A Robust Tuned Random Forest Classifier Using Randomized Grid Search to Predict Coronary Artery Diseases

Coronary artery disease(CAD)is one of themost authentic cardiovascular afflictions because it is an uncommonly overwhelming heart issue.The breakdown of coronary cardiovascular disease is one of the principal sources of death all over theworld.Cardiovascular deterioration is a challenge,especially in youthful and rural countries where there is an absence of humantrained professionals.Since heart diseases happen without apparent signs,high-level detection is desirable.This paper proposed a robust and tuned random forest model using the randomized grid search technique to predictCAD.The proposed framework increases the ability of CADpredictions by tracking down risk pointers and learning the confusing joint efforts between them.Nowadays,the healthcare industry has a lot of data but needs to gain more knowledge.Our proposed framework is used for extracting knowledge from data stores and using that knowledge to help doctors accurately and effectively diagnose heart disease(HD).We evaluated the proposed framework over two public databases,Cleveland and Framingham datasets.The datasets were preprocessed by using a cleaning technique,a normalization technique,and an outlier detection technique.Secondly,the principal component analysis(PCA)algorithm was utilized to lessen the feature dimensionality of the two datasets.Finally,we used a hyperparameter tuning technique,randomized grid search,to tune a random forest(RF)machine learning(ML)model.The randomized grid search selected the best parameters and got the ideal CAD analysis.The proposed framework was evaluated and compared with traditional classifiers.Our proposed framework’s accuracy,sensitivity,precision,specificity,and f1-score were 100%.The evaluation of the proposed framework showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing frameworks.

Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years.Coronary cardiovascular(CHD)is a kind of heart and blood vascular disease.Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders.Implementing Grid Search Optimization(GSO)machine training models is therefore a useful way to forecast the sickness as soon as possible.The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate.Three models with a cross-validation approach do the required task.Feature Selection based on the use of statistical and correlation matrices for multivariate analysis.For Random Search and Grid Search models,extensive comparison findings are produced utilizing retrieval,F1 score,and precision measurements.The models are evaluated using the metrics and kappa statistics that illustrate the three models’comparability.The study effort focuses on optimizing function selection,tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification.Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.

基于Gridsearch-SVM梯形区域极点分类的故障诊断

针对一类线性定常系统,基于梯形区域极点配置,给出了执行器部件故障诊断的一种方法。首先,利用极点观测器,通过测量系统的状态,得到极点的动态信息;其次,根据模拟各通道执行器故障,实时采集闭环系统的极点信息,形成极点分类数据库;最后,利用支持向量机算法(Support Vector Machine,SVM)根据不同通道发生故障时极点所处位置不同,设计极点分类器,对极点进行分类,实现对系统的故障诊断。针对SVM中惩罚因子和核宽度系数需要依靠先验知识的缺陷,采用Grid search优化其参数,缩小寻优范围。仿真结果表明设计方案的可行性以及故障诊断的有效性。

The Grid Search Algorithm of Tectonic Stress Tensor Based on Focal Mechanism Data and Its Application in the Boundary Zone of China, Vietnam and Laos

Stress field plays a key role in geodynamics. In this study, an algorithm to determine the stress tensor and its confidence range from focal mechanism data by using grid search method was proposed. The experiment uses artificial focal mechanism data which were generated by extensional, compression and strike-slip stress regime and different level of noise, shows that the precision of the estimated stress tensor based on this algorithm is greatly improved compared with traditional algorithms. This algorithm has three advantages：（1） The global optimal solution of the stress tensor is determined by fine grid search of 1o×1o×1o×0.01 and local minimum value is avoided; （2） precision of focal mechanism data can be considered, i.e., different weight of the focal mechanism data contributes differently to the process of determining stress tensor; （3） the confidence range of the determined stress tensor can be obtained by using F-test. We apply this algorithm in the boundary zone of China, Vietnam and Laos, and obtain the stress field with SSE-NNW compressive stress direction and NEE-SWW extensional stress direction. The stress ratio is 0.6, which shows that the eigen values of the stress tensor are nearly in arithmetic sequence. The stress field in this region is consistent with the left-lateral strike slip of the Dienbien-Lauangphrabang arc fault. The result will be helpful in studying the geological dynamic process in this region.

基于Grid-Search_PSO优化SVM回归预测矿井涌水量

为了解决矿井涌水量预测难题,在Grid-Search_PSO优化SVM参数的基础上,采用SVM非线性回归预测法,对大海则煤矿1999~2008年7月份的矿井涌水量进行了预测。分析对比SVM回归预测法和ARIMA时间序列预测法预测结果的数据误差,发现SVM回归法预测值与实测值之间的偏差比ARIMA时间序列法要小很多。可见在影响矿井涌水量各种因素值具备的情况下,SVM非线性回归预测所建立的模型能够更准确地预测矿井的涌水量,在矿井安全生产中具有很大的应用价值。

Research on Low Voltage Series Arc Fault Prediction Method Based on Multidimensional Time-Frequency Domain Characteristics

The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.

Grid-Search和PSO优化的SVM在Shibor回归预测中的应用研究

作为一种动态和非稳定时间序列,Shibor发展变化是随机波动的,难以准确预测Shibor的波动性.支持向量机(SVM)在回归预测非线性时间序列方面有很好地预测效果,SVM的预测精度和泛化能力的核心是参数的优化选择,分别用网格搜索法(Grid-Search)和粒子群(PSO)算法来优化SVM的参数c和g.从而将参数优化后的SVM非线性回归预测法与基于传统ARIMA时间序列预测结果进行对比分析.实验表明,优化后的SVM回归预测方法比ARIMA时间序列方法更精确,在实际中具有很大的应用价值.

METADATA EXPANDED SEMANTICALLY BASED RESOURCE SEARCH IN EDUCATION GRID

With the rapid increase of educational resources, how to search for necessary educational resource quickly is one of most important issues. Educational resources have the characters of distribution and heterogeneity, which are the same as the characters of Grid resources. Therefore, the technology of Grid resources search was adopted to implement the educational resources search. Motivated by the insufficiency of currently resources search methods based on metadata, a method of extracting semantic relations between words constituting metadata is proposed. We mainly focus on acquiring synonymy, hyponymy, hypernymy and parataxis relations. In our schema, we extract texts related to metadata that will be expanded from text spatial through text extraction templates. Next, metadata will be obtained through metadata extraction templates. Finally, we compute semantic similarity to eliminate false relations and construct a semantic expansion knowledge base. The proposed method in this paper has been applied on the education grid.

A Rapid Grid Search Method for Solving Dynamic Programming Problems in Economics

我们在在经济解决动态编程问题介绍一个快速的格子搜索方法。比作主流的格子搜索方法，由使用鸣钟者方程的本地信息，这个方法能显著地在由减少点在控制空格寻找了的格子解决动态编程问题增加效率。

Nearest neighbor search algorithm based on multiple background grids for fluid simulation

The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth length is introduced. Through tested on lid driven cavity flow, it is clear that this method can provide high accuracy. Analysis and experiments have been made on its parallelism, and the results show that this method has better parallelism and with adding processors its accuracy become higher, thus it achieves that efficiency grows in pace with accuracy.

Managing of Smart Micro-Grid Connected Scheme Using Group Search Optimization

This article introduces a group search optimization (GSO) based tuning model for modelling and managing Smart Micro-Grids connected system. In existing systems, typically tuned PID controllers are engaged to point out the load frequency control (LFC) problems through different tuning techniques. Though, inappropriately tuned PID controller may reveal pitiable dynamical reply and also incorrect option of integral gain may even undermine the complete system. This research is used to explain about an optimized energy management system through Group Search Optimization (GSO) for building incorporation in smart micro-grids (MGs) with zero grid-impact. The essential for this technique is to develop the MG effectiveness, when the complete PI controller requires to be tuned. Consequently, we proposed that the proposed GSO based algorithm with appropriate explanation or member representation, derivation of fitness function, producer process, scrounger process, and ranger process. An entire and adaptable design of MATLAB/SIMULINK also proposed. The related solutions and practical test verifications are given. This paper verified that the proposed method was effective in Micro-Grid (MG) applications. The comparison results demonstrate the advantage of the proposed technique and confirm its potential to solve the problem.

基于GS-SVR的架空输电线路工程投资估算预测研究

传统的投资估算编制模式存在过度依赖定额的现象,随着大量工程造价数据的积累,利用其实现投资估算,以弥补传统定额计价模式的不足,能够对建设项目工程造价起到总体控制作用。文章以架空输电线路工程为例,基于支持向量回归机(Support Vector Regression,SVR)研究架空输电线路工程投资估算问题。结果表明:通过选取影响架空输电线路工程投资估算的主要指标,构建基于SVR的架空输电线路工程投资估算模型,并利用改进的网格搜索法(Grid Search,GS)优化模型参数,得到基于GS-SVR的投资估算预测模型;与传统的线性回归和SVR模型相比,GS-SVR模型表现出更为良好的性能。

基于聚类和GBDT的镀锌钢卷力学性能预测

热镀锌钢卷力学性能影响因素之间关系复杂,限制了模型精度的提升。采用k-means算法利用化学成分属性对镀锌钢卷数据集进行聚类,将数据聚成三种模式簇实现样本的优选。利用梯度提升树算法,开展各模式数据集与不划分模式的全数据集下的力学性能建模研究,最后结合网格搜索与交叉验证方法进行模型参数优化。研究结果表明,分模式下模型MAE误差相比于全数据集建模平均减小0.85 MPa。参数优化后,各模式下MAE误差平均减少5.19 MPa,RMSE误差平均减少3.63 MPa,提高了预测模型精度。

GSM-SVM在地震震级预测中的应用

针对地震震级影响因子众多且关系重复等问题,为合理预测地震震级,提出了基于网格搜索法优化支持向量机(support vector machine,SVM)的地震震级预测模型。选取地震累积频度、累积释放能量、b值、异常震群个数、地震条带个数、活动周期和相关区震级等7个影响因子,利用主成分分析法(principal component analysis,PCA)去除因子间的冗余信息,降低输入维数,并利用网格搜索法(grid search method,GSM)确定SVM参数C和g,建立震级预测模型,并对测试样本进行预测,与遗传算法(genetic algorithm,GA)和粒子群算法(particle swarm optimization,PSO)预测结果相对比,结果表明:PCA-GSM-SVM模型预测结果平均相对误差为1.29%,具有较高的预测精度。

Grid和P2P混合环境中一种基于信任的资源搜索机制

Grid和P2P两种分布式计算模式中的资源搜索算法均假设节点提供可靠的资源,但Grid和P2P混合计算环境的动态、异构、自组织等特点使得一些节点存在冒名和提供虚假服务等行为。本文对基于经验和最好邻居搜索机制进行改进,引入信任因子,提出了基于信任的资源搜索机制。该机制有效抑制了欺骗行为,提高了资源搜索的可靠性和安全性。

基于网格搜索和投票分类模型的喷油器故障诊断研究

为了提高高压共轨试验台对喷油器检修效率,提出一种基于网格搜索和投票分类模型的喷油器故障自动诊断方法。由于压电喷油器故障数据采集困难,使用AMESim软件模拟不同轨压和脉宽状态下压电喷油器可能出现的多种故障情况。随后,将采集到的1 760组数据使用由随机森林、支持向量机和GBM组成的投票分类模型进行训练,并使用网格搜索法优化各分类器的超参数。实验结果表明:该模型对压电喷油器的5种故障状态及正常状态诊断时的准确率、精确率、召回率和F1-score分别为98.86%、99.13%、98.56%、98.83%,表现出较高的准确性和稳定性。该方法能够快速高效地对喷油器故障情况进行定位。

基于Grid-GSA算法的植保无人机路径规划方法

为了提高植保无人机的作业效率,研究了一种路径规划方法。运用栅格法构建环境模型,根据实际的作业区域规模、形状等环境信息和无人机航向,为相应栅格赋予概率,无人机优先选择概率高的栅格行进。基于上述机制实现了在形状不规则的作业区域内进行往复回转式全覆盖路径规划;以每次植保作业距离为变量,根据仿真算法得出返航点数量与位置来确定寻优模型中的变量维数范围,以往返飞行、电池更换与药剂装填等非植保作业耗费时间最短为目标函数,通过采用引力搜索算法,实现对返航点数量与位置的寻优;为无人机设置必要的路径纠偏与光顺机制,使无人机能够按既定路线与速度飞行。对提出的路径规划方法进行了实例检验,结果显示,相比于简单规划与未规划的情况,运用Grid-GSA规划方法得出的结果中往返飞行距离总和分别减少了14%与68%,非植保作业时间分别减少了21%与36%,其它各项指标也均有不同程度的提高。在验证测试试验中,实际的往返距离总和减少了322 m,实际路径与规划路径存在较小偏差。验证了路径规划方法具有合理性、可行性以及一定的实用性。

一种改进的Tabu Search算法及其在区域电网无功优化中的应用

提出将改进的Tabu(禁忌)搜索算法用于区域电网无功电压优化控制问题的求解.首先根据已知的实际电网的历史数据获得可行的初始解,然后对区域电网采用改进的禁忌搜索方法进行无功优化.在求解的过程中,由于对Tabu表中所记录的"移动"采取"有条件地释放Tabu表中的记录"这一策略,可以使搜索有效地跳出局部极小值点,更好地找到最优解.通过IEEE-14节点算例验证了该算法的有效性.

P-Grid:一种高效的自组织P2P系统

P2P(peertopeer)技术在分布计算时代发挥着越来越重要的作用,但在数据管理方面,P2P的应用表现出较大的局限性。作为一种快速高效的P2P信息系统P-Grid,它能够极大地减少其它P2P系统所需要的网络带宽。P-Grid系统的构造和维护严格依照本地交互的随机算法,即使在不可靠的节点也能提供可靠的访问,并且能够较好地衡量存取和通信代价。

基于改进人工鱼群算法的蠕虫机器人路径规划

针对人工鱼群算法在机器人路径规划中存在路径长、精度不高、易陷入局部最优等问题,提出了一种改进的人工鱼群算法,旨在提高算法效率及精度。首先,在算法觅食行为中加入寻优循环,减少算法在路径规划中选取位置点的随机性,使机器人能够更快地走向目标点;其次,融合禁忌搜索算法,通过引入禁忌表来记录算法陷入局部最优的路径,使算法在选取新位置点时能够避开局部最优区域,避免算法在局部过度循环,同时对规划出的路径进行优化处理,删去重复栅格点之间的路径,保证路径中没有重复的栅格点;最后,将改进后的人工鱼群算法应用在一种新型的三维栅格地图中。实验结果表明:相较于其他对比算法,在地图1、2、3中改进人工鱼群算法所取得的平均路径长度分别减少了10%、15%、30%,在复杂地图中路径规划的成功率提高了75%。