基于voting集成的智能电能表故障多分类方法

为提升智能电能表故障准确分类能力,助力维护人员迅速排除故障,提出基于投票法voting集成的智能电能表故障多分类方法。针对实际智能电能表故障数据进行编码预处理,基于皮尔逊系数法筛选智能电能表故障分类关键影响因素,结合合成少数类过采样技术(synthetic minority oversampling technique, SMOTE)算法解决数据类别不平衡问题,由此建立模型所需数据集,再通过投票法进行模型融合,结合粒子群PSO(particle swarm optimization)确定各基模型的权重,据此构建基于极限梯度提升树(extreme gradient boosting trees, XGBT)、K近邻(k-nearest neighbor, KNN)和朴素贝叶斯(naive bayes, NB)模型的智能电能表故障多分类方法。实测实验结果表明:所提出方法能有效实现智能电能表的故障快速准确分类,与现有方法相比,在智能电能表的故障分类精确率、召回率及F1-Score均有明显提升。

基于SVM-DT-MLP模型的Web日志异常流量检测研究

随着Web应用程序的普及,网络攻击和安全漏洞的风险日益增加。Web日志文件详细记录了网站运行信息,对日志中的流量进行分类从而检测出异常攻击流量是保障网页长期提供稳定、安全服务行之有效的方法之一。文中将Voting特征选择与Stacking集成相结合,构建了SVM-DT-MLP模型,并将其用于Web日志异常流量检测。测试结果表明,SVM-DT-MLP模型的性能显著优于单一算法模型,其Precision(精确度)达到92.44%,Recall(召回率)达到92.43%,F1-Score(F1值)达到92.44%。这意味着该模型能够有效地检测出异常攻击流量,并在保障网页提供稳定和安全服务方面具有很好的效果。

基于集成算法的在线购物平台消费者评价情感分析与研究

文章对在线购物平台的消费者评价数据进行了情感分析和分类。通过使用Python实现自动化浏览器驱动和反爬虫技术,成功采集了某东购物平台的消费者评价信息。文章提出了一种改进的集成算法,将LSTM、BiGRU、BiLSTM作为分类器,分别采用Voting和Bagging方法进行集成。结果表明,与传统的贝叶斯和逻辑回归相比,LSTM+Bagging集成算法在准确率方面分别提高了5.9%和6%,而与LSTM+Voting集成算法相比,准确率提高了0.5个百分点。另外,LSTM+Bagging模型在稳定性和鲁棒性方面表现优于LSTM+Voting算法。

Data-enhanced revealing of trends in Geoscience

Purpose:This article presents an in-depth analysis of global research trends in Geosciences from 2014 to 2023.By integrating bibliometric analysis with expert insights from the Deep-time Digital Earth(DDE)initiative,this article identifies key emerging themes shaping the landscape of Earth Sciences①.Design/methodology/approach:The identification process involved a meticulous analysis of over 400,000 papers from 466 Geosciences journals and approximately 5,800 papers from 93 interdisciplinary journals sourced from the Web of Science and Dimensions database.To map relationships between articles,citation networks were constructed,and spectral clustering algorithms were then employed to identify groups of related research,resulting in 407 clusters.Relevant research terms were extracted using the Log-Likelihood Ratio(LLR)algorithm,followed by statistical analyses on the volume of papers,average publication year,and average citation count within each cluster.Additionally,expert knowledge from DDE Scientific Committee was utilized to select top 30 trends based on their representation,relevance,and impact within Geosciences,and finalize naming of these top trends with consideration of the content and implications of the associated research.This comprehensive approach in systematically delineating and characterizing the trends in a way which is understandable to geoscientists.Findings:Thirty significant trends were identified in the field of Geosciences,spanning five domains:deep space,deep time,deep Earth,habitable Earth,and big data.These topics reflect the latest trends and advancements in Geosciences and have the potential to address real-world problems that are closely related to society,science,and technology.Research limitations:The analyzed data of this study only contain those were included in the Web of Science.Practical implications:This study will strongly support the organizations and individual scientists to understand the modern frontier of earth science,especially on solid earth.The organizations such as the surveys or natural science fund could map out areas for future exploration and analyze the hot topics reference to this study.Originality/value:This paper integrates bibliometric analysis with expert insights to highlight the most significant trends on earth science and reach the individual scientist and public by global voting.

Density Clustering Algorithm Based on KD-Tree and Voting Rules

Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.

Improving Badminton Action Recognition Using Spatio-Temporal Analysis and a Weighted Ensemble Learning Model

Incredible progress has been made in human action recognition(HAR),significantly impacting computer vision applications in sports analytics.However,identifying dynamic and complex movements in sports like badminton remains challenging due to the need for precise recognition accuracy and better management of complex motion patterns.Deep learning techniques like convolutional neural networks(CNNs),long short-term memory(LSTM),and graph convolutional networks(GCNs)improve recognition in large datasets,while the traditional machine learning methods like SVM(support vector machines),RF(random forest),and LR(logistic regression),combined with handcrafted features and ensemble approaches,perform well but struggle with the complexity of fast-paced sports like badminton.We proposed an ensemble learning model combining support vector machines(SVM),logistic regression(LR),random forest(RF),and adaptive boosting(AdaBoost)for badminton action recognition.The data in this study consist of video recordings of badminton stroke techniques,which have been extracted into spatiotemporal data.The three-dimensional distance between each skeleton point and the right hip represents the spatial features.The temporal features are the results of Fast Dynamic Time Warping(FDTW)calculations applied to 15 frames of each video sequence.The weighted ensemble model employs soft voting classifiers from SVM,LR,RF,and AdaBoost to enhance the accuracy of badminton action recognition.The E2 ensemble model,which combines SVM,LR,and AdaBoost,achieves the highest accuracy of 95.38%.

MUS Model:A Deep Learning-Based Architecture for IoT Intrusion Detection

In the face of the effective popularity of the Internet of Things(IoT),but the frequent occurrence of cybersecurity incidents,various cybersecurity protection means have been proposed and applied.Among them,Intrusion Detection System(IDS)has been proven to be stable and efficient.However,traditional intrusion detection methods have shortcomings such as lowdetection accuracy and inability to effectively identifymalicious attacks.To address the above problems,this paper fully considers the superiority of deep learning models in processing highdimensional data,and reasonable data type conversion methods can extract deep features and detect classification using advanced computer vision techniques to improve classification accuracy.TheMarkov TransformField(MTF)method is used to convert 1Dnetwork traffic data into 2D images,and then the converted 2D images are filtered by UnsharpMasking to enhance the image details by sharpening;to further improve the accuracy of data classification and detection,unlike using the existing high-performance baseline image classification models,a soft-voting integrated model,which integrates three deep learning models,MobileNet,VGGNet and ResNet,to finally obtain an effective IoT intrusion detection architecture:the MUS model.Four types of experiments are conducted on the publicly available intrusion detection dataset CICIDS2018 and the IoT network traffic dataset N_BaIoT,and the results demonstrate that the accuracy of attack traffic detection is greatly improved,which is not only applicable to the IoT intrusion detection environment,but also to different types of attacks and different network environments,which confirms the effectiveness of the work done.

A New Speed Limit Recognition Methodology Based on Ensemble Learning:Hardware Validation

Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.

Ensemble Voting-Based Anomaly Detection for a Smart Grid Communication Infrastructure

Advanced Metering Infrastructure(AMI)is the metering network of the smart grid that enables bidirectional communications between each consumer’s premises and the provider’s control center.The massive amount of data collected supports the real-time decision-making required for diverse applications.The communication infrastructure relies on different network types,including the Internet.This makes the infrastructure vulnerable to various attacks,which could compromise security or have devastating effects.However,traditional machine learning solutions cannot adapt to the increasing complexity and diversity of attacks.The objective of this paper is to develop an Anomaly Detection System(ADS)based on deep learning using the CIC-IDS2017 dataset.However,this dataset is highly imbalanced;thus,a two-step sampling technique:random under-sampling and the Synthetic Minority Oversampling Technique(SMOTE),is proposed to balance the dataset.The proposed system utilizes a multiple hidden layer Auto-encoder(AE)for feature extraction and dimensional reduction.In addition,an ensemble voting based on both Random Forest(RF)and Convolu-tional Neural Network(CNN)is developed to classify the multiclass attack cate-gories.The proposed system is evaluated and compared with six different state-of-the-art machine learning and deep learning algorithms:Random Forest(RF),Light Gradient Boosting Machine(LightGBM),eXtreme Gradient Boosting(XGboost),Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and bidirectional LSTM(biLSTM).Experimental results show that the proposed model enhances the detection for each attack class compared with the other machine learning and deep learning models with overall accuracy(98.29%),precision(99%),recall(98%),F_(1) score(98%),and the UNDetection rate(UND)(8%).

Novel traveling quantum anonymous voting scheme via GHZ states

Based on traveling ballot mode,we propose a secure quantum anonymous voting via Greenberger–Horne–Zeilinger(GHZ)states.In this scheme,each legal voter performs unitary operation on corresponding position of particle sequence to encode his/her voting content.The voters have multiple ballot items to choose rather than just binary options“yes”or“no”.After counting votes phase,any participant who is interested in voting results can obtain the voting results.To improve the efficiency of the traveling quantum anonymous voting scheme,an optimization method based on grouping strategy is also presented.Compared with the most existing traveling quantum voting schemes,the proposed scheme is more practical because of its privacy,verifiability and non-repeatability.Furthermore,the security analysis shows that the proposed traveling quantum anonymous voting scheme can prevent various attacks and ensure high security.

Maximizing Influence in Temporal Social Networks:A Node Feature-Aware Voting Algorithm

Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes.However,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social networks.To bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation model.Firstly,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes selection.Specifically,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their features.During the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability accordingly.The process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based algorithms.Secondly,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node features.Finally,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.

Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection

Managing physical objects in the network’s periphery is made possible by the Internet of Things(IoT),revolutionizing human life.Open attacks and unauthorized access are possible with these IoT devices,which exchange data to enable remote access.These attacks are often detected using intrusion detection methodologies,although these systems’effectiveness and accuracy are subpar.This paper proposes a new voting classifier composed of an ensemble of machine learning models trained and optimized using metaheuristic optimization.The employed metaheuristic optimizer is a new version of the whale optimization algorithm(WOA),which is guided by the dipper throated optimizer(DTO)to improve the exploration process of the traditionalWOA optimizer.The proposed voting classifier categorizes the network intrusions robustly and efficiently.To assess the proposed approach,a dataset created from IoT devices is employed to record the efficiency of the proposed algorithm for binary attack categorization.The dataset records are balanced using the locality-sensitive hashing(LSH)and Synthetic Minority Oversampling Technique(SMOTE).The evaluation of the achieved results is performed in terms of statistical analysis and visual plots to prove the proposed approach’s effectiveness,stability,and significance.The achieved results confirmed the superiority of the proposed algorithm for the task of network intrusion detection.

Deep Learning-Based Robust Morphed Face Authentication Framework for Online Systems

The amalgamation of artificial intelligence(AI)with various areas has been in the picture for the past few years.AI has enhanced the functioning of several services,such as accomplishing better budgets,automating multiple tasks,and data-driven decision-making.Conducting hassle-free polling has been one of them.However,at the onset of the coronavirus in 2020,almost all worldly affairs occurred online,and many sectors switched to digital mode.This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business.This paper proposes a three-layered deep learning(DL)-based authentication framework to develop a secure online polling system.It provides a novel way to overcome security breaches during the face identity(ID)recognition and verification process for online polling systems.This verification is done by training a pixel-2-pixel Pix2pix generative adversarial network(GAN)for face image reconstruction to remove facial objects present(if any).Furthermore,image-to-image matching is done by implementing the Siamese network and comparing the result of various metrics executed on feature embeddings to obtain the outcome,thus checking the electorate credentials.

An Intelligent Hazardous Waste Detection and Classification Model Using Ensemble Learning Techniques

Proper waste management models using recent technologies like computer vision,machine learning(ML),and deep learning(DL)are needed to effectively handle the massive quantity of increasing waste.Therefore,waste classification becomes a crucial topic which helps to categorize waste into hazardous or non-hazardous ones and thereby assist in the decision making of the waste management process.This study concentrates on the design of hazardous waste detection and classification using ensemble learning(HWDC-EL)technique to reduce toxicity and improve human health.The goal of the HWDC-EL technique is to detect the multiple classes of wastes,particularly hazardous and non-hazardous wastes.The HWDC-EL technique involves the ensemble of three feature extractors using Model Averaging technique namely discrete local binary patterns(DLBP),EfficientNet,and DenseNet121.In addition,the flower pollination algorithm(FPA)based hyperparameter optimizers are used to optimally adjust the parameters involved in the EfficientNet and DenseNet121 models.Moreover,a weighted voting-based ensemble classifier is derived using three machine learning algorithms namely support vector machine(SVM),extreme learning machine(ELM),and gradient boosting tree(GBT).The performance of the HWDC-EL technique is tested using a benchmark Garbage dataset and it obtains a maximum accuracy of 98.85%.

Classifying Hematoxylin and Eosin Images Using a Super-Resolution Segmentor and a Deep Ensemble Classifier

Developing an automatic and credible diagnostic system to analyze the type,stage,and level of the liver cancer from Hematoxylin and Eosin(H&E)images is a very challenging and time-consuming endeavor,even for experienced pathologists,due to the non-uniform illumination and artifacts.Albeit several Machine Learning(ML)and Deep Learning(DL)approaches are employed to increase the performance of automatic liver cancer diagnostic systems,the classi-fication accuracy of these systems still needs significant improvement to satisfy the real-time requirement of the diagnostic situations.In this work,we present a new Ensemble Classifier(hereafter called ECNet)to classify the H&E stained liver histopathology images effectively.The proposed model employs a Dropout Extreme Learning Machine(DrpXLM)and the Enhanced Convolutional Block Attention Modules(ECBAM)based residual network.ECNet applies Voting Mechanism(VM)to integrate the decisions of individual classifiers using the average of probabilities rule.Initially,the nuclei regions in the H&E stain are seg-mented through Super-resolution Convolutional Networks(SrCN),and then these regions are fed into the ensemble DL network for classification.The effectiveness of the proposed model is carefully studied on real-world datasets.The results of our meticulous experiments on the Kasturba Medical College(KMC)liver dataset reveal that the proposed ECNet significantly outperforms other existing classifica-tion networks with better accuracy,sensitivity,specificity,precision,and Jaccard Similarity Score(JSS)of 96.5%,99.4%,89.7%,95.7%,and 95.2%,respectively.We obtain similar results from ECNet when applied to The Cancer Genome Atlas Liver Hepatocellular Carcinoma(TCGA-LIHC)dataset regarding accuracy(96.3%),sensitivity(97.5%),specificity(93.2%),precision(97.5%),and JSS(95.1%).More importantly,the proposed ECNet system consumes only 12.22 s for training and 1.24 s for testing.Also,we carry out the Wilcoxon statistical test to determine whether the ECNet provides a considerable improvement with respect to evaluation metrics or not.From extensive empirical analysis,we can conclude that our ECNet is the better liver cancer diagnostic model related to state-of-the-art classifiers.

Ensemble Learning for Fetal Health Classification

Cardiotocography(CTG)represents the fetus’s health inside the womb during labor.However,assessment of its readings can be a highly subjective process depending on the expertise of the obstetrician.Digital signals from fetal monitors acquire parameters(i.e.,fetal heart rate,contractions,acceleration).Objective:This paper aims to classify the CTG readings containing imbalanced healthy,suspected,and pathological fetus readings.Method:We perform two sets of experiments.Firstly,we employ five classifiers:Random Forest(RF),Adaptive Boosting(AdaBoost),Categorical Boosting(CatBoost),Extreme Gradient Boosting(XGBoost),and Light Gradient Boosting Machine(LGBM)without over-sampling to classify CTG readings into three categories:healthy,suspected,and pathological.Secondly,we employ an ensemble of the above-described classifiers with the oversamplingmethod.We use a random over-sampling technique to balance CTG records to train the ensemble models.We use 3602 CTG readings to train the ensemble classifiers and 1201 records to evaluate them.The outcomes of these classifiers are then fed into the soft voting classifier to obtain the most accurate results.Results:Each classifier evaluates accuracy,Precision,Recall,F1-scores,and Area Under the Receiver Operating Curve(AUROC)values.Results reveal that the XGBoost,LGBM,and CatBoost classifiers yielded 99%accuracy.Conclusion:Using ensemble classifiers over a balanced CTG dataset improves the detection accuracy compared to the previous studies and our first experiment.A soft voting classifier then eliminates the weakness of one individual classifier to yield superior performance of the overall model.

基于异质集成学习方法的城轨列车客流智能分析系统研究

为解决当前城市轨道交通(简称:城轨)列车客流分析存在的检测精度不高和适用场景单一等问题,设计了一种基于异质集成学习方法的城轨列车智能客流分析系统。该系统基于云边协同架构,采用分组Voting方法,将YOLOv5s(You Only Look Once v5s)、FCHD(Fully Convolutional Head Detector)、CSRNet(Network for Congested Scene Recognition)模型作为基模型进行集成,最终实现客流统计、拥挤度分析和辅助清客等功能。利用北京城轨某线路列车的监控图像数据进行实验,结果表明,与其他各基模型相比,该系统采用的模型检测效果更佳,有效提升了检测精度,丰富了可适用的检测场景。

基于Voting和Stacking集成算法的岩爆倾向性预测

岩爆是矿山深部开采中常见的地质灾害,准确预测岩爆可降低矿山生产安全风险。将机器学习应用于岩爆预测是切实可行的,但仅用一种方法或将几种方法简单结合对于提高岩爆预测的准确性或泛化性作用十分有限。基于此,将最大切向应力、应力集中系数、脆性系数、弹性能量指数等作为指标,利用231组有效岩爆数据,基于Voting和Stacking集成算法,融合精确率较高的6种基础分类器(LR、RF、SVM、DT、KNN、GNB),建立了4个集成分类器V 1、V 2、S 1和S 2。根据预测结果的混淆矩阵计算了精确率、准确率、召回率及F 1分数,对各分类器性能进行了评估,结果表明:基础分类器中SVM、RF对Ⅰ级、Ⅱ级样本较敏感,KNN对Ⅲ级、Ⅳ级样本更敏感;RF、SVM整体预测效果最好,精确率分别为0.93、0.94;集成分类器相对于基础分类器性能均有不同程度的提升,但受性能较差的基础分类器及投票机制影响,Voting集成分类器整体性能弱于Stacking集成分类器;4个集成分类器中S 1性能提升最显著,预测效果最佳,精确率、准确率、召回率、F 1分数分别为0.95、0.97、0.96、0.95;将基于Stacking算法构建的集成分类器S 1应用于秦岭隧道的岩爆预测,预测结果与工程现场实际一致,验证了其可靠性。