Knowledge-Based Systems最新文献_第4页

Capsule based regressor network for multivariate short term weather forecasting 基于胶囊的多元短期天气预报回归网络

IF 7.6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Knowledge-Based Systems

Pub Date : 2026-04-08 Epub Date: 2026-02-05 DOI: 10.1016/j.knosys.2026.115439

Arjun Mallick, Arkadeep De, Arpan Nandi, Asif Iqbal Middya, Sarbani Roy

The problem of weather forecasting has always been in the forefront of studies regarding time series analysis and has a long trailing history of applied techniques due to its immense importance in science as well as our socio-economic lives. In recent years, short-term weather forecasting has evolved rapidly, driven by availability of enormous amounts of data, exponential lift in computation feasibility, and theoretical progress in machine learning. The paradigm of weather forecasting is gradually shifting from simulation-based physics modeling methods to more data centric methods - resulting in more accurate and real time forecasts. This study frames the weather forecasting problem as a multivariate time series problem specific to a fixed location and introduces the method of dynamic routing between capsules to the weather forecasting paradigm. Learning from some selected parameters and their inter dependencies, the capsule regressor network forecasts the temperature, humidity, wind speed, sea level pressure and vapor pressure for next timesteps. We have rigorously compared its performance against all broader varieties of neural networks which have been applied in weather forecasting - and it was observed that the capsule regressor worked fairly well within the forecast horizon of 120 h. It outperformed all other baselines in 48 h and 72 h forecast horizons and remained close to best in other timesteps. The study also portrays a measure of the genericness of models predicting different features with unique characteristics and across all horizons, where the capsule network was found to be the most consistent.

天气预报问题一直处于时间序列分析研究的前沿，由于其在科学以及我们的社会经济生活中的巨大重要性，它具有悠久的应用技术历史。近年来，由于大量数据的可用性、计算可行性的指数提升以及机器学习的理论进步，短期天气预报发展迅速。天气预报的模式正逐渐从基于仿真的物理建模方法转向以数据为中心的方法，从而实现更准确、更实时的预报。本研究将天气预报问题作为一个特定于固定位置的多变量时间序列问题，并将胶囊之间的动态路由方法引入天气预报范式。从一些选定的参数及其相互依赖关系中学习，胶囊回归网络预测下一个时间步的温度、湿度、风速、海平面压力和蒸汽压。我们将其性能与应用于天气预报的所有更广泛的神经网络进行了严格的比较，并观察到胶囊回归器在120小时的预测范围内工作得相当好。在48小时和72小时的预测范围内，它的表现优于所有其他基线，并在其他时间步长中保持接近最佳。该研究还描绘了一种模型的通用性，该模型预测了具有独特特征的不同特征，并跨越了所有的视野，其中胶囊网络被发现是最一致的。

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引用次数: 0

Confusion-Calibrated Cross-Entropy and Class-Specialized Aggregation for Robust Federated Learning under Extreme Data Heterogeneity 基于混淆校正的交叉熵和类专用聚合的极端数据异构鲁棒联邦学习

IF 7.6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Knowledge-Based Systems

Pub Date : 2026-04-08 Epub Date: 2026-02-05 DOI: 10.1016/j.knosys.2026.115497

Sujit Chowdhury , Raju Halder

Federated Learning (FL) enables privacy-preserving collaborative model training across decentralized devices without exchanging raw data. However, its effectiveness is severely hampered by data heterogeneity – particularly label distribution skew, missing classes, and data sparsity-which causes model divergence, poor generalization, and unfair performance across classes. Traditional FL methods rely on uniform aggregation and standard loss functions that fail to account for local biases and class-level struggles, leading to catastrophic degradation under realistic data heterogeneity conditions. To this end, we introduce FedCA, a federated learning framework that jointly mitigates label skew, missing classes, and data sparsity through two synergistic components: a Confusion-Calibrated Cross-Entropy (C3E) loss for client-side training and a struggling-class-prioritized top-kaggregation scheme at the server. C3E dynamically calibrates local objectives using an adaptive, on-device soft confusion matrix that penalizes persistent misclassifications to correct client-level biases. Complementing this, the aggregation module leverages a compact struggler signal from clients to prioritize updates targeting the most challenging classes across the federation, enhancing both robustness and fairness. Extensive experiments on MNIST, CIFAR-10, and CIFAR-100 demonstrate that FedCA significantly outperforms state-of-the-art baselines. Under extreme label distribution skew, FedCA achieves a 20.12% absolute accuracy advantage on CIFAR-10. It maintains 63.1% accuracy when clients hold only two classes-a regime where baselines fail-and reaches target accuracies in up to 4 × fewer rounds. These results validate FedCA as a statistically grounded, communication-efficient, and robust solution for high-performance federated learning under realistic data heterogeneity conditions.

联邦学习（FL）可以在不交换原始数据的情况下跨分散设备进行保护隐私的协作模型训练。然而，它的有效性受到数据异质性的严重阻碍——特别是标签分布倾斜、缺失类和数据稀疏性——这会导致模型分歧、泛化不良和跨类的不公平性能。传统的FL方法依赖于统一的聚合和标准损失函数，这些函数不能考虑局部偏差和类水平的斗争，导致在实际数据异质性条件下的灾难性退化。为此，我们引入了FedCA，这是一个联邦学习框架，它通过两个协同组件共同减轻了标签倾斜、缺失类和数据稀疏性：用于客户端训练的混淆校准交叉熵（C3E）损失和服务器端的挣扎类优先级顶级聚合方案。C3E使用自适应的、设备上的软混淆矩阵来动态校准本地目标，该矩阵会惩罚持续的错误分类，以纠正客户级偏差。与此相补充的是，聚合模块利用来自客户端的紧凑的挣扎者信号来优先考虑针对整个联邦中最具挑战性的类的更新，从而增强了鲁棒性和公平性。在MNIST、CIFAR-10和CIFAR-100上进行的大量实验表明，FedCA的性能明显优于最先进的基线。在极端标签分布偏斜的情况下，FedCA在CIFAR-10上获得了20.12%的绝对精度优势。当客户端只持有两个类别（基线失效的情况）时，它保持63.1%的准确性，并在最多4次 × 少轮中达到目标准确性。这些结果验证了FedCA是一种基于统计的、通信高效的、健壮的解决方案，适用于实际数据异构条件下的高性能联邦学习。

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引用次数: 0

Enabling nearshore cross-modal video object detector to learn more accurate spatial and temporal information 使近岸跨模态视频目标检测器能够学习更准确的时空信息

IF 7.6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Knowledge-Based Systems

Pub Date : 2026-04-08 Epub Date: 2026-02-06 DOI: 10.1016/j.knosys.2026.115426

Yuanlin Zhao , Jiangang Ding , Yansong Wang , Yihui Shan , Lili Pei , Wei Li

Nearshore scenarios are frequently affected by fog and contain a variety of objects exhibiting distinct motion patterns. These inherent factors pose significant challenges for accurate object detection in nearshore scenarios. Common approach is to utilize Video Object Detection (VOD) to learn the spatial features and motion information of nearshore objects. However, this method becomes hindered in situations involving foggy conditions or when different objects share similar optical characteristics, thus impeding effective pipeline modeling. To address these challenges, we propose a nearshore Cross-modal Video Object Detector (CVODNet). By leveraging learnable feature interaction between Infrared (IR) and visible light videos, we reduce the obstacles in pipeline modeling caused by the transient loss of features from unimodal. Learning from correlated frames to obtain the optimal weights for moving objects. Finally, deformable convolution is employed to address the challenges of pixel-level misalignment in cross-modal data presented in video form. After end-to-end training, CVODNet achieves State-of-the-art (SOTA) performance in benchmark evaluations.

近岸场景经常受到雾的影响，并且包含各种表现出不同运动模式的物体。这些固有因素对近岸场景中精确的目标检测提出了重大挑战。常用的方法是利用视频目标检测（Video Object Detection， VOD）来学习近岸目标的空间特征和运动信息。然而，这种方法在有雾或不同物体具有相似光学特性的情况下会受到阻碍，从而阻碍了有效的管道建模。为了解决这些挑战，我们提出了一种近岸跨模态视频对象检测器（CVODNet）。通过利用红外（IR）和可见光视频之间的可学习特征交互，我们减少了由于单峰特征的瞬时损失而导致的管道建模障碍。从相关帧中学习以获得运动对象的最优权值。最后，采用可变形卷积来解决以视频形式呈现的跨模态数据中的像素级不对齐问题。经过端到端训练，CVODNet在基准评估中达到了最先进（SOTA）的性能。

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引用次数: 0

Diff-GDAformer: A diffusion-guided dynamic attention transformer for image inpainting Diff-GDAformer：一种用于图像绘制的扩散引导动态注意力转换器

IF 7.6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Knowledge-Based Systems

Pub Date : 2026-04-08 Epub Date: 2026-01-31 DOI: 10.1016/j.knosys.2026.115443

Hao Wu, Shuzhen Xu, Cuicui Lv, Yuanwei Bi, Zhizhong Liu, Shuo Wang

Diffusion model (DM) has shown great promise in image inpainting by modeling complex data distributions and generating high-quality reconstructions. However, current diffusion-based methods often face challenges such as excessive iterative steps and limited adaptability to both local and global features, resulting in high computational costs and suboptimal restoration quality. To address these issues, we propose Diff-GDAformer, a novel image inpainting framework that combines diffusion-based prior feature generation with guided dynamic attention Transformer (GDAformer) for robust and efficient restoration. In our approach, the DM iteratively refines Gaussian noise in a compressed latent space to generate high-quality prior features, which guide the restoration process. These prior features are injected into GDAformer, which innovatively adopts a dynamic recursive local attention (DRLA) module. DRLA makes use of two complementary attention mechanisms: guided local self-attention (GL-SA) and guided recursive-generalized self-attention (GRG-SA). GL-SA enhances the model’s ability to capture fine-grained local details, while GRG-SA focuses on aggregating global contextual information efficiently. To bridge the gap between local and global features, we introduce the hybrid feature integration (HFI) module, which effectively fuses features from different attention layers, enabling a more comprehensive understanding of image contexts. The two-stage training strategy combines GDAformer with DM optimization, ensuring that the extracted prior features are accurate and seamlessly integrated into the restoration pipeline. Extensive experiments demonstrate that Diff-GDAformer achieves state-of-the-art performance on standard benchmarks, delivering superior visual quality and computational efficiency compared to existing methods. https://github.com/w1zzzzzWu/Diff-GDAformer.

扩散模型（DM）通过建模复杂的数据分布和生成高质量的重建图像，在图像绘制中显示出巨大的前景。然而，目前基于扩散的方法往往面临迭代步骤过多、对局部和全局特征的适应性有限等挑战，导致计算成本高、恢复质量欠佳。为了解决这些问题，我们提出了Diff-GDAformer，这是一种新的图像修复框架，将基于扩散的先验特征生成与引导动态注意力转换器（GDAformer）相结合，以实现鲁棒和高效的恢复。在我们的方法中，DM迭代地细化压缩潜在空间中的高斯噪声以生成高质量的先验特征，这些特征指导恢复过程。将这些先验特征注入到GDAformer中，创新地采用了动态递归局部注意（DRLA）模块。DRLA使用了两种互补的注意机制：引导局部自注意（GL-SA）和引导递归-广义自注意（GRG-SA）。GL-SA增强了模型捕获细粒度局部细节的能力，而GRG-SA侧重于有效地聚合全局上下文信息。为了弥合局部和全局特征之间的差距，我们引入了混合特征集成（HFI）模块，该模块有效地融合了来自不同关注层的特征，从而能够更全面地理解图像上下文。两阶段训练策略将GDAformer与DM优化相结合，确保提取的先验特征准确且无缝集成到恢复管道中。大量实验表明，与现有方法相比，Diff-GDAformer在标准基准测试中实现了最先进的性能，提供了卓越的视觉质量和计算效率。https://github.com/w1zzzzzWu/Diff-GDAformer。

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引用次数: 0

A meta-heuristic stochastic algorithm for the numerical treatment of cancer model through the chemotherapy and stem cells 通过化疗和干细胞对癌症模型进行数值治疗的元启发式随机算法

IF 7.6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Knowledge-Based Systems

Pub Date : 2026-04-08 Epub Date: 2026-02-04 DOI: 10.1016/j.knosys.2026.115493

Zulqurnain Sabir , M.A. Abdelkawy , Dumitru Baleanu , Ozlem Defterli

Objective

The aim of current research is to present the numerical performances of the cancer treatment model based on chemotherapy and stem cells using one of the heuristic computing neural network procedures. The cancer treatment model through chemotherapy and stem cells is categorized into stem cells, affected cells, tumor cells, and chemotherapy-based concentration drug.

Method

A process of artificial neural network is applied using the hybrid optimization of global and local search schemes, which are taken as genetic algorithm (GA) and an active set (AS). An error-based fitness function is designed by using the differential model and then optimized by the hybridization of both global and local search schemes. GA is applied to exploit the global result and give a primary guess to the AS that further improves the results locally. AS is rooted in the GA, where GA produces new populaces and AS optimizes the fitness function for every individual. The hybridization of these two schemes is used iteratively for purifying the results. Ten numbers of neurons and log-sigmoid activation functions has been used to solve the cancer treatment model based on chemotherapy and stem cells.

Results

For the correctness of the stochastic solver, the obtained numerical results have been compared with any traditional scheme. Moreover, the reliability and capability of the scheme are performed through the absolute error around 10^-05 to 10^-07 along with different statistical approaches for solving the mathematical model.

Novelty

The proposed artificial neural network structure along with the hybrid optimization of global and local search schemes has never been implemented before to solve the cancer treatment model based on chemotherapy and stem cells.

目的利用一种启发式计算神经网络程序，对基于化疗和干细胞的肿瘤治疗模型进行数值计算。通过化疗和干细胞治疗癌症的模式分为干细胞、受累细胞、肿瘤细胞和以化疗为基础的浓缩药物。方法采用遗传算法（GA）和活动集（as）两种全局和局部搜索混合优化的人工神经网络处理方法。利用差分模型设计了基于误差的适应度函数，并结合全局和局部搜索方案进行了优化。利用遗传算法对全局结果进行挖掘，并对自治系统进行初步猜测，进一步对局部结果进行改进。AS以遗传算法为基础，遗传算法产生新的种群，并对每个个体的适应度函数进行优化。用这两种方案的杂交迭代来净化结果。10个神经元和对数s型激活函数被用来解决基于化疗和干细胞的癌症治疗模型。结果为了验证随机解算器的正确性，将所得到的数值结果与任何传统格式进行了比较。采用不同的统计方法求解数学模型，通过对10-05 ~ 10-07之间的绝对误差，验证了方案的可靠性和性能。本文提出的人工神经网络结构以及全局和局部混合优化搜索方案在解决基于化疗和干细胞的癌症治疗模型中是前所未有的。

{"title":"A meta-heuristic stochastic algorithm for the numerical treatment of cancer model through the chemotherapy and stem cells","authors":"Zulqurnain Sabir , M.A. Abdelkawy , Dumitru Baleanu , Ozlem Defterli","doi":"10.1016/j.knosys.2026.115493","DOIUrl":"10.1016/j.knosys.2026.115493","url":null,"abstract":"<div><h3>Objective</h3><div>The aim of current research is to present the numerical performances of the cancer treatment model based on chemotherapy and stem cells using one of the heuristic computing neural network procedures. The cancer treatment model through chemotherapy and stem cells is categorized into stem cells, affected cells, tumor cells, and chemotherapy-based concentration drug.</div></div><div><h3>Method</h3><div>A process of artificial neural network is applied using the hybrid optimization of global and local search schemes, which are taken as genetic algorithm (GA) and an active set (AS). An error-based fitness function is designed by using the differential model and then optimized by the hybridization of both global and local search schemes. GA is applied to exploit the global result and give a primary guess to the AS that further improves the results locally. AS is rooted in the GA, where GA produces new populaces and AS optimizes the fitness function for every individual. The hybridization of these two schemes is used iteratively for purifying the results. Ten numbers of neurons and log-sigmoid activation functions has been used to solve the cancer treatment model based on chemotherapy and stem cells.</div></div><div><h3>Results</h3><div>For the correctness of the stochastic solver, the obtained numerical results have been compared with any traditional scheme. Moreover, the reliability and capability of the scheme are performed through the absolute error around 10<sup>-05</sup> to 10<sup>-07</sup> along with different statistical approaches for solving the mathematical model.</div></div><div><h3>Novelty</h3><div>The proposed artificial neural network structure along with the hybrid optimization of global and local search schemes has never been implemented before to solve the cancer treatment model based on chemotherapy and stem cells.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"338 ","pages":"Article 115493"},"PeriodicalIF":7.6,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174780","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}

引用次数: 0

A software-defined wrapper discriminant federated learning-reinforcement attention adversarial regression approach for privacy and task management in cloud edge computing 一种用于云边缘计算隐私和任务管理的软件定义包装器判别联合学习强化注意对抗回归方法

IF 7.6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Knowledge-Based Systems

Pub Date : 2026-04-08 Epub Date: 2026-02-02 DOI: 10.1016/j.knosys.2026.115430

K. Thangaraj , Koppula Srinivas Rao , Kayam Saikumar , Shruti Garg

In recent years, the widespread adoption of the Internet of Things has played an important role in advancing artificial intelligence by continuously generating large volumes of data used for model training and decision-making processes. The conventional cloud edge computing paradigm faces challenges in handling the massive data generated by Internet of Things. These challenges include high latency, excessive bandwidth usage, limited scalability, and privacy risks. To rectify these limitations, this research proposes a novel Software-defined Wrapper Discriminant Federated learning-Reinforcement Attention Adversarial Regression (SWDF-RAAR) model. Unlike other existing studies, the SWDF-RAAR model jointly addresses privacy analysis and intelligent resource task management within a unified structure. For privacy analysis, the model integrates software-defined networking, linear discriminant analysis with wrapper-style bi-directional removal technique, federated learning, and extreme learning machines. Resource task management is performed using a combination of dynamic perceptrons, scaled dot-product attention, a multilayer perceptron with graph convolution, deep Q-learning aided by generative adversarial networks, and a support vector machine. The main contribution of this model is to develop a scalable, lightweight, and privacy-preserving model for detecting intrusions while enhancing the efficiency of resource task management in cloud edge computing environments. Experimental analysis on various security based datasets and cloud-edge parameters demonstrated that the proposed model attained 98.73% detection accuracy, 93% scalability, 95% quality of service, 96% network efficiency, and 5.3 ms latency.

近年来，物联网的广泛采用，通过不断产生用于模型训练和决策过程的大量数据，在推进人工智能方面发挥了重要作用。传统的云边缘计算模式在处理物联网产生的海量数据时面临挑战。这些挑战包括高延迟、过多的带宽使用、有限的可伸缩性和隐私风险。为了纠正这些局限性，本研究提出了一种新的软件定义包装器判别联邦学习-强化注意对抗回归（SWDF-RAAR）模型。与其他现有研究不同，SWDF-RAAR模型在统一的结构中联合解决了隐私分析和智能资源任务管理。对于隐私分析，该模型集成了软件定义网络、线性判别分析（带有包装式双向删除技术）、联邦学习和极限学习机。资源任务管理使用动态感知器、缩放点积注意、具有图卷积的多层感知器、生成对抗网络辅助的深度q学习和支持向量机的组合来执行。该模型的主要贡献是开发了一种可扩展、轻量级和隐私保护的模型，用于检测入侵，同时提高了云边缘计算环境中资源任务管理的效率。在各种基于安全的数据集和云边缘参数上的实验分析表明，该模型达到了98.73%的检测准确率、93%的可扩展性、95%的服务质量、96%的网络效率和5.3 ms的延迟。

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引用次数: 0

Visual and textual spaces both matter: Taming CLIP for non-IID federated medical image classification 视觉和文本空间都很重要：驯服CLIP用于非iid联合医学图像分类

IF 7.6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Knowledge-Based Systems

Pub Date : 2026-04-08 Epub Date: 2026-02-10 DOI: 10.1016/j.knosys.2026.115524

Lulu Feng , Shengchao Chen

Federated Learning-based medical image analysis system can offer significant insights to enhance privacy-preserving computer-aided diagnosis (CAD) by accessing both public and private medical data. Adapting pre-trained Vision-Language Foundation Models like CLIP for federated learning-based medical image analysis offers cross-modal insights that boost decision support compared to unimodal visual models. However, effective cross-domain federated adaptation requires intensive fine-tuning and knowledge sharing, challenging in low-resource medical practice due to the divergence between pretrained natural image knowledge and medical imagery. Moreover, the significant statistical heterogeneity (non-IID) of medical data exacerbates these challenges. To address these issues, this paper introduces a Parallel Multimodal Reinforcement framework (PMRFed) that tames CLIP for non-IID federated medical image classification. PMRFed develops client-specific personalized models by reinforcement and constrain local cross-modal alignment, enabling the models to integrate client-specific and globally common knowledge. This approach not only addresses non-IID challenges but also optimizes the trade-off between performance and efficiency. Extensive experiments on real-world medical image classification datasets demonstrate the effectiveness and superiority of our proposed PMRFed.

基于联邦学习的医学图像分析系统可以通过访问公共和私人医疗数据，为增强保护隐私的计算机辅助诊断（CAD）提供重要见解。将预训练的视觉语言基础模型（如CLIP）用于基于联邦学习的医学图像分析，可以提供跨模态的见解，与单模态视觉模型相比，可以提高决策支持。然而，有效的跨域联合自适应需要密集的微调和知识共享，由于预训练的自然图像知识与医学图像之间的差异，在资源匮乏的医疗实践中具有挑战性。此外，医疗数据的显著统计异质性（非iid）加剧了这些挑战。为了解决这些问题，本文引入了一个并行多模态强化框架（PMRFed），该框架将CLIP命名为非iid联邦医学图像分类。PMRFed通过强化和约束局部跨模态对齐来开发客户特定的个性化模型，使模型能够集成客户特定的和全球通用的知识。这种方法不仅解决了非iid挑战，还优化了性能和效率之间的权衡。在实际医学图像分类数据集上的大量实验证明了我们提出的PMRFed的有效性和优越性。

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引用次数: 0

Innovative optimization-driven machine learning models for hourly streamflow forecasting 创新优化驱动的机器学习模型，用于每小时流量预测

IF 7.6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Knowledge-Based Systems

Pub Date : 2026-04-08 Epub Date: 2026-02-02 DOI: 10.1016/j.knosys.2026.115487

Peiman Parisouj , Changhyun Jun , Sayed M. Bateni , Shunlin Liang

This study introduces a novel framework for short-term streamflow forecasting by integrating multilayer perceptron (MLP) and gradient boosting (GB) models with artificial rabbit optimization (ARO) and the honey badger algorithm (HBA). The proposed framework addresses a critical need for accurate flood forecasting by providing a robust alternative to complex physical models. The methodology is applied to the flood-prone Chehalis Basin in the U.S. using 2011–2023 hydrometeorological data, including precipitation, temperature, humidity, wind speed, and streamflow. The study systematically evaluates the impact of input data quality and quantity by testing two model configurations: base models (M1 and M2) with simpler inputs, and upgraded models (M3, M4, and M5) with more complex features. The optimized HBA-MLP hybrid model achieves 1–6 h streamflow forecasts with root mean square error (RMSE) values of 1.87–7.58

(m^{3} / s)

and

R^{2}

of 0.99–1.0 during testing on 2019–2023 data, which was excluded from training. On average, the MLP models using M5 inputs demonstrate a 58 % lower RMSE and 22.6 % lower mean absolute error (MAE) compared to GB models. The HBA-MLP M5 model excels in predicting extreme flow events, addressing a key challenge in hydrological forecasting. Furthermore, the proposed framework outperformed the National Water Model (NWM), especially during high-flow periods, making it more suitable for real-time flood forecasting. Overall, this study demonstrates how machine learning models, when combined with optimization techniques, can enhance the accuracy and reliability of flood forecasting systems, facilitating more effective flood mitigation strategies in similar basins.

本研究通过将多层感知器（MLP）和梯度增强（GB）模型与人工兔子优化（ARO）和蜜獾算法（HBA）相结合，提出了一种新的短期流量预测框架。提出的框架通过提供复杂物理模型的可靠替代方案，解决了精确洪水预报的关键需求。该方法使用2011-2023年水文气象数据，包括降水、温度、湿度、风速和流量，应用于美国易发生洪水的Chehalis盆地。本研究通过测试两种模型配置，即输入更简单的基础模型（M1和M2）和特征更复杂的升级模型（M3、M4和M5），系统地评估了输入数据质量和数量的影响。优化后的HBA-MLP混合模型在2019-2023年排除训练数据的测试中，实现了1-6 h的流量预测，均方根误差（RMSE）为1.87-7.58 (m3/s)， R2为0.99-1.0。平均而言，与GB模型相比，使用M5输入的MLP模型显示RMSE降低58%，平均绝对误差（MAE）降低22.6%。HBA-MLP M5模型在预测极端流量事件方面表现出色，解决了水文预测中的一个关键挑战。此外，所提出的框架优于国家水模型（NWM），特别是在高流量时期，使其更适合实时洪水预报。总体而言，本研究表明，机器学习模型与优化技术相结合，可以提高洪水预报系统的准确性和可靠性，从而在类似流域促进更有效的洪水缓解策略。

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引用次数: 0

Graph-Prototype distillation with prototype-Guided contrastive training for multimodal emotion recognition in conversations 对话中多模态情感识别的图-原型升华与原型引导对比训练

IF 7.6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Knowledge-Based Systems

Pub Date : 2026-04-08 Epub Date: 2026-02-04 DOI: 10.1016/j.knosys.2026.115484

Bengong Yu , Jun Wang , Chenyue Li , Zhonghao Xi , Xianxian Zhao , Yue Li

Multimodal Emotion Recognition in Conversations aims to determine utterance-level emotions robustly when heterogeneous textual, acoustic, and visual signals intertwine and the dialogue context evolves across turns. Although graph-based dialogue methods have made progress, decision calibration, geometric alignment, and class-level organization are often modeled in isolation when modality conflicts coexist with cross-turn context shifts. This promotes information diffusion and structural redundancy, thereby hampering the separability of weakly distinguishable emotions and overall robustness. To address these issues, we introduce Graph-Prototype Distillation with Prototype-Guided Contrastive Training (GPGC), which jointly constrains representation alignment, distributional consistency, and prototype alignment on a unified intra-modal graph-aggregated representation, thereby tightening intra-class dispersion from both probabilistic and geometric perspectives and stabilizing class-prototype directions. Prototype-guided momentum contrast is further employed to leverage a cross-batch stable dictionary and guided positives to consistently enlarge margins against hard negatives while reducing the interference of noisy samples during optimization. Systematic evaluations on two widely used MERC benchmarks and an in-the-wild multimodal sentiment benchmark demonstrate consistent improvements in both overall performance and stability.

对话中的多模态情感识别旨在识别异质文本、声音和视觉信号交织在一起、对话语境跨回合演变时的话语级情感。尽管基于图的对话方法已经取得了进展，但当模态冲突与交叉转弯上下文转换共存时，决策校准、几何对齐和类级组织往往是孤立的建模。这促进了信息扩散和结构冗余，从而阻碍了弱可区分情绪的可分离性和整体鲁棒性。为了解决这些问题，我们引入了带有原型引导对比训练（GPGC）的图-原型蒸馏，它在统一的模态内图聚合表示上联合约束表示对齐、分布一致性和原型对齐，从而从概率和几何角度加强类内分散，并稳定类-原型方向。原型引导动量对比进一步用于利用跨批稳定字典和引导阳性，以一致地扩大硬阴性的边际，同时减少优化过程中噪声样本的干扰。对两个广泛使用的MERC基准和一个野外多模态情绪基准的系统评估表明，两者在整体性能和稳定性方面都有一致的改进。

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引用次数: 0

Multi-objective optimization approach with decomposition-based algorithm for selecting tagSNPs 基于分解算法的标签snp选择多目标优化方法

IF 7.6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Knowledge-Based Systems

Pub Date : 2026-04-08 Epub Date: 2026-02-04 DOI: 10.1016/j.knosys.2026.115471

María Victoria Díaz-Galián, Miguel A. Vega-Rodríguez, Sergio Santander-Jiménez

Nowadays multiple bioinformatics issues can be solved by using evolutionary computation due to its potential to address complex optimization problems. TagSNP selection lies within this class of challenging problems, since genotyping all the Single Nucleotide Polymorphisms (SNPs) for haplotype identification is economically costly and time-consuming. If a reduced number of tagSNPs is chosen instead, the classification of haplotypes will accordingly show a worsening. As a result, tagSNP selection can be considered as a multi-objective optimization problem, in which the aim is to optimize haplotype dissimilarity while minimizing the number of selected tagSNPs. We propose and detail an approach based on the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) for accurately selecting tagSNPs attending to these two objectives. The proposed method includes novel problem-aware operators for the initialization, crossover, and mutation to boost optimization capabilities. The proposal is experimentally compared with six approaches from the literature on five real datasets, using in the evaluation three quality metrics and their corresponding statistical analyses. The attained results denote that our algorithm provides statistically-significant improvements over previous methods with competitive runtimes, thus highlighting the relevance of the proposed multi-objective approach.

目前，由于进化计算具有解决复杂优化问题的潜力，许多生物信息学问题可以通过进化计算来解决。标签snp选择属于这类具有挑战性的问题，因为对所有单核苷酸多态性（snp）进行基因分型以进行单倍型鉴定既经济又耗时。如果选择较少数量的标签snp，单倍型的分类将相应地恶化。因此，标签snp选择可以看作是一个多目标优化问题，其目的是优化单倍型不相似性，同时使选择的标签snp数量最小化。我们提出并详细介绍了一种基于基于分解的多目标进化算法（MOEA/D）的方法，用于准确选择符合这两个目标的标记snp。提出的方法包括新的问题感知算子，用于初始化、交叉和突变，以提高优化能力。在5个真实数据集上，将该方法与文献中的6种方法进行了实验比较，使用了3种质量度量及其相应的统计分析。所获得的结果表明，我们的算法在具有竞争运行时间的情况下提供了统计上显着的改进，从而突出了所提出的多目标方法的相关性。

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