检索结果-南通市图书馆

Research on Interpolation Method for Missing Electricity Consumption Data

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第2期78卷 2575-2591页

作者： Junde Chen Jiajia Yuan Weirong Chen Adnan Zeb Md Suzauddola Yaser A.Nanehkaran Department of Electronic Commerce Xiangtan UniversityXiangtan411105China school of Information Engineering Yancheng Teachers UniversityYancheng224000China Department of Information and Electrical Engineering Ningde Normal UniversityNingde352100China College of Engineering Southern University of Science and TechnologyShenzhen518005China school of informatics Xiamen UniversityXiamen361005China

Missing value is one of the main factors that cause dirty *** high-quality data,there will be no reliable analysis results and precise ***,the data warehouse needs to integrate high-quality data *** the power system,the electricity consumption data of some large users cannot be normally collected resulting in missing data,which affects the calculation of power supply and eventually leads to a large error in the daily power line loss *** the problem of missing electricity consumption data,this study proposes a group method of data handling(GMDH)based data interpolation method in distribution power networks and applies it in the analysis of actually collected electricity ***,the dependent and independent variables are defined from the original data,and the upper and lower limits of missing values are determined according to prior knowledge or existing data *** missing data are randomly interpolated within the upper and lower ***,the GMDH network is established to obtain the optimal complexity model,which is used to predict the missing data to replace the last imputed electricity consumption *** last,this process is implemented iteratively until the missing values do not *** a relatively small noise level(α=0.25),the proposed approach achieves a maximum error of no more than 0.605%.Experimental findings demonstrate the efficacy and feasibility of the proposed approach,which realizes the transformation from incomplete data to complete ***,this proposed data interpolation approach provides a strong basis for the electricity theft diagnosis and metering fault analysis of electricity enterprises.

关键词： Data interpolation GMDH electricity consumption data distribution system

Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第2期78卷 2441-2468页

作者： Vaishnawi Priyadarshni Sanjay Kumar Sharma Mohammad Khalid Imam Rahmani Baijnath Kaushik Rania Almajalid Department of Computer Science Gautam Buddha UniversityGreater NoidaUttar PradeshIndia College of Computing and informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia school of Computer Science Shri Vaishno Devi UniversityKatraIndia

Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in *** detection and effective treatment of BC can help save women’s *** an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection *** paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography(CBIS-DDSM)data *** novelty of the proposed framework lies in the integration of various techniques,where the fusion of deep learning(DL),traditional machine learning(ML)techniques,and enhanced classification models have been deployed using the curated *** analysis outcome proves that the proposed enhanced RF(ERF),enhanced DT(EDT)and enhanced LR(ELR)models for BC detection outperformed most of the existing models with impressive results.

关键词： Autoencoder breast cancer deep neural network convolutional neural network image processing machine learning deep learning

Sliced Average Variance Estimation for Tensor Data

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Acta Mathematicae Applicatae Sinica 2024年第3期40卷 630-655页

作者： Chuan-quan LI Pei-wen XIAO Chao YING Xiao-hui LIU school of Statistics Jiangxi University of Finance and EconomicsNanchang 330013China Key Laboratory of Data Science in Finance and Economics Jiangxi University of Finance and EconomicsNanchang 330013China Biostatistics and Medical informatics University of Wisconsin MadisonMadison 53726USA

Tensor data have been widely used in many fields,e.g.,modern biomedical imaging,chemometrics,and economics,but often suffer from some common issues as in high dimensional *** to find their low-dimensional latent structure has been of great interest for *** this end,we develop two efficient tensor sufficient dimension reduction methods based on the sliced average variance estimation(SAVE)to estimate the corresponding dimension reduction *** first one,entitled tensor sliced average variance estimation(TSAVE),works well when the response is discrete or takes finite values,but is not■consistent for continuous response;the second one,named bias-correction tensor sliced average variance estimation(CTSAVE),is a de-biased version of the TSAVE *** asymptotic properties of both methods are derived under mild *** and real data examples are also provided to show the superiority of the efficiency of the developed methods.

关键词： tensor data sliced average variance estimation sufficient dimension reduction central subspace

维普期刊数据库

Joint Active and Passive Beamforming Design in Intelligent Reflecting Surface(IRS)-Assisted Covert Communications:A Multi-Agent DRL Approach

在线全文

学校读者我要写书评

暂无评论

China Communications 2024年第9期21卷 11-26页

作者： Gao Ang Ren Xiaoyu Deng Bin Sun Xinshun Zhang Jiankang school of Electronic Information Northwestern Polytechnical UniversityXi’an 710072China Key Laboratory of Near Ground Detection and Perception Technology Wuxi 214035China Department of Computing and informatics Bournemouth UniversityUK

Intelligent Reflecting Surface(IRS),with the potential capability to reconstruct the electromagnetic propagation environment,evolves a new IRSassisted covert communications paradigm to eliminate the negligible detection of malicious eavesdroppers by coherently beaming the scattered signals and suppressing the signals ***,when multiple IRSs are involved,accurate channel estimation is still a challenge due to the extra hardware complexity and communication *** the crossinterference caused by massive reflecting paths,it is hard to obtain the close-formed solution for the optimization of covert *** this basis,the paper improves a heterogeneous multi-agent deep deterministic policy gradient(MADDPG)approach for the joint active and passive beamforming(Joint A&P BF)optimization without the channel estimation,where the base station(BS)and multiple IRSs are taken as different types of agents and learn to enhance the covert spectrum efficiency(CSE)*** to the‘centralized training and distributed execution’feature of MADDPG,each agent can execute the active or passive beamforming independently based on its partial observation without referring to *** results demonstrate that the proposed deep reinforcement learning(DRL)approach could not only obtain a preferable CSE of legitimate users and a low detection of probability(LPD)of warden,but also alleviate the communication overhead and simplify the IRSs deployment.

关键词： covert communications deep reinforcement learning intelligent reflecting surface

维普期刊数据库

Personality Trait Detection via Transfer Learning

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第2期78卷 1933-1956页

作者： Bashar Alshouha Jesus Serrano-Guerrero Francisco Chiclana Francisco P.Romero Jose A.Olivas Department of Information Technologies and Systems University of Castilla-La ManchaCiudad Real13071Spain Institute of Artificial Intelligence School of Computer Science and InformaticsDe Montfort UniversityThe GatewayLeicesterLE19BHUK

Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human ***-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their *** study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality *** models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term *** makes them suitable to classify multi-label personality traits from reviews while mitigating computational *** focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from ***,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook *** status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality *** test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted *** results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of *** findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality *** findings represent substantial a

关键词： Personality trait detection pre-trained language model big five model transfer learning

黏贴FRP剪力键的FRP-UHPC组合梁受剪性能研究

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

中国公路学报 2024年

作者：葛文杰张志文 Ashraf Ashour 曹大富李胜才扬州大学建筑科学与工程学院 university of bradford Faculty of Engineering & informatics

为了研究纤维增强复合材料（Fiber reinforced polymer， FRP）-超高性能混凝土（Ultra high performance concrete， UHPC）组合梁的受剪性能，对FRP-UHPC组合梁进行四点受弯试验。组合界面采用FRP剪力键（FRP shear key， FSK）与环... 详细信息

为了研究纤维增强复合材料（Fiber reinforced polymer， FRP）-超高性能混凝土（Ultra high performance concrete， UHPC）组合梁的受剪性能，对FRP-UHPC组合梁进行四点受弯试验。组合界面采用FRP剪力键（FRP shear key， FSK）与环氧树脂胶结混合处理的方式降低界面滑移的影响。对组合梁的破坏模式、荷载-位移响应、应变响应和滑移响应进行分析，研究了混凝土类型、FSK间距、混凝土板宽度和高度对组合梁受剪性能的影响。结果表明，FRP-UHPC组合梁的破坏模式是剪切破坏，FRP-NSC组合梁的破坏模式是弯、剪协同破坏。组合梁存在剪力滞后效应，随着混凝土板宽度的增大，剪力滞后效应越明显。混凝土采用UHPC、减小FSK间距和提高混凝土板截面尺寸都可以有效提高组合梁的抗剪性能，并减小界面滑移。基于合理假定，提出了FRP-UHPC组合梁的抗剪承载力和考虑剪切变形的挠度计算方法，预测结果与试验结果吻合较好。

关键词：桥梁工程 FRP-UHPC组合梁弯曲试验受剪性能 FRP剪力键

Cyberbullying Sexism Harassment Identification by Metaheurustics-Tuned eXtreme Gradient Boosting

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第9期80卷 4997-5027页

作者： Milos Dobrojevic Luka Jovanovic Lepa Babic Miroslav Cajic Tamara Zivkovic Miodrag Zivkovic Suresh Muthusamy Milos Antonijevic Nebojsa Bacanin Technical Faculty Singidunum UniversityBelgrade11000Serbia informatics and Computing Singidunum UniversityBelgrade11000Serbia Business Economics Singidunum UniversityBelgrade11000Serbia Computing and informatics Sinergija UniversityBijeljina76300Bosnia and Herzegovina Department for Information Systems and Technologies University“Union Nikola Tesla”Cara DusanaBelgrade11080Serbia Department for Computer Science and informatics School of Electrical EngineeringUniversity of BelgradeBelgrade11000Serbia Department of Electrical and Electronics Engineering Kongu Engineering College(Autonomous)PerunduraiErode638060India Department of Mathematics Saveetha School of Engineering(Deemed to be University)SIMATS ThandalamChennai602105India MEU Research Unit Middle East UniversityAmman11831Jordan

Cyberbullying is a form of harassment or bullying that takes place online or through digital devices like smartphones,computers,or *** can occur through various channels,such as social media,text messages,online forums,or gaming *** involves using technology to intentionally harm,harass,or intimidate others and may take different forms,including exclusion,doxing,impersonation,harassment,and ***,due to the rapid growth of malicious internet users,this social phenomenon is becoming more frequent,and there is a huge need to address this ***,the main goal of the research proposed in this manuscript is to tackle this emerging challenge.A dataset of sexist harassment on Twitter,containing tweets about the harassment of people on a sexual basis,for natural language processing(NLP),is used for this *** algorithms are used to transform the text into a meaningful representation of numbers for machine learning(ML)input:Term frequency inverse document frequency(TF-IDF)and Bidirectional encoder representations from transformers(BERT).The well-known eXtreme gradient boosting(XGBoost)ML model is employed to classify whether certain tweets fall into the category of sexual-based harassment or ***,with the goal of reaching better performance,several XGBoost models were devised conducting hyperparameter tuning by *** this purpose,the recently emerging Coyote optimization algorithm(COA)was modified and adjusted to optimize the XGBoost ***,other cutting-edge metaheuristics approach for this challenge were also implemented,and rigid comparative analysis of the captured classification metrics(accuracy,Cohen kappa score,precision,recall,and F1-score)was ***,the best-generated model was interpreted by Shapley additive explanations(SHAP),and useful insights were gained about the behavioral patterns of people who perform social harassment.

关键词： Coyote optimization algorithm NLP TF-IDF BERT XGBoost online harassment and cyberbullying metaheuristics

A novel method for clustering cellular data to improve classification

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Neural Regeneration Research 2025年第9期20卷 2697-2705页

作者： Diek W.Wheeler Giorgio A.Ascoli Center for Neural informatics Structures&PlasticityKrasnow Institute for Advanced Study Bioengineering Department Volgenau School of Engineering George Mason university FairfaxVAUSA

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are *** recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within *** we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical *** general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical *** demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

关键词： cellular data clustering dendrogram data classification Levene's one-tailed statistical test unsupervised hierarchical clustering

Graph based recurrent network for context specific synthetic lethality prediction

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China Life Sciences 2024年

作者： Yuyang Jiang Jing Wang Yixin Zhang ZhiWei Cao Qinglong Zhang Jinsong Su Song He Xiaochen Bo Academy of Medical Engineering and Translational Medicine Tianjin University Department of Bioinformatics Institute of Health Service and Transfusion MedicineBeijing school of Medicine Tsinghua University school of informatics Xiamen University

The concept of synthetic lethality (SL) has been successfully used for targeted therapies. To further explore SL for cancer therapy, identifying more SL interactions with therapeutic potential are essential. Recently, graph neural network-based deep learning methods have been proposed for SL prediction, which reduce the SL search space of wet-lab based methods. However, these methods ignore that most SL interactions depend strongly on genetic context, which limits the application of the predicted results. In this study, we proposed a graph recurrent network-based model for specific context-dependent SL prediction (SLGRN). In particular, we introduced a Graph Recurrent Network-based encoder to acquire a context-specific, low-dimensional feature representation for each node, facilitating the prediction of novel SL. SLGRN leveraged gate recurrent unit (GRU) and it incorporated a context-dependent-level state to effectively integrate information from all nodes. As a result, SLGRN outperforms the state-of-the-arts models for SL prediction. We subsequently validate novel SL interactions under different contexts based on combination therapy or patient survival analysis. Through in vitro experiments and retrospective clinical analysis, we emphasize the potential clinical significance of this context-specific SL prediction model.

关键词：