s10489-025-06549-4","DOIUrl":"10.1007/s10489-025-06549-4","url":null,"abstract":"<div><p>Along with the wide application of intelligent systems in various fields, the combination of data fusion and knowledge graph has become the key to enhance the system’s problem solving capability. However, existing data fusion methods still face challenges when dealing with multi-source heterogeneous data, especially in how to effectively combine knowledge graph. Therefore, this paper systematically reviews existing data fusion methods based on knowledge graph and classifies them into three categories: fusion of raw data, fusion of raw data with knowledge graph, and fusion of knowledge graphs. Each category of methods is described and analyzed in detail by combining a general framework with specific examples. In addition, this paper also discusses the future research direction of data fusion based on knowledge graph, and analyzes the challenges and opportunities it faces. This paper provides a theoretical framework and practical guidance for the problem of multi-source heterogeneous data fusion, and provides methodological support for the development of intelligent systems.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852602","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 comprehensive study on enhanced QR extraction techniques with deep learning-based verification","authors":"Nur Alam,&nbsp;A S M Sharifuzzaman Sagar,&nbsp;Wenqi Zhang,&nbsp;Taicheng Jin,&nbsp;Arailym Dosset,&nbsp;L. Minh Dang,&nbsp;Hyeonjoon Moon","doi":"10.1007/s10489-025-06509-y","DOIUrl":"10.1007/s10489-025-06509-y","url":null,"abstract":"<div><p>In the digital age, Quick Response (QR) codes have become essential in sectors such as digital payments and ticketing, propelled by advancements in Internet of Things (IoT) and deep learning. Despite their growing use, there are significant challenges in the accurate extraction and verification of QR codes, particularly in dynamic environments. Traditional methods struggle with issues like variable lighting, complex backgrounds, and counterfeits, which degrade the performance of QR code extraction and verification processes. This paper introduces a comprehensive approach that refines QR code extraction using enhanced adaptive thresholding techniques and incorporates a deep learning framework specifically tailored for robust QR code verification. Our methodology integrates dynamic window size adjustment, statistical weighting, and post-thresholding refinement to ensure precise QR code extraction under varying conditions. The verification process employs the ShuffleNetV2 network to ensure high performance with significantly low processing times suitable for real-time applications. Additionally, our deep learning model is trained on a comprehensive dataset comprising 28,523 images across 24 distinct QR code pattern classes, including variations in lighting, noise, and backgrounds to simulate real-world conditions. Experimental results demonstrate that our proposed methodology outperforms competing techniques in both processing speed and recognition accuracy, achieving a processing time of 0.08 seconds and a validation accuracy of 99.99% in constrained scenarios. Our approach shows an exceptional ability to distinguish real QR codes from counterfeits and highlights the significance and efficacy of our method in addressing contemporary challenges.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"55 7","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852603","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}