Applied Soft Computing最新文献

{"title":"Multiscale attention feature deep fusion network for iris region localization and segmentation from dual-spectral iris image","authors":"Shubin Guo ,&nbsp;Ying Chen ,&nbsp;Liang Xu ,&nbsp;Junkang Deng ,&nbsp;Huiling Chen ,&nbsp;Ali Asghar Heidari","doi":"10.1016/j.asoc.2025.113334","DOIUrl":"10.1016/j.asoc.2025.113334","url":null,"abstract":"<div><div>The precision of iris localization and segmentation is crucial for the accuracy of iris recognition. With the evolution of iris recognition technology and the diversification of application scenarios, it is imperative to improve the performance of algorithms further to address both existing and emerging challenges and issues. In response, this article introduces a lightweight multiscale attention feature deep fusion network (MA-DFNet), designed for multitask segmentation, simultaneously locating both internal and external iris boundaries and generating iris masks. The primary contributions of this work are as follows: Firstly, to provide a more robust feature representation when facing complex scenarios, a multiscale feature extractor is designed in the middle layer, and a gated attention pyramid is incorporated into the skip connections. These designs realize dynamic and adaptive feature selection and emphasis, thereby achieving precise iris region localization and segmentation. Secondly, extensive experiments were performed on iris datasets collected under two near-infrared (CASIA-Iris-M1 and CASIA.v4-distance) and two visible spectra (MICHE-I and UBIRIS.v2). These experiments demonstrate that MA-DFNet, achieves excellent performance in both segmentation and localization tasks in dual-spectrum iris images, outperforming existing state-of-the-art networks. Finally, this study raised objections to the annotations of the publicly available ground truth (GT) images, arguing that inaccurate annotations are adverse to the development of iris recognition technology. In contrast, the segmentation and localization results of MA-DFNet were more rational than GT, which gets validated by comparing recognition experiment results from both.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"179 ","pages":"Article 113334"},"PeriodicalIF":7.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Short-time photovoltaic power forecasting based on Informer model integrating Attention Mechanism","authors":"Weijie Yu ,&nbsp;Yeming Dai ,&nbsp;Tao Ren ,&nbsp;Mingming Leng","doi":"10.1016/j.asoc.2025.113345","DOIUrl":"10.1016/j.asoc.2025.113345","url":null,"abstract":"<div><div>Precise Photovoltaic Power Generation Forecasting (PVGF) is significant for achieving reliable power supply, optimizing energy scheduling, and responding to changing energy market demand for sustainable development. However, Photovoltaic Power (PV) is vulnerable to changes in solar radiation levels and temperature, then result in electricity generation fluctuations. To further enhance the precision of PVGF, we propose a new short-term PVGF method based on Informer model integrating attention mechanism. Firstly, Locally Weighted Scatterplot Smoothing (LOWESS) is introduced to preprocess data, enhancing the stability of the input data. Secondly, Feature Engineering (FE) is used for feature screening. Thirdly, Informer model is improved, termed as Attention-Informer-Attention (AT-Informer-AT) model. Specifically, Attention mechanism (AM) layer is added to the encoder and decoder of Informer model respectively, allowing the model to flexibly adjust the attention to different time series data and effectively capture important patterns in the PV data, thereby enhancing prediction performance and generalization ability. Eventually, the novel prediction approach’s efficiency is confirmed through analyzing the cases of two different power stations in DKASC area, Alice Springs, Australia and Xuhui District, Shanghai, China. The Experimental results demonstrate that the proposed method superiors other models, with the best prediction accuracy and generalization ability.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"178 ","pages":"Article 113345"},"PeriodicalIF":7.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144155113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Reinforcement-learning-based parallel genetic algorithm for the multi-objective hot-rolling scheduling problem of wide-thick slab","authors":"Zhuolun Zhang ,&nbsp;Bailin Wang ,&nbsp;Shuaipeng Yuan ,&nbsp;Yiren Li ,&nbsp;Xiqing Wang ,&nbsp;Tieke Li","doi":"10.1016/j.asoc.2025.113355","DOIUrl":"10.1016/j.asoc.2025.113355","url":null,"abstract":"<div><div>Hot rolling is the core process of steel production, and its scheduling problem is the key to determining the rolling rhythm. Compared with traditional hot-rolling scheduling, hot-rolling scheduling optimisation of wide-thick slabs needs to consider the characteristics of cross rolling and the influence of the reheating furnace, which significantly increase the difficulty of problem-solving. In this paper, the characteristics of cross rolling and the influence of the reheating furnace are analysed, and the hot-rolling scheduling problem of the wide-thick slab (WTS-HRSP) is mapped to a multi-objective asymmetric vehicle routing problem (MAVRP) model. Combined with the multi-objective characteristics of MAVRP, a reinforcement-learning-based parallel genetic algorithm (RPGA) was designed, which implements parallel computation of multiple populations based on the master-slave island model, designs three priority rules to initialise populations, and optimises the parameters of the genetic operation using Q-Learning. Based on the rolling data of the wide-thick slab hot-rolling mill of a Chinese iron and steel group, experiments were conducted to compare RPGA with five advanced multi-objective optimisation algorithms, and the results showed that RPGA had better convergence and stability and was more suitable for the WTS-HRSP. The findings can help optimise hot-rolling scheduling.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"179 ","pages":"Article 113355"},"PeriodicalIF":7.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Extracting hierarchical relationships of aspects from reviews","authors":"Jiangtao Qiu ,&nbsp;Ling Lin ,&nbsp;Siyu Wang","doi":"10.1016/j.asoc.2025.113335","DOIUrl":"10.1016/j.asoc.2025.113335","url":null,"abstract":"<div><div>Aspect Based Sentiment Analysis (ABSA) attracts significant attention in recent years. Three elements of ABSA including aspect term, aspect, and domain (or entity) present a hierarchical relationships in e-commerce reviews. Extracting the hierarchical relationships can significantly enhance various applications, such as creating user profiles, identifying hierarchical topics, and visualizing review data. In this study, we proposed a framework to tackle this task, consisting of two primary components: a text adversarial autoencoder that efficiently encodes review content, and a deep network that extracts the clusters of aspect terms from review dataset and organizes them to a hierarchical structure using the Student-Teacher paradigm. Our framework also addresses the challenge of acquiring labeled training data by utilizing self-supervised learning. We evaluated the proposed framework on three public datasets and observed that it outperforms baseline models, indicating the feasibility and effectiveness of our approach.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"178 ","pages":"Article 113335"},"PeriodicalIF":7.2,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144155109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Path-planning algorithm for small environmental surveillance unmanned surface vehicles","authors":"Zhenyang Wang,&nbsp;Ping Yang,&nbsp;Diju Gao,&nbsp;Chunteng Bao","doi":"10.1016/j.asoc.2025.113342","DOIUrl":"10.1016/j.asoc.2025.113342","url":null,"abstract":"<div><div>Ports are essential hub facilities that provide support for economic development. However, the construction, development, and operation of ports increase the risk of environmental pollution in marine areas. Small environmental surveillance unmanned surface vehicles (ESUSVs) are being deployed to monitor port environments and prevent pollution. This study proposes a bidirectional elastic force contraction algorithm (BEFCA) and a Lévy flight weighted whale optimization (LFWWOA) and BEFCA hybrid algorithm (LFWWOA-BEFCA) to solve the path planning problem of ESUSVs. BEFCA solves the slow convergence and unsmooth path-characteristic problem of the elastic force contraction algorithm (EFCA) by employing a bidirectional search strategy and ship kinematics to smoothen the turning points in the path, respectively. LFWWOA uses a Lévy flight-based strategy in the global exploration phase of the whale optimization algorithm (WOA) to increase the solution diversity and improves the global and local search performance by modifying the coefficient calculation method and adding adaptive weighting coefficients. Thirteen benchmark functions were selected for the LFWWOA optimization performance experiments and compared with other intelligence algorithms. The results demonstrate that the proposed algorithm achieved the best global performance. Therefore, LFWWOA was used to optimize the BEFCA parameters, which resulted in higher-quality planned paths. Simulation experiments of real scenarios and complex environments showed that the path lengths and algorithm runtimes of BEFCA and LFWWOA-BEFCA outperformed those of the state prediction rapidly exploring random trees (spRRT) and spRRT-informed algorithms, respectively. The planned paths are consistent with the motion characteristics of ESUSVs, which can be used directly for tracking. The findings of this study indicate that shorter travel paths can be planned for ESUSVs in harbors for environmental monitoring, effectively solving the difficulty of tracking the paths of ESUSVs, and reducing energy consumption during the travel process.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"178 ","pages":"Article 113342"},"PeriodicalIF":7.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Diffusion-based road defect detection model integrating edge information and efficient multi-scale convolution","authors":"Xueqiu Wang ,&nbsp;Huanbing Gao ,&nbsp;Zemeng Jia","doi":"10.1016/j.asoc.2025.113332","DOIUrl":"10.1016/j.asoc.2025.113332","url":null,"abstract":"<div><div>Roads are vital infrastructure components, and the prompt detection and repair of defects are critical for their longevity and safety. This paper introduces the Edge Efficient Multi-Scale Focusing Diffusion Network (EEFNet), a precise method for road defect detection. The Edge Information Enhancement Module (EIEM) accentuates crack contours while minimizing background noise. An Efficient Multi-Scale Convolution (EMSConv) is proposed. The EMSConv captures features across multiple scales, thereby enhancing model efficiency through reduced computational demands and parameter count. The Focusing Diffusion Pyramid Network (FDPN) collects and distributes context-rich features across various scales using a diffusion mechanism, thereby improving detection capabilities. Additionally, the Task Dynamic Align Detection Head (TADDH) facilitates parameter sharing among detection heads, which enhances classification and localization accuracy. EEFNet has demonstrated a 92.7 % accuracy rate at 126 FPS (Frames Per Second) on a road defect dataset and has proven robustness on several diverse datasets including Unmanned Aerial Vehicle Asphalt Pavement Distress Dataset (UAPD), Visual Object Classes 2007 (VOC2007), Global Road Damage Detection2022 (GRDD2022), and Vision Meets Drone 2019 (Visdrone2019). In addition, by pruning the model and deploying it onto edge computing devices, practical experiments have demonstrated that the EEFNet model has substantial practical application value.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"178 ","pages":"Article 113332"},"PeriodicalIF":7.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Signature kernel ridge regression time series model: A novel approach for hydrological drought modeling using multi-station meteorological drought information","authors":"Mir Jafar Sadegh Safari ,&nbsp;Shervin Rahimzadeh Arashloo ,&nbsp;Babak Vaheddoost","doi":"10.1016/j.asoc.2025.113343","DOIUrl":"10.1016/j.asoc.2025.113343","url":null,"abstract":"<div><div>In the context of growing environmental challenges and the need for sustainable water resource management, hydrological drought prediction has gained prominence as a critical issue. Existing artificial intelligence and time series-based models for hydrological drought indices have traditionally been established using streamflow data. This study gives a significant progress in hydrological drought modeling through the introduction of the Signature Kernel Ridge Regression (SKRR) time series model. Instead of directly using rainfall and runoff data to develop a rainfall-runoff (RR) model, the Standardized Precipitation Evapotranspiration Index (SPEI) values in neighbor meteorological stations serve as inputs for estimating the Streamflow Drought Index (SDI) in target hydrometric stations, considering the 3-, 6-, and 12-month moving average time windows. The objective of this study is to enhance hydrological drought modeling by integrating soft computing techniques that effectively handle multivariate and irregular time series. The efficacy of the SKRR is compared with the well-established Generalized Regression Neural Network (GRNN), Random Forest (RF), and Auto Regressive Integrated Moving Average model with eXogenous input (ARIMAX). The findings indicate that SKRR is capable of precisely estimating SDI in three hydrometric stations using meteorological drought information from 14 stations, outperforming the GRNN, RF and ARIMAX models. The enhanced performance of the SKRR time series model stems from the utilization of a new and effective <em>signature kernel</em> which can be utilized for the study of irregularly sampled, multivariate time series in addition to be applicable to time series of different temporal spans while being a positive-definite kernel, facilitating usage in the Hilbert space. The novel drought based-RR model established by SKRR utilized various external stations’ meteorological drought indices to compute the hydrological drought indices in target stations not only enhances the modeling capability but also progress our understanding of drought dynamics by showcasing the power of soft computing in handling environmental uncertainty. Furthermore, it offers visions for developing of adaptive and resilience strategies to lessen the hazards caused by drought phenomenon.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"178 ","pages":"Article 113343"},"PeriodicalIF":7.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144155114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"TAS-TsC: A data-driven framework for Estimating Time of Arrival using Temporal-Attribute-Spatial Tri-space Coordination of truck trajectories","authors":"Mengran Li ,&nbsp;Junzhou Chen ,&nbsp;Guanying Jiang ,&nbsp;Fuliang Li ,&nbsp;Ronghui Zhang ,&nbsp;Siyuan Gong ,&nbsp;Zhihan Lv","doi":"10.1016/j.asoc.2025.113214","DOIUrl":"10.1016/j.asoc.2025.113214","url":null,"abstract":"<div><div>Accurately estimating the time of arrival (ETA) for trucks is crucial for optimizing transportation efficiency in logistics. GPS trajectory data provides valuable information for ETA, but challenges arise due to temporal sparsity, variable sequence lengths, and the interdependencies among multiple trucks. To address these issues, we propose the Temporal-Attribute-Spatial Tri-space Coordination (TAS-TsC) framework, which leverages three feature spaces – temporal, attribute, and spatial – to enhance ETA. Our framework consists of a Temporal Learning Module (TLM) that uses state space models to capture temporal dependencies, an Attribute Extraction Module (AEM) that transforms sequential features into structured attribute embeddings, and a Spatial Fusion Module (SFM) that models the interactions among multiple trajectories using graph representation learning. These modules collaboratively learn trajectory embeddings, which are then used by a Downstream Prediction Module (DPM) to estimate arrival times. We validate TAS-TsC on real truck trajectory datasets collected from Shenzhen, China, demonstrating its superior performance compared to existing methods.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"178 ","pages":"Article 113214"},"PeriodicalIF":7.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Incorporating vision-based artificial intelligence and large language model for smart traffic light control","authors":"Jiarong Yao ,&nbsp;Jiangpeng Li ,&nbsp;Xiaoyu Xu ,&nbsp;Chaopeng Tan ,&nbsp;Kim Hui Yap ,&nbsp;Rong Su","doi":"10.1016/j.asoc.2025.113333","DOIUrl":"10.1016/j.asoc.2025.113333","url":null,"abstract":"<div><div>The increasingly complicated urban traffic patterns lead traffic signal control to a new trend of higher flexibility and quicker response, which becomes possible with advances in both sensor technology and artificial intelligence. Though in its early stage, existing intelligent signal controllers equipped with reinforcement learning (RL)-based feature extractor and large language model (LLM)-driven scenario understanding and decision support already demonstrate powerful data digesting ability. This study thus proposes a smart traffic light control system integrating a vision-based perception tool to extract traffic state from real-time snapshot image of the intersection, and an LLM agent controller for signal phase switching upon scenario analysis. An indicator describing the urgency for green time at phase level is defined to abstract the contextual information regarding the competition of multiple approaching traffic flows, which augments the LLM with domain-specific logical reasoning for signal control action generation, aimed at assigning green time to the flows with the most compelling needs. With a RL-based controller providing initial control decision as backup, the proposed method is able to handle both pre-trained and out-of-distribution scenarios through real-time traffic state diagnosis and knowledgeable reasoning. Simulation evaluation on different intersection layouts and vehicle compositions is conducted with horizontal comparison of five benchmarks. A decrease in average waiting time was realized by more than 5 % under normal traffic scenario and 20 % under emergency vehicle scenario, respectively. Further, comprehensive analysis was conducted to explore the applicability of the proposed method and feasibility for real-world application in unmanned aerial vehicle (UAV)-based intelligent traffic management.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"179 ","pages":"Article 113333"},"PeriodicalIF":7.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Explainable AI-enhanced machine learning for CBR prediction in stabilized and unstabilized subgrade soils","authors":"Ishwor Thapa,&nbsp;Sufyan Ghani","doi":"10.1016/j.asoc.2025.113275","DOIUrl":"10.1016/j.asoc.2025.113275","url":null,"abstract":"<div><div>This study presents a novel framework integrating explainable artificial intelligence (XAI) techniques with machine learning (ML) models to predict the California Bearing Ratio (CBR) of subgrade soils, addressing the \"black-box\" limitation in traditional ML applications. Five ML models, CatBoost, XGBoost, Random Forest, LightGBM, and Categorical Mixture Density Networks (CasMDN), were employed to predict the CBR of unstabilized and nano-silica stabilized soils. Quantitative results revealed that CatBoost achieved the highest predictive accuracy with an R² of 0.98, RMSE of 0.07, and IOA of 1.0 for unstabilized soils, outperforming the other models across multiple metrics. For nano-silica stabilized soils, CatBoost also led with an R² of 0.81 and an RMSE of 0.32, showing its robustness for varying soil types. Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were employed to provide both global and local interpretability, illustrating the key features influencing CBR predictions. The findings from SHAP and LIME indicate that sand content and plastic limit are the most influential factors in CBR estimation, with variable feature importance across the soil types. A user-friendly application was developed by integrating these explainable ML models, thereby enabling rapid, reliable, and interpretable CBR predictions for practical geotechnical applications. This study’s insights enhance transparency and foster trust in ML models, paving the way for their wider adoption in infrastructure design and soil stability assessment.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"178 ","pages":"Article 113275"},"PeriodicalIF":7.2,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}