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。

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。

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.

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.

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.

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.

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%).

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。

基于Tensor Voting的蚁蛉翅脉修补

针对蚁蛉模式识别中蚁蛉翅脉断裂问题,利用Tensor Voting技术修补其数字照片中断裂的翅脉;展示将其应用于蚁蛉模式识别前期处理,以获取主要翅脉尽量完整信息的算法;数值实验中采用3种蚁蛉翅的图像作为测试,收到了很好的结果.

高速列车司机室内热舒适性的评价与优化

高速列车司机室是整个列车运行的控制中枢,舒适的热环境可有效保证司机良好的工作状态,从而提高列车运行的安全性。本文利用Airpak三维软件对某型高速列车司机室内夏季和冬季极端工况下的热环境进行仿真计算,对司机室内的热舒适性进行评价。计算结果表明:夏季极端工况(室外温度35℃)下,司机头部温度偏高,头部PMV值偏大,人体感觉偏热;冬季极端工况(室外温度-20℃)下,热环境参数指标满足热舒适性要求。在不改变原有送风系统结构设计的前提下,对司机室空调送风口的风量分配以及送风角度进行了优化。仿真结果表明:优化后的司机室热环境得到明显改善。

贝叶斯网络参数的在线学习算法及应用

以 EM算法为基础 ,在给定贝叶斯网络结构情况下 ,研究分析了 Voting EM算法并利用该算法对防洪决策贝叶斯网络进行在线参数学习 ,将该算法与 EM算法的学习结果进行了比较分析 ,结果表明 Voting EM算法不但能够进行在线参数学习 。

基于多层SimHash的Android恶意应用程序检测方法

提出一个基于多层SimHash的相似度检测方法,通过对APK文件进行分析,最终从5个方面提取分析内容来表征APK,同时在每一层上使用改进的SimHash方法进行相似度检测分析。通过从APK文件中提取的Android Manifest.xml文件、从dex反编译得出的Smali代码累加和、Smali文件指令提取、Java代码集合、Java指令集提取5个层面进行分析。同时通过学习Voted Perceptron投票算法,将其应用到检测过程中,采用信任值权重的方法,为每一层赋予一个可信值,并在最后得出结果时将每一层结果结合权重分析,实验分析结果表明该方法具有更好的检测效果。

应用Voting Machine构建研究型、互动型的双语物理课堂的研究与实践

高等教育"质量工程"的实施为高等学校本科教学提出了更新、更高的要求和挑战.本文报道了应用Voting Machine这一具有强大的互动和统计功能的教学设备在双语物理课堂上开展研究型、互动型教学的实践和研究成果.

4种状态下OSAHS患者上气道软组织狭窄程度的镜下比较

目的比较阻塞性睡眠呼吸暂停低通气综合征(obstructive sleep apnea-hypopnea syndrome,OSAHS)患者在不同状态下上气道软组织狭窄部位、形态及程度。方法随机抽取48例经多导睡眠监测(polysomnograpy,PSG)确诊的中重度OSAHS患者,分别行清醒坐位、清醒平卧位、清醒侧卧位及药物诱导睡眠平卧位4种状态下上气道软组织塌陷部位、形态及程度的自身比较,并根据VOTE(velum,oropharynx,tonguebase,epiglottis)评分方法对腭咽平面、口咽侧壁平面、舌根平面及会厌平面狭窄度进行评分。结果清醒坐位、清醒平卧位、清醒侧卧位及药物诱导睡眠平卧位4种状态下,腭咽平面、口咽侧壁平面、舌根平面及会厌平面4个平面阻塞部位及程度差异均有统计学意义(P<0.05);清醒坐位各个平面阻塞最轻,睡眠平卧位最重,尤其舌根及会厌平面差异较大,口咽侧壁平面变化较小。睡眠平卧位,清醒平卧位的VOTE总评分值与呼吸暂停低通气指数(apnea-hypopnea index,AHI)呈正相关(P<0.05)。结论药物诱导睡眠喉镜(drug-induced sleep endoscopy,DISE)对OSAHS患者上气道软组织狭窄部位及程度的判断对于OSASH患者腭咽平面尤其舌咽平面的软组织阻塞定位有一定帮助。