Knowledge-Based Systems最新文献_第9页

Toward more effective bag-of-functions architectures: Exploring initialization and sparse parameter representation 迈向更有效的函数袋架构：探索初始化和稀疏参数表示

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-27 DOI: 10.1016/j.knosys.2025.114536

David Orlando Salazar Torres, Diyar Altinses, Andreas Schwung

{"title":"Toward more effective bag-of-functions architectures: Exploring initialization and sparse parameter representation","authors":"David Orlando Salazar Torres, Diyar Altinses, Andreas Schwung","doi":"10.1016/j.knosys.2025.114536","DOIUrl":"10.1016/j.knosys.2025.114536","url":null,"abstract":"<div><div>Time series datasets often present complex temporal patterns that challenge both feature extraction and interpretability. The Bag-of-Functions (BoF) architecture has emerged as a promising approach to model such data by capturing diverse dynamics through functional components. However, its effectiveness is constrained by limitations in both interpretability and training stability. In this work, we address these challenges by introducing two complementary contributions: a regularization strategy that promotes sparse and interpretable parameter representations, and a tailored initialization scheme based on the Kaiming method adapted to the properties of BoF models. Our proposed initialization ensures improved convergence behavior and training stability, while the regularization enhances the clarity and semantic interpretability of the learned components. Evaluations on synthetic and real-world time series datasets demonstrate that these improvements preserve model performance and generalize well across varying signal complexities. Together, these strategies provide a more robust and interpretable foundation for Bag-of-Functions architectures in time series decomposition tasks.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114536"},"PeriodicalIF":7.6,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269363","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

TreeQA: Enhanced LLM-RAG with logic tree reasoning for reliable and interpretable multi-hop question answering TreeQA：增强LLM-RAG与逻辑树推理可靠和可解释的多跳问题回答

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-27 DOI: 10.1016/j.knosys.2025.114526

Xiangrui Zhang , Fuyong Zhao , Yutian Liu , Panfeng Chen , Yanhao Wang , Xiaohua Wang , Dan Ma , Huarong Xu , Mei Chen , Hui Li

{"title":"TreeQA: Enhanced LLM-RAG with logic tree reasoning for reliable and interpretable multi-hop question answering","authors":"Xiangrui Zhang , Fuyong Zhao , Yutian Liu , Panfeng Chen , Yanhao Wang , Xiaohua Wang , Dan Ma , Huarong Xu , Mei Chen , Hui Li","doi":"10.1016/j.knosys.2025.114526","DOIUrl":"10.1016/j.knosys.2025.114526","url":null,"abstract":"<div><div>Multi-Hop Question Answering (MHQA), crucial for complex information retrieval, remains challenging for current Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems, which often suffer from hallucination, reliance on incomplete knowledge, and opaque reasoning processes. Existing RAG methods, while beneficial, still struggle with the intricacies of multi-step inference and ensuring verifiable accuracy. This research introduces TreeQA, a novel framework designed to significantly enhance the reliability and interpretability of LLM-RAG systems in MHQA tasks. TreeQA addresses these limitations by decomposing complex multi-hop questions into a hierarchical logic tree of simpler, verifiable sub-questions, integrating evidence from both structured knowledge bases (e.g., Wikidata) and unstructured text (e.g., Wikipedia), and employing an iterative, evidence-based validation and self-correction mechanism at each reasoning step to dynamically rectify errors and prevent their accumulation. Extensive experiments on four benchmark datasets (WebQSP, QALD-en, AdvHotpotQA, and 2WikiMultiHopQA) demonstrate TreeQA’s superior performance, achieving Hit@1 scores of 87 %, 57 %, 53 %, and 59 %, respectively, representing improvements of 4 %-12 % over state-of-the-art LLM-RAG methods. These findings highlight the significant impact of structured, verifiable reasoning pathways in developing more robust, accurate, and interpretable knowledge-intensive AI systems, thereby enhancing the practical utility of LLMs in complex reasoning scenarios. Our code is publicly available at <span><span>https://github.com/ACMISLab/TreeQA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114526"},"PeriodicalIF":7.6,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268713","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 topology-aware multiscale feature fusion network for EEG-based motor imagery decoding 基于脑电图的多尺度特征融合网络

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-26 DOI: 10.1016/j.knosys.2025.114540

Chaowen Shen, Akio Namiki

{"title":"A topology-aware multiscale feature fusion network for EEG-based motor imagery decoding","authors":"Chaowen Shen, Akio Namiki","doi":"10.1016/j.knosys.2025.114540","DOIUrl":"10.1016/j.knosys.2025.114540","url":null,"abstract":"<div><div>Motor imagery electroencephalography (MI-EEG) decoding is a crucial component of brain-computer interface (BCI) systems, serving as a valuable tool for motor function rehabilitation and fundamental neuroscience research. However, the strong nonlinearity and non-stationarity of MI-EEG signals make achieving high-precision decoding a challenging task. Current deep learning methods primarily extract the spatiotemporal features of MI-EEG signals while neglecting their potential association with spectral-topological features, thereby limiting the ability to integrate multidimensional information. To address these limitations, this paper proposes a Topology-Aware Multiscale Feature Fusion network (TA-MFF network) for MI-EEG signal decoding. Specifically, we designed a Spectral-Topological Data Analysis-Processing (S-TDA-P) module that leverages persistent homology features to analyze the spatial topological relationships between EEG electrodes and the persistent patterns of neural activity. Then, the Inter Spectral Recursive Attention (ISRA) mechanism is employed to model the correlations between different frequency bands, enhancing critical spectral features while suppressing irrelevant noise. Finally, the Spectral-Topological and Spatio-Temporal Feature Fusion (SS-FF) Unit is employed to progressively integrate topological, spectral, and spatiotemporal features, capturing dependencies across different domains. The experimental results show that the classification accuracy of the proposed model in BCIC-IV-2a, BCIC-IV-2b, and BCIC-III-Iva is 85.87 %, 90.2 %, and 80.5 %, respectively, outperforming the most advanced methods.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114540"},"PeriodicalIF":7.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222173","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

Hierarchical reinforcement learning for dynamic collision avoidance of autonomous ships under uncertain scenarios 不确定情景下自主船舶动态避碰的分层强化学习

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-26 DOI: 10.1016/j.knosys.2025.114528

Sijin Yu, Yunbo Li, Jiaye Gong

{"title":"Hierarchical reinforcement learning for dynamic collision avoidance of autonomous ships under uncertain scenarios","authors":"Sijin Yu, Yunbo Li, Jiaye Gong","doi":"10.1016/j.knosys.2025.114528","DOIUrl":"10.1016/j.knosys.2025.114528","url":null,"abstract":"<div><div>Autonomous ships hold substantial potential for enhancing navigational safety, improving collision avoidance efficiency, and increasing adaptability in complex maritime environments, thereby presenting broad prospects for intelligent shipping. This paper introduces a dynamic collision avoidance control method based on a hierarchical reinforcement learning framework for autonomous ships. By integrating high-level global intent planning with low-level fine-grained rudder control, the proposed approach markedly enhances the interpretability, stability, and behavioral consistency of the learned policy. Furthermore, a multidimensional uncertainty modeling mechanism is incorporated during training, systematically accounting for variations in initial states and obstacle behavior patterns, which effectively strengthens policy adaptability and generalization under uncertain conditions. To validate the method, simulations are conducted in representative encounter scenarios as well as in omnidirectional dynamic obstacle tests. A comprehensive evaluation is carried out using multiple control performance metrics, environmental adaptability analysis, policy consistency assessment, and equivalent energy consumption comparisons. The results confirm that the proposed approach achieves stable and reliable intelligent collision avoidance control in highly dynamic environments, offering a feasible and scalable solution for high-performance collision avoidance in intelligent maritime navigation.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114528"},"PeriodicalIF":7.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269714","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

Few-Shot hyperspectral image classification with mamba and manifold convolution fusion network 基于曼巴和流形卷积融合网络的少镜头高光谱图像分类

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-26 DOI: 10.1016/j.knosys.2025.114531

Heling Cao , Yanlong Guo , Yonghe Chu , Yun Wang , Junyi Duan , Peng Li

{"title":"Few-Shot hyperspectral image classification with mamba and manifold convolution fusion network","authors":"Heling Cao , Yanlong Guo , Yonghe Chu , Yun Wang , Junyi Duan , Peng Li","doi":"10.1016/j.knosys.2025.114531","DOIUrl":"10.1016/j.knosys.2025.114531","url":null,"abstract":"<div><div>Efficient modeling of global-local features is crucial for hyperspectral image (HSI) classification. The mamba network demonstrates strong capability in capturing global dependencies in HSI classification tasks, primarily utilizing a state-space model to extract first-order statistical features of spectral-spatial information in euclidean space, providing an initial representation of data characteristics. However, under few-shot conditions, fully exploiting effective features from limited samples and overcoming challenges such as class overlap and feature space sparsity caused by the insufficient extraction of second-order statistical features in riemannian space remain major research challenges. Therefore, we propose a dual branch manifold convolution-mamba network (DBMCMamba) for HSI classification. Specifically, it adaptively fuses forward and backward information through the vision mamba (Vim) block and utilizes the S6 module to extract global information, thereby enhancing global feature extraction capability. Meanwhile, the manifold convolution module extracts first-order statistical features of spectral-spatial information through convolutional layers and learns second-order statistics via the SPD manifold to strengthen DBMCMamba’s local feature representation under few-shot conditions. Finally, global and local features are fused for classification, effectively improving the accuracy and performance of HSI classification. On the Indian Pines, Pavia University, HongHu, and HanChuan datasets, DBMCMamba achieved classification accuracies of 95.23 %, 95.80 %, 95.58 %, and 94.93 %, respectively. Experimental results show that DBMCMamba demonstrates significant performance improvements compared to the state-of-the-art classification models. The code will be available online at <span><span>https://github.com/ASDFFGG121EAA/DBMCMamba</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114531"},"PeriodicalIF":7.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222170","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

Heterogeneous graph collaborative representation learning for drug-related microbe prediction with attentive fusion and reciprocal distillation 基于注意融合和互反蒸馏的药物相关微生物预测异构图协同表示学习

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-25 DOI: 10.1016/j.knosys.2025.114548

Yanbu Guo , Quanming Guo , Shengli Song , Yihan Wang , Jinde Cao

{"title":"Heterogeneous graph collaborative representation learning for drug-related microbe prediction with attentive fusion and reciprocal distillation","authors":"Yanbu Guo , Quanming Guo , Shengli Song , Yihan Wang , Jinde Cao","doi":"10.1016/j.knosys.2025.114548","DOIUrl":"10.1016/j.knosys.2025.114548","url":null,"abstract":"<div><div>Microbes are microorganisms with biological molecules and have significant therapeutic potential for treating diseases, underscoring the need for computational methods to screen microbes targeting disease-associated drugs. However, the computational methods often consider node embedding or structure features between microbes and drugs, and have a severe class imbalance problem inherent in sparse association data. In this work, we proposed a heterogeneous graph collaborative representation learning model that combines the merits of attentive fusion and reciprocal distillation for drug-related microbe prediction. First, we constructed the heterogeneous biological information and meta-path-induced graphs of microbes and drugs. Then, a topological structure feature encoder is devised to extract complex topological and semantic interaction patterns from heterogeneous biological graphs with microbes and drugs, while an efficient transformer concurrently extracts discriminative semantic and structural information based on the graph position information of nodes. Next, a reciprocal distillation schema is developed to mitigate the adverse effects of the data imbalance problem, and enable the distribution consistency of the model between topological and semantic information extraction. Moreover, we devised a dual collaborative feature fusion schema that combines graph topological and dual meta-path-based semantic features to obtain the discriminative features of microbes and drugs. Through reciprocal distillation, an efficient optimization function focuses on hard-to-classify samples of drug-related microbes via discriminative features. Extensive experiments demonstrate that our model could deal with the association sparsity problem and extract more semantics and structure. Meanwhile, case studies indicate that our model could discover reliable candidate microbes associated with a special drug.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114548"},"PeriodicalIF":7.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222223","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

Dynamic pick-up point recommendation with multi-modal deep forest and incentive-based adaptive Kuhn-Munkres Algorithm 基于多模态深度森林和基于激励的自适应Kuhn-Munkres算法的动态拾取点推荐

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-25 DOI: 10.1016/j.knosys.2025.114543

Yuhan Guo , Rushi Zhu , Wenhua Li , Youssef Boulaksil , Hamid Allaoui

{"title":"Dynamic pick-up point recommendation with multi-modal deep forest and incentive-based adaptive Kuhn-Munkres Algorithm","authors":"Yuhan Guo , Rushi Zhu , Wenhua Li , Youssef Boulaksil , Hamid Allaoui","doi":"10.1016/j.knosys.2025.114543","DOIUrl":"10.1016/j.knosys.2025.114543","url":null,"abstract":"<div><div>Recommendations for optimal pick-up points significantly enhance service efficiency, reduce economic and temporal costs, and alleviate traffic congestion. However, spatiotemporal imbalance between ride-hailing supply and passenger demand presents significant challenges. Current models often overlook critical influencing factors such as passenger satisfaction, travel environment, and travel cost factors. Moreover, solution algorithms, including exact algorithms and heuristics, struggle to achieve global optimality and computational efficiency in large-scale scenarios. This study introduces a comprehensive mathematical model that incorporates four key influencing factors: passenger walking distance, passenger waiting time, traffic conditions, and estimated ride-hailing fare. The solution approach consists of a novel pick-up point evaluation algorithm and an incentive-based adaptive Kuhn-Munkres matching algorithm. The evaluation algorithm employs a multi-modal decision tree structure, enhanced by deep learning techniques to improve the accuracy of pick-up point evaluations. The matching algorithm features a multi-scenario adaptive mechanism that dynamically adjusts edge weights and selects optimal edges for augmentation under various conditions and strategies, thereby ensuring globally optimal matching of passengers and pick-up points. Extensive experiments on large-scale real-world datasets validate the superior performance of the evaluation and matching algorithms, especially in handling large-scale instances. The developed model and algorithms assist ride-hailing platforms in optimizing operations, enhancing service quality, increasing profitability, and improving cost management.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114543"},"PeriodicalIF":7.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222221","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

DriveFL: A dynamic reputation incentive mechanism for federated learning in dense internet of vehicles DriveFL：密集车联网联合学习的动态声誉激励机制

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-25 DOI: 10.1016/j.knosys.2025.114539

Xin Chang , Lixin Liu , Jingyu Wang , Jinling Yu , Xiaolin Zhang

{"title":"DriveFL: A dynamic reputation incentive mechanism for federated learning in dense internet of vehicles","authors":"Xin Chang , Lixin Liu , Jingyu Wang , Jinling Yu , Xiaolin Zhang","doi":"10.1016/j.knosys.2025.114539","DOIUrl":"10.1016/j.knosys.2025.114539","url":null,"abstract":"<div><div>Federated Learning (FL) enables devices to use data locally for model training and thus has received significant attention for protecting data privacy in the Internet of Vehicles (IoV). However, rational vehicles are reluctant to contribute their data to participate in training without compensation, necessitating the implementation of effective incentive algorithms to motivate vehicles to participate in training. Nevertheless, unlike incentives in other domains, the IoV has the following challenges for the design of incentive systems. First, the large number of vehicles in a dense IoT imposes a huge communication burden and pressure on computational efficiency. Second, road data used by vehicles for training may be affected by factors such as damaged sensors or harsh environments, resulting in changes in data quality. Third, intermittent participation issues are caused by the vehicle’s mobility. To address these issues, we propose DriveFL: A Dynamic Reputation Incentive Mechanism for Federated Learning in Dense Internet of Vehicles. Specifically, we employ gradient compression techniques to reduce communication costs. Subsequently, we design a similarity-based gradient compression quality assessment method capable of evaluating the quality of vehicle data in real time. Then, we develop a dynamic reputation incentive mechanism that quantifies quality assessment records and integrates reverse auction theory, which can attract vehicles with higher data quality from those that intermittently participate in training, thereby enhancing model training quality under constrained communication costs and budget limitations. Theoretical analysis demonstrates that our mechanism satisfies computational efficiency, individual rationality, budget feasibility, and truthfulness. Simulation experiments confirm the effectiveness of our approach.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114539"},"PeriodicalIF":7.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268707","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

Uncovering novel scientific insights with a synergistic GNN-LLM framework 揭示新的科学见解与协同GNN-LLM框架

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-25 DOI: 10.1016/j.knosys.2025.114527

Qingqing Wang , Derui Lyu , Qiuju Chen

{"title":"Uncovering novel scientific insights with a synergistic GNN-LLM framework","authors":"Qingqing Wang , Derui Lyu , Qiuju Chen","doi":"10.1016/j.knosys.2025.114527","DOIUrl":"10.1016/j.knosys.2025.114527","url":null,"abstract":"<div><div>The exponential growth of scientific literature demands intelligent systems capable of uncovering emerging knowledge associations and fostering creativity. While graph neural networks (GNNs) excel at modeling literature structures, their static temporal modeling and lack of semantic awareness limit the discovery of interpretable signals. Conversely, large language models (LLMs) offer deep semantic reasoning but struggle to find non-obvious, structurally-grounded patterns without structured input. To address these limitations, this paper proposes a multi-stage GNN-LLM framework that integrates structural pattern recognition and semantic interpretation for scientific knowledge discovery. The framework begins with a Semantic-Enhanced Temporal Graph Network (SE-TGN), which embeds paper-level semantic information into an event-based temporal GNN to identify emerging keyword associations. These structurally grounded candidates are refined through the Contextual Re-ranking and Evaluation Framework (CREF), which leverages LLM capabilities to assess contextual novelty and relevance. Finally, the Generative Interpretation and Contextualization (GIC) produces human-readable explanations and research prompts to support innovation. Experiments in two scientific domains demonstrate the effectiveness of the framework in discovering semantically rich, contextually grounded, and forward-looking knowledge associations, illustrating its potential to support interpretable and creativity-driven scientific exploration.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114527"},"PeriodicalIF":7.6,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222226","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

Advanced smart farming system based multi-anchor space-aware temporal convolutional neural networks in internet-of-things 基于物联网多锚点空间感知时序卷积神经网络的先进智能农业系统

IF 7.6 1区计算机科学

Knowledge-Based Systems Pub Date : 2025-09-24 DOI: 10.1016/j.knosys.2025.114544

M Shanmathi , Kumar S Praveen

{"title":"Advanced smart farming system based multi-anchor space-aware temporal convolutional neural networks in internet-of-things","authors":"M Shanmathi , Kumar S Praveen","doi":"10.1016/j.knosys.2025.114544","DOIUrl":"10.1016/j.knosys.2025.114544","url":null,"abstract":"<div><div>Agriculture is an important to the economic growth of a country. Farmers possess until recently employed standard farming methods. Accurate farming helps boost output by accurately identifying the actions that must be taken at the right time. Precision farming includes forecasting the weather, evaluating the soil, suggesting crops to grow, and figuring out the fertilizer the crops require. In this paper, an Advanced Smart Farming System based Multi-anchor Space-aware Temporal Convolutional Neural Networks in Internet-of-Things (ASFS-MSTCNN-IoT) is proposed. Initially, the input data is taken from Indian Agriculture Dataset. Then, the input data is pre-processed utilizingCompact Maximal Correntropy-derived Error State Kalman Filter (CMCESKF)which is used to remove the outliers from the input data. The pre-processed data are given into Deep Kernel Principal Component Analysis (DKPCA)which reduces the high dimensionality of the data. Generally, MSTCNN does not show any adaption of optimization methods for finding the optimal parameters to ensure exactforecastof crop yield. Black-Winged Kite Algorithm (BWKA) is proposed in this work to optimize the weight parameter of MSTCNN classifier, which predicts the crop yield precisely. The ASFS-MSTCNN-IoT approach is implemented and analyzed with the help of performance metrics like Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), R<sup>2</sup> and Root Mean Square Error (RMSE) is evaluated. Performance of the ASFS-MSTCNN-IoT approach attains17.85%, 25.82%, 32.64% lower Mean Absolute Error, 25.43%, 19.94%, 31.68% lower Mean Absolute Percentage Error and 18.59%, 25.64% and 31.89% higher R<sup>2</sup> with existing methods respectively.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":"330 ","pages":"Article 114544"},"PeriodicalIF":7.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325450","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