Applied Soft Computing最新文献

{"title":"Automated detection of tomato ripeness in natural environment using a coupled deep learning model of Sequencer and MLP","authors":"Faying Wu,&nbsp;Mingxuan Li,&nbsp;Chongda Zhou,&nbsp;Feifan Zhang","doi":"10.1016/j.asoc.2026.116038","DOIUrl":"10.1016/j.asoc.2026.116038","url":null,"abstract":"<div><div>Accurate tomato ripeness detection is crucial for automated harvesting, yet remains challenging under complex field conditions such as uneven illumination, occlusion, and background clutter. This paper proposes a lightweight coupled framework, Sequencer+MLP, where the attention modules are replaced by 2D BiLSTM-based Sequencer blocks to efficiently capture spatial dependencies, and a compact MLP head performs final classification. Experiments were conducted on a greenhouse dataset collected under diverse lighting and occlusion scenarios. The proposed method achieved 96.8% precision, 94.8% recall, and 98.9% classification mAP, while maintaining an inference latency of 0.6135<!--> <!-->s with a model size of 2.4 MB, indicating potential for practical deployment after further edge-device validation. Additional evaluations under illumination and shelter subsets further verified robustness, with mAP above 95%. These results demonstrate that the coupled Sequencer+MLP design provides an effective and efficient solution for tomato ripeness detection in natural agricultural environments. The MLP classifier achieved the best classification performance among the tested heads, indicating stable behavior across repeated trials.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"203 ","pages":"Article 116038"},"PeriodicalIF":7.8,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148575919","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":"Conditional generation-augmented multi-source domain generalization for rolling bearing RUL prediction via manifold learning-based health indicator construction and adaptive ensemble learning","authors":"Jimeng Li,&nbsp;Haobo Zhang,&nbsp;Hongliang Sun,&nbsp;Jinfeng Zhang","doi":"10.1016/j.asoc.2026.116003","DOIUrl":"10.1016/j.asoc.2026.116003","url":null,"abstract":"<div><div>Predictive maintenance of equipment relies on the accurate prediction of remaining useful life (RUL); however, fluctuating operating conditions in industrial scenarios often cause significant distribution shifts between training and testing data. Domain generalization (DG) can mitigate such domain shifts without requiring access to target domain test data. However, negative transfer caused by multi-source domain knowledge conflicts and inappropriate knowledge fusion has long been a key factor restricting the generalization and prediction accuracy of the model. To address these challenges, this paper proposes a novel DG framework for RUL prediction based on conditional generation-based augmentation. First, a robust health indicator is constructed via manifold learning, and a piecewise function based on degradation rate is designed to encode RUL labels. Second, a conditioned diffusion model incorporating hybrid source domain constraints is developed to generate task-relevant synthetic degradation-feature samples using source domain information, thereby enriching the training distribution and improving robustness to unseen operating-condition shifts. Third, a TimeSerFormer network integrating temporal convolutional networks and Transformer architectures within a main-auxiliary task design is proposed to capture multi-scale degradation features. Additionally, dual-distribution alignment and adaptive ensemble learning are employed to mitigate negative transfer across source domains. Experimental results on the IEEE PHM 2012 and XJTU-SY bearing datasets demonstrate that the proposed method achieves mean RMSE and mean MAE of 0.1248 and 0.0964, respectively, which outperforms several state-of-the-art DG methods and provides a promising solution for reliable cross-condition RUL prediction of rolling bearings.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"203 ","pages":"Article 116003"},"PeriodicalIF":7.8,"publicationDate":"2026-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148575917","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":"Emotion-Based Reinforcement Learning for Passivity-Based Control of Redundant Manipulators in Challenging Environments","authors":"Rami Al-Khulaidi,&nbsp;Tien-Fu Lu,&nbsp;Steven Grainger,&nbsp;Rini Akmeliawati","doi":"10.1016/j.asoc.2026.115997","DOIUrl":"10.1016/j.asoc.2026.115997","url":null,"abstract":"<div><div>Controlling redundant manipulators in unstructured environments like agricultural settings is challenging due to high nonlinearities and the need for both precision and stability. This paper presents a novel intelligent control architecture that integrates a stable-by-design Passivity-Based Control (PBC) law with a bio-inspired reinforcement learning tuner. The core novelty lies in our Human Learning Behaviour (HLB) model, which, unlike conventional reward shaping, employs a finite-state machine to dynamically modulate the exploration strategy of a Deep Deterministic Policy Gradient (DDPG) agent. This state machine transitions between discrete states, framed as artificial emotions (e.g., Joy, Fear), based on real-time performance indicators. Each state directly adjusts the agent's exploration noise, enabling it to learn more efficiently by exploring aggressively when performance is poor and exploiting known good policies when performance is high. This adaptive tuner intelligently adjusts the proportional (K_p) and differential (K_D) gains of the PBC law, which is derived from a Port-Controlled Hamiltonian (PCH) model to ensure the underlying manipulator dynamics remain stable for any positive-definite gains selected by the learning agent. Validated on an 8-DOF manipulator in simulated, obstacle-rich agricultural scenarios, the HLB-driven controller demonstrated a 77–80% improvement in trajectory tracking and a 30.7% reduction in energy consumption compared to fixed-gain and standard DDPG baselines, while maintaining a final tracking error of approximately 10⁻⁴ radians. The results confirm that our FSM-based adaptive exploration strategy yields a highly precise, energy-efficient, and robust intelligent control system suitable for complex robotic applications.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"203 ","pages":"Article 115997"},"PeriodicalIF":7.8,"publicationDate":"2026-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148575891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"On directional quantitative assessment of information uncertainty conveyed by interval-valued intuitionistic fuzzy sets","authors":"Grażyna Szkatuła,&nbsp;Maciej Krawczak","doi":"10.1016/j.asoc.2026.115971","DOIUrl":"10.1016/j.asoc.2026.115971","url":null,"abstract":"<div><div>This study presents a novel theoretical and conceptual evaluation framework for interval-valued intuitionistic fuzzy sets consisting of both global and directional components. This enables a more comprehensive analysis of information uncertainty by explicitly considering its structure and variability, treated here as inherent property of fuzzy data. The entire information uncertainty area includes all possible hypothetical changes in the degrees of membership and non-membership under a fixed hesitation parameter. The entire area is divided into three analytically distinct subregions related to changes in (i) the degree of non-membership, (ii) the degree of membership, and (iii) their simultaneous variation. Such decomposition allows for a multi-faceted analysis of the dynamics of information uncertainty, reflecting natural changes perceived from a human perspective. Based on this geometric interpretation, a global quantitative function for assessing information uncertainty and a family of directional assessment functions is developed. These functions assume values within a normalized range and serve as non-probabilistic indicators of both overall and directional information uncertainty. We also propose a ranking procedure based on information certainty. The developed methodology offers a conceptually transparent tool for quantifying information uncertainty, but its broad practical validity still requires confirmation and extensive computational experiments.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"203 ","pages":"Article 115971"},"PeriodicalIF":7.8,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148575843","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":"Measuring successful, failed and deceptive exploration of evolutionary algorithms based on attraction basins","authors":"Miha Ravber,&nbsp;Jernej Jerebic,&nbsp;Matej Črepinšek,&nbsp;Luka Mernik,&nbsp;Stephen Chen,&nbsp;Marjan Mernik","doi":"10.1016/j.asoc.2026.115973","DOIUrl":"10.1016/j.asoc.2026.115973","url":null,"abstract":"<div><div>This paper introduces a novel direct measure of exploration and exploitation in Evolutionary Algorithms, based on attraction basins — regions of the search space with an attractor toward which neighboring solutions tend to evolve. Each solution can be associated with a particular attraction basin. If a new solution belongs to the same attraction basin as its parent, then the search process is identified as exploitation; otherwise, it is identified as exploration. Four different exploration types definitions were used: successful, failed, deceptive, and successful rejection. These are measured directly at the individual level (without precomputing basins) on six single-objective continuous benchmark problems and a real-world problem using DE/rand/1/bin, JADE, LSHADE, ABC, and TLBO. The results revealed that EAs differentiate themselves in how they handle different types of exploration. The approach outperformed prior basin-based measures in efficiency, and exposed the limitations of diversity-based methods, enabling explicit quantification and control of the balance between exploration and exploitation.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"203 ","pages":"Article 115973"},"PeriodicalIF":7.8,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148575918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Industrial large model-driven online early fault warning: A novel incremental anomaly detection framework with wavelet scattering contrastive transformer","authors":"Ziyi Zhang,&nbsp;Yanna Zhang,&nbsp;Wentao Mao,&nbsp;Hui Ma,&nbsp;Jiayi Wang,&nbsp;Shubin Du","doi":"10.1016/j.asoc.2026.115975","DOIUrl":"10.1016/j.asoc.2026.115975","url":null,"abstract":"<div><div>Despite substantial potential of industrial large model (ILM), there are still some challenges for deploying ILM in specific industry scenarios: (1) Current ILMs generally adopt learning mode with large-scale parameters, which tends to raise over-fitting on limited data in real-world engineering and fails to generalize to various working conditions; (2) Current ILMs usually employ static deployment based on fine-tuning technique, which is hard to establish a dynamic downstream task with sequentially-collected monitoring data. To address these challenges, this paper selects the early fault detection (EFD) of rotating machinery as an example, and designs an ILM-driven incremental anomaly detection framework that includes a pre-training model and a downstream task. In the pre-training stage, a new wavelet scattering contrastive Transformer (WaveCT) is proposed by introducing wavelet scattering transform to expand physically-meaningful time-frequency feature set from limited data. Through exploring the contextual dependency among normal state data, WaveCT effectively extracts the representation of common one-class detection rules under complex working conditions such as being interference by irregular noise. In the downstream task, an online EFD network is designed based on fine-tuning the sequential data, while an incremental fault alarm indicator is built based on support vector data description (SVDD) generalization error upper bound. Then the statistical distribution shift caused by weak state changes is perceived to achieve early fault warning. Experiments on the vibration signals from our bearing cage testbed and the actual finishing mill bearing of steel enterprise from China validate the effectiveness of the proposed framework.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"203 ","pages":"Article 115975"},"PeriodicalIF":7.8,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148582986","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":"Distribution and expression aware retrospective learning for single-cell long-tailed class-incremental annotation","authors":"Tianhao Li ,&nbsp;Zixuan Wang ,&nbsp;Chenpeng Wu ,&nbsp;Zhengxiao Huang ,&nbsp;Yixin Xiang ,&nbsp;Yuhang Liu ,&nbsp;Quan Zou ,&nbsp;Naifeng Wen ,&nbsp;Yongqing Zhang","doi":"10.1016/j.asoc.2026.114745","DOIUrl":"10.1016/j.asoc.2026.114745","url":null,"abstract":"<div><div>Single-cell annotation, which identifies specific cell types in biological tissues, is a cornerstone of targeted therapy in precision medicine. However, the lack of incremental annotation capabilities in existing methods limits their adaptability to dynamic immune environment changes and broader applications. Additionally, the high-dimensional sparsity and long-tail distribution of single-cell data hinder practical long-tail incremental annotation. This paper introduces a single-cell incremental annotation framework that combines a distribution-aware diffusion model with an expression-aware knowledge distillation architecture to address these challenges. The distribution-aware module retrospects and replays historical data distributions, enabling precise generation of previously annotated cell types and mitigating catastrophic forgetting. The expression-aware module aligns gene representations through a multi-perspective attention mechanism, enhancing sensitivity to novel cell types while retaining knowledge of dominant categories. A fuzzy incremental guidance mechanism with uncertainty constraints further reduces the adverse effects of long-tail distributions, ensuring more robust annotation performance. Experimental results validate the effectiveness of this framework, demonstrating significant improvements in multi-session incremental annotation accuracy. In addition, this framework enhances the biological and clinical relevance of incremental annotation, enabling continuous integration of new datasets for improved discovery of rare cell types and disease progression analysis. This approach offers a scalable and generalizable solution to the challenges of long-tail incremental annotation in single-cell data.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"193 ","pages":"Article 114745"},"PeriodicalIF":6.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146196908","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":"Knowledge-guided course recommendation in MOOCs with cross-graph and forgetting-aware cross-attention","authors":"Yangtao Zhou ,&nbsp;Qingshan Li ,&nbsp;Ruoyu Li ,&nbsp;Hua Chu ,&nbsp;Jianan Li ,&nbsp;Feifei Zhu ,&nbsp;Xiangming Li ,&nbsp;Chengyu Feng ,&nbsp;Wanqiang Yang","doi":"10.1016/j.asoc.2026.114768","DOIUrl":"10.1016/j.asoc.2026.114768","url":null,"abstract":"<div><div>Course recommendation (CR) plays a pivotal role in the e-learning field, aiming at tailoring course suggestions to learners based on their historical interaction behaviors. Existing CR methods predominantly utilize generic recommendation techniques to infer learners’ preferences from historical interactions. In course learning scenarios, learners’ interaction behaviors are not only preference-driven but also significantly influenced by their knowledge states. However, how to effectively capture the learners’ knowledge states in the CR task remains unexplored, and presents significant challenges due to two critical reasons. First, the knowledge states are intertwined with learners’ preferences, exhibiting mutual collaborative influences. Second, the knowledge states of learners dynamically decay over time due to natural forgetting processes. In this paper, we propose a novel knowledge-guided course recommendation method, named KnowCR, to capture the dynamic knowledge states of learners for enhancing course recommendations. Specifically, we propose a cross-graph collaborative encoding module that integrates the representations of learners’ preferences and knowledge states to exploit their collaborative influences. This module constructs two distinct graphs based on learner-course and learner-exercise interactions, and facilitates information integration via a cross-graph propagation mechanism. Moreover, we devise a forgetting-aware cross-attention encoding module that simulates the decay of short-term and long-term memories to capture the knowledge forgetting properties of learners. This module extracts the dynamic representations of learners by performing sequential encoding with a hierarchical knowledge forgetting mechanism on time-aligned interaction sequences. Extensive experiments on a real-world MOOC dataset demonstrate that our method significantly outperforms the state-of-the-art methods. The implementation codes and datasets are available at <span><span>https://github.com/KasISET/KnowCR</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"193 ","pages":"Article 114768"},"PeriodicalIF":6.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146196904","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":"Towards explainable image composition assessment: a dataset and a model","authors":"Quan Yuan ,&nbsp;Yipo Huang ,&nbsp;Pengfei Chen ,&nbsp;Leida Li","doi":"10.1016/j.asoc.2026.114784","DOIUrl":"10.1016/j.asoc.2026.114784","url":null,"abstract":"<div><div>Explainable Image Composition Assessment (Explainable ICA) provides quantitative scores and qualitative explanations. While recent research on ICA has demonstrated remarkable performance in regressing a scalar score for the overall composition perception, we still need a better understanding of how the image compositions, especially their influencing factors, relate to high-level semantics. However, the requirement for a high-quality explainable ICA dataset limits the broad real-world applications like intelligent photography and content recommendation. This deficiency is primarily because the manual annotation is costly, time-consuming, and struggles to capture the inherent aesthetic subjectivity of composition which heavily relies on expert knowledge. In this work, we take a step toward addressing this challenge through the use of multimodal large language models, and construct a large-scale composition description dataset based on the well-designed prompts in a Chain-of-Thought pattern, named CADB Comments. During its development, we ensured diversity across scenes, composition elements, and levels. This dataset comprises 9497 structured textual annotations. Leveraging this dataset, we propose Multimodal Image Composition Assessment (MICA) to bridge quantitative scoring and qualitative explanation, enabling simultaneous output of scores and descriptions. MICA generates descriptions, embeds composition knowledge into visual encoder, and extracts composition representation using the knowledge-enhanced encoder. A composition level contrastive alignment module strengthens correlation between representation and scores via contrastive learning. Extensive experiments on three datasets demonstrate MICA’s competitive performance in explanation, scoring, and classification. The dataset and code are available at <span><span>https://github.com/dylanqyuan/MICA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"193 ","pages":"Article 114784"},"PeriodicalIF":6.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146196902","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":"A key node detection method assisted by multi-layer surrogate model and multi-dimensional network features in complex networks","authors":"Hong Zhao ,&nbsp;Ling Tang ,&nbsp;Jia-Rui Li ,&nbsp;Zong-Gan Chen","doi":"10.1016/j.asoc.2026.114803","DOIUrl":"10.1016/j.asoc.2026.114803","url":null,"abstract":"<div><div>Identifying key nodes in complex networks is a critical task, as these nodes significantly shape network structure and functionality. Despite the progress of existing methods, two major challenges remain: high computational complexity in large-scale networks and the insufficient adaptability of evaluation models to diverse network structures. To address these challenges, we propose a key node detection method assisted by multi-layer surrogate model and multi-dimensional network features (KND-MSMNF). Our method introduces a multi-layer surrogate model-assisted network performance evaluation (MSNE) strategy, which integrates a Kriging model and the <em>K</em>-nearest neighbors (KNN) model within a differential evolutionary (DE) algorithm. By dynamically employing different surrogate models at various stages of population evolution, MSNE approximates the objective function, thereby circumventing computationally expensive evaluations and significantly improving the efficiency of network assessment and optimization. Furthermore, an adaptive multi-dimensional network feature search (AMNFS) strategy is proposed to evaluate node importance. This AMNFS strategy dynamically adjusts attribute weights and metrics based on network characteristics, leveraging a comprehensive set of features to avoid bias of single evaluation criteria and enhance adaptability. Additionally, a multimodal optimization-driven key node selection (MKNS) strategy assesses node impact from a global perspective by considering both network cost and overall connectivity. By identifying multiple global optima, MKNS improves the robustness of the solution set and provides flexible alternatives for decision-makers. Extensive experiments on diverse network datasets demonstrate that KND-MSMNF significantly outperforms existing methods in terms of key node detection accuracy (i.e., exceeds 96 %) while maintaining strong practicality and adaptability. Moreover, our method can simultaneously identify multiple combinations of key nodes. This enables the rapid localization of corresponding alternative key nodes in the event of a key node failure, thereby effectively mitigating the risk of network functional impairment caused by the failure of a single node.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"193 ","pages":"Article 114803"},"PeriodicalIF":6.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146196905","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}