Applied Soft Computing最新文献

{"title":"Spatial-temporal analysis and trend prediction of regional crop disease based on electronic medical records","authors":"Chang Xu ,&nbsp;Lei Zhao ,&nbsp;Haojie Wen ,&nbsp;Lingxian Zhang","doi":"10.1016/j.asoc.2024.112423","DOIUrl":"10.1016/j.asoc.2024.112423","url":null,"abstract":"<div><div>Intelligent diagnosis of individual crop diseases has matured. How to understand the evolution patterns and predict regional disease trends remains a significant challenge. Plant Electronic Medical Records (PEMRs) offer valuable spatial-temporal characteristics about crop diseases, presenting a new opportunity for predicting the occurrence of regional diseases. In this study, we used a large prescription database from Beijing (2018–2021) to reframe regional disease prediction as a time series forecasting task. Firstly, to analyze spatial-temporal evolution patterns, we use ArcGIS to extract key information and identify potential connections between different disease occurrence points. Then, we developed a novel deep learning combined model SV-CBA, which combines Seasonal and Trend decomposition (STL) with Variational Mode Decomposition (VMD) to identify trend, seasonal, and residual components, and re-decomposes the residuals. STL-VMD can capture long-term trends and periodic variations while managing nonlinear and volatile characteristics. The CNN-BiLSTM-Attention model calculates disease trends by linearly integrating predictions of each sub-series. To reduce computational complexity while maintaining predictive performance, we propose an improved simplified attention mechanism. Our model demonstrates superior performance in both comparative and ablation experiments using the PEMRs dataset, outperforming numerous other models. This study provides accurate disease trend predictions, aiding farmers and regional managers in agricultural production management.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112423"},"PeriodicalIF":7.2,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656741","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":"Personalized blood pressure estimation using multiview fusion information of wearable physiological signals and transfer learning","authors":"Jian Liu ,&nbsp;Shuaicong Hu ,&nbsp;Yanan Wang ,&nbsp;Wei Xiang ,&nbsp;Qihan Hu ,&nbsp;Cuiwei Yang","doi":"10.1016/j.asoc.2024.112390","DOIUrl":"10.1016/j.asoc.2024.112390","url":null,"abstract":"<div><div>Continuous blood pressure (BP) monitoring is crucial for individual health management, yet the significant inter-individual variations among patients pose challenges to achieving precision medicine. In response to this issue, we propose a parallel cross-hybrid architecture that integrates a convolutional neural network backbone and a Mix-Transformer backbone. This model, grounded in multi-view physiological signals and personalized fine-tuning strategies, aims to estimate BP, facilitating the capture of physiological information across diverse receptive fields and enhancing network expressive capabilities. Our proposed architecture exhibits superior performance in estimating systolic blood pressure and diastolic blood pressure, with average absolute errors of 3.94 mmHg and 2.24 mmHg, respectively. These results surpass existing baseline models and align with the standards set by the British Hypertension Society, the Association for the Advancement of Medical Instrumentation, and the Institute of Electrical and Electronics Engineers for BP measurement. Additionally, this study explores a personalized model fine-tuning strategy by adjusting specific layers and incorporating individual information, presenting an optimal solution. The model's generalization ability is validated through transfer learning across databases (public and self-made). To enhance the proposed architecture's usability in wearable devices, this study employs a knowledge distillation strategy for model lightweighting, with preliminary application in our designed real-time BP estimation system. This study provides an efficient and accurate solution for personalized BP estimation, exhibiting broad potential applications.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112390"},"PeriodicalIF":7.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656732","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":"Many-to-many: Domain adaptation for water quality prediction","authors":"Shunnan Wang ,&nbsp;Min Gao ,&nbsp;Huan Wu ,&nbsp;Fengji Luo ,&nbsp;Feng jiang ,&nbsp;Liang Tao","doi":"10.1016/j.asoc.2024.112381","DOIUrl":"10.1016/j.asoc.2024.112381","url":null,"abstract":"<div><div>Predicting water quality is crucial for sustainable water management. To mitigate data scarcity for specific water quality targets, domain adaptation methods have been employed, adjusting a model to perform in a related domain and leveraging learned knowledge to bridge domain differences. However, these methods often fall short by overfitting certain domain-specific patterns, overlooking consistent water quality patterns in multi-water domains. Despite regional variations, these Consistent patterns show fundamental commonalities and can be observed across monitoring sites, stemming from their widespread and interconnected nature. Addressing these limitations, we introduce the Many-to-Many Domain Adaptation framework (M2M) for prediction to bridge the gap between multi-source domains and multi-target domains, aligning shared insights with the distinct profiles of individual monitoring sites while considering their geographical interconnections. M2M adeptly addresses the formidable challenge of concurrently deciphering and integrating multifaceted patterns across an array of source and target domains, while also navigating the intricate regional heterogeneity intrinsic to the water quality of different sites. The M2M includes a domain pattern fusion module for consistent pattern extraction and numerical scale maintenance from source domains, a domain pattern sharing module for sharing pattern extraction from target domains, and an M2M learning method to ensure the training of these modules. Extensive experiments conducted on 120 diverse monitoring stations demonstrate that M2M markedly enhances the accuracy of water quality predictions using various time series encoders. Code available at <span><span>https://github.com/biya0105/M2M</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112381"},"PeriodicalIF":7.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654071","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":"Privacy preserving verifiable federated learning scheme using blockchain and homomorphic encryption","authors":"Ganesh Kumar Mahato ,&nbsp;Aiswaryya Banerjee ,&nbsp;Swarnendu Kumar Chakraborty ,&nbsp;Xiao-Zhi Gao","doi":"10.1016/j.asoc.2024.112405","DOIUrl":"10.1016/j.asoc.2024.112405","url":null,"abstract":"<div><div>This paper introduces a novel Privacy-Preserving Verifiable Federated Learning (PPVFL) scheme that integrates blockchain technology and homomorphic encryption to address critical challenges in decentralized machine learning. The proposed scheme ensures data privacy, integrity, verifiability, robust security, and efficiency in collaborative learning environments, particularly in sensitive domains such as healthcare. By leveraging blockchain’s decentralized, immutable ledger and homomorphic encryption’s capability to perform computations on encrypted data, the model maintains the confidentiality of sensitive information throughout the learning process. The inclusion of Byzantine fault tolerance and Elliptic Curve Digital Signature Algorithm (ECDSA) further enhances the system’s security against malicious attacks and data tampering, while the optimization of computational processes ensures efficient model training and communication. The novelty of this work lies in the seamless integration of blockchain and homomorphic encryption within a federated learning framework, specifically tailored for post-quantum cryptography, a combination that has not been extensively explored in prior research. This research represents a significant advancement in secure and efficient federated learning, offering a promising solution for industries that prioritize data privacy, security, and trust in collaborative machine learning. The effectiveness, security, and efficiency of the PPVFL scheme were validated using the Glaucoma dataset. The proposed method outperformed baseline federated learning algorithms, achieving a Dice coefficient of 0.918 and a Hausdorff distance of 4.05 on Severe Glaucoma (SG) cases, compared to 0.905 and 5.27, respectively, with traditional FedAvg. Moreover, the integration of blockchain and homomorphic encryption ensured that data privacy was upheld without compromising model performance, while efficient computation and communication processes minimized latency and resource consumption. This study contributes a robust, privacy-preserving, secure, efficient, and verifiable federated learning framework that addresses the pressing need for secure and scalable data management in distributed machine learning environments.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112405"},"PeriodicalIF":7.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578869","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":"Bi-Branching Feature Interaction Representation Learning for Multivariate Time Series","authors":"Wenyan Wang ,&nbsp;Enguang Zuo ,&nbsp;Ruiting Wang ,&nbsp;Jie Zhong ,&nbsp;Chen Chen ,&nbsp;Cheng Chen ,&nbsp;Xiaoyi Lv","doi":"10.1016/j.asoc.2024.112383","DOIUrl":"10.1016/j.asoc.2024.112383","url":null,"abstract":"<div><div>Representational learning of time series plays a crucial role in various fields. However, existing time-series models do not perform well in representation learning. These models usually focus only on the relationship between variables at the same timestamp or only consider the change of individual variables in time, while failing to effectively integrate the two, which limits their ability to capture complex time dependencies and multivariate interactions. We propose a <strong>Bi</strong>-Branching <strong>F</strong>eature <strong>I</strong>nteraction Representation Learning for Multivariate Time Series (Bi-FI) to address these issues. Specifically, we elaborated a frequency domain analysis branch to address the complex associations between variables that are difficult to visualize in the time domain. In addition, to eliminate the time lag effect, another branch employs the mechanism of variable tokenization for attention to learning intra-variable representations. Ultimately, we interactively fuse the features learned from the two branches to obtain a more comprehensive time series representation. Bi-FI performs well in three time series analysis tasks: long sequence prediction, classification, and anomaly detection. Our code and dataset will be available at <span><span>https://github.com/wwy8/Bi_FI</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112383"},"PeriodicalIF":7.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656805","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":"Robust Chinese Clinical Named Entity Recognition with information bottleneck and adversarial training","authors":"Yunfei He ,&nbsp;Zhiqiang Zhang ,&nbsp;Jinlong Shen ,&nbsp;Yuling Li ,&nbsp;Yiwen Zhang ,&nbsp;Weiping Ding ,&nbsp;Fei Yang","doi":"10.1016/j.asoc.2024.112409","DOIUrl":"10.1016/j.asoc.2024.112409","url":null,"abstract":"<div><div>Chinese Clinical Named Entity Recognition (CCNER) aims to extract entities with specific medical significance from Chinese clinical texts, which is an important part of medical data mining. Some existing CCNER models may assume perfect text data and design complex models to improve their accuracy. However, due to the complexity of Chinese clinical entity semantics and the professionalism of annotation, Chinese clinical texts are prone to contain irregular misrepresentations and sparse entity labeling. That would lead to noisy or incomplete text features extracted by CCNER, seriously threatening the robustness of recognition in real-world scenarios. To address these problems, we propose the Robust Chinese Clinical Named Entity Recognition model (RCCNER). RCCNER comprises three essential components: multifaceted text representation, robust feature extraction, and robust model training. For multifaceted text representation, the model enhances consistency and collaboration between feature representations by integrating word embedding, radical embedding, and dictionary embedding to help withstand textual noise. Then, guided by the information bottleneck and the Hilbert–Schmidt independence criterion, robust feature extraction compresses the dependency between text representation and extracted features, while enhancing the dependency between extracted features and labels, which consequently provides reliable text features for robust recognition. The robust model training aspect leverages adversarial training to diminish RCCNER’s sensitivity to noise disturbances and sparse entity labeling, thereby reinforcing its robustness in entity recognition. RCCNER collaboratively enhances the noise immunity through text representation, text feature extraction and model training. Several experiments on two popular public datasets validate the effectiveness and robustness of RCCNER.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112409"},"PeriodicalIF":7.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578863","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":"Clustering based fuzzy classification with a noise cluster in detecting fraud in insurance","authors":"Oguz Koc ,&nbsp;Furkan Baser ,&nbsp;A. Sevtap Selcuk-Kestel","doi":"10.1016/j.asoc.2024.112430","DOIUrl":"10.1016/j.asoc.2024.112430","url":null,"abstract":"<div><div>Fraud detection is one of the main issues in reducing the unsystematic risks in insurance business as its costs might reach to catastrophic amounts leading to higher loadings on reserves and premiums. Due to its cause of nature in diversity, fraud detection may require a wide range of factors and variables to be considered. To make logical relations between many factors and reveal their differences, estimate odds (or probabilities), and predict the fraud risk, scoring systems become an important aid. In this paper, we introduce a clustering-based fuzzy classification with a noise cluster (CBFCN) to identify the true state of a fraud. The approach proposed in this paper is based on fuzzy k-means clustering having a noise cluster (FKMN) and is a novel method for identifying outliers by achieving robust clustering. We integrate fuzzy theory to boost the prediction ability of machine learning (ML) approaches for a proper determination of the contributing features. The two critical features of the CBFCN method which are the membership values obtained from the FKMN clustering algorithm are implemented to capture the behavior of an existing structure better and detect the noise (extremes) in the dataset. Extensive analyses are made on two real datasets exposing different characteristics in their variables to demonstrate how CBFCN performs in detecting the fraud compared to the conventional approaches. Additionally, employing fuzzy approach to improve the ML performance is elaborated through the inclusion of noise clusters. The findings indicate that the suggested CBFCN models produce promising classification results in fraud detection in insurance claims occurrences.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112430"},"PeriodicalIF":7.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578868","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":"Clustering by detecting skeletal structure and identifying density fluctuation","authors":"Wenjie Guo,&nbsp;Wei Chen,&nbsp;Xinggao Liu","doi":"10.1016/j.asoc.2024.112432","DOIUrl":"10.1016/j.asoc.2024.112432","url":null,"abstract":"<div><div>Clustering is one of the most important techniques for unsupervised learning, it tries to divide points into different clusters without any priori knowledge of data. Therefore, several criterions for clustering algorithm are as follows: 1. Handling clusters with arbitrary shape and various density; 2. Finding cluster centers automatically; 3. Low parameter sensitivity and computational complexity. In this context, a novel algorithm namely clustering by detecting skeletal structure and identifying density fluctuation (CSSDF) was presented. In CSSDF, an efficient strategy based on density and local information of neighborhood is firstly proposed to detect the skeletal structure, which can collect the local information and identify the rough distribution of data. With the identified distribution information, a method takes expanded neighborhood and density fluctuation into consideration is proposed to further collect global information of data, which can assign all skeleton points and find cluster centers. To sum up, CSSDF can not only discover the underlying structure of data regardless of its’ distribution, but also ensure the correct assignment of all skeleton points and thus lead to a satisfying clustering performance. In addition, the computational complexity of the proposed approach is <span><math><mrow><mi>O</mi><mo>(</mo><mi>nlogn</mi><mo>)</mo></mrow></math></span>, which makes it possible to deal with some large clustering problem.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112432"},"PeriodicalIF":7.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142654025","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":"Gesture recognition framework for upper-limb prosthetics using entropy features from electromyographic signals and a Gaussian kernel SVM classifier","authors":"Prabhavathy T. ,&nbsp;Vinodh Kumar Elumalai ,&nbsp;Balaji E.","doi":"10.1016/j.asoc.2024.112382","DOIUrl":"10.1016/j.asoc.2024.112382","url":null,"abstract":"<div><div>This paper puts forward a novel entropy features based multi-class SVM classifier framework to predict the limb movement of the transradial amputees from the surface electromyography (sEMG) signals. The major challenges with the sEMG signal are nonlinear and non-stationary characteristics and susceptibility to noise. Consequently, a robust and an effective feature extraction framework which is invariant to force level variations is central in sEMG based prosthesis control. To address the aforementioned challenges, this study leverages the potential of variational mode decomposition (VMD) technique to identify the prominent frequency modes of the sEMG signals, and performs the spectral evaluation of the decomposed sEMG modes to identify the dominant ones to extract the entropy features. Subsequently, we evaluate the efficacy of four nonlinear optimal feature selection techniques and identify the prominent entropy features to train the multi-class SVM model that can predict the gestures. Specifically, to handle the nonlinearly separable input data, this study implements a kernelization named a radial basis function (RBF), which has good generalization and noise tolerance features. The efficacy of the proposed framework is tested using the publicly available datasets that contain gestures from transradial and congenital amputees for functional gestures. Experimental results obtained for various gestures with dynamic force levels underscore that the proposed framework is highly robust against the force level variations and can achieve a classification accuracy of 99.07%.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112382"},"PeriodicalIF":7.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142656736","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":"Physics-informed neutral network with physically consistent and residual learning for excavator precision operation control","authors":"Chenlong Feng ,&nbsp;Jixin Wang ,&nbsp;Yuying Shen ,&nbsp;Qi Wang ,&nbsp;Yi Xiong ,&nbsp;Xudong Zhang ,&nbsp;Jiuchen Fan","doi":"10.1016/j.asoc.2024.112402","DOIUrl":"10.1016/j.asoc.2024.112402","url":null,"abstract":"<div><div>The data-driven methodologies can establish accurate Inverse Dynamics Model (IDM) of the excavator thus improving control precisions. However, the inherent black-box nature of these models often results in overfitting to the dataset, leading to predictions that deviate from the constraints of physical system. Consequently, this can lead to controller failures, introducing unpredictable behavior that threatens operation precision. In addition, the uncertainty of the external disturbance poses great challenge to the precision of controller. This study presents a physics-informed neural network to build accurate IDM with physical consistency. The Rigid Body Dynamics (RBD) of the excavator are coupled within a Deep Lagrangian Network (DeLaN), while a Convolutional Neural Network (CNN) and a Long Short-Term Memory Network (LSTM) are employed to assimilate the residual nonlinear characteristics, such as hydraulic flexibilities and stick–slip friction. To the uncertainty of the external disturbance, the Prescribed Performance Inverse Dynamics Controller combination with the DeLaN-CNN-LSTM model (PPIDC-DCL) is constructed for precise control by constraining the control error within a finite region. The experimental results demonstrate that the model captures the underlying structure of the dynamic and builds the IDM with high accuracy and robustness. Moreover, the PPIDC-DCL controller effectively constrains the control error and realizes precision control. The proposed method has potential applications and provides novel insights for achieving precise operation control of excavators.</div></div>","PeriodicalId":50737,"journal":{"name":"Applied Soft Computing","volume":"167 ","pages":"Article 112402"},"PeriodicalIF":7.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573120","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}