The Early Warning Signs of a Stroke: An Approach Using Machine Learning Predictions

Early stroke prediction is vital to prevent damage. A stroke happens when the blood flow to the brain is disrupted by a clot or bleeding, resulting in brain death or injury. However, early diagnosis and treatment reduce long-term needs and lower health costs. We aim for this research to be a machine-learning method for forecasting early warning signs of stroke. The methodology we employed feature selection techniques and multiple algorithms. Utilizing the XGboost Algorithm, the research findings indicate that their proposed model achieved an accuracy rate of 96.45%. This research shows that machine learning can effectively predict early warning signs of stroke, which can help reduce long-term treatment and rehabilitation needs and lower health costs.

An imputation/copula-based stochastic individual tree growth model for mixed species Acadian forests： a case study using the Nova Scotia permanent sample plot network

Background： A novel approach to modelling individual tree growth dynamics is proposed. The approach combines multiple imputation and copula sampling to produce a stochastic individual tree growth and yield projection system. Methods： The Nova Scotia, Canada permanent sample plot network is used as a case study to develop and test the modelling approach. Predictions from this model are compared to predictions from the Acadian variant of the Forest Vegetation Simulator, a widely used statistical individual tree growth and yield model. Results： Diameter and height growth rates were predicted with error rates consistent with those produced using statistical models. Mortality and ingrowth error rates were higher than those observed for diameter and height, but also were within the bounds produced by traditional approaches for predicting these rates. Ingrowth species composition was very poorly predicted. The model was capable of reproducing a wide range of stand dynamic trajectories and in some cases reproduced trajectories that the statistical model was incapable of reproducing. Conclusions： The model has potential to be used as a benchmarking tool for evaluating statistical and process models and may provide a mechanism to separate signal from noise and improve our ability to analyze and learn from large regional datasets that often have underlying flaws in sample design.

ball tree优化的自动驾驶仿真测试场景生成方法

基于场景的仿真测试方法可以有效加速自动驾驶汽车的测试进程,但是传统的采样方法面对高维度采样空间时无法维持高效性,提出了一种ball tree优化的仿真测试场景采样方法,并基于Carla模拟器构建了仿真测试场景自动化生成框架验证算法的有效性。分别使用随机采样方法、基于KD tree结构的最近邻采样方法与基于ball tree结构的最近邻采样方法进行场景参数采样,并生成不同天气要素下的仿真测试场景进行验证。最后将仿真过程与人工方法进行对比。结果表明,提出方法相对于人工方法具有11.38倍场景制作速度的提升,且相对于KD tree结构的采样方法的场景生成速度提升了27.97%。

A Logarithmic-Complexity Algorithm for Nearest Neighbor Classification Using Layered Range Trees

Finding Nearest Neighbors efficiently is crucial to the design of any nearest neighbor classifier. This paper shows how Layered Range Trees (LRT) could be utilized for efficient nearest neighbor classification. The presented algorithm is robust and finds the nearest neighbor in a logarithmic order. The proposed algorithm reports the nearest neighbor in , where k is a very small constant when compared with the dataset size n and d is the number of dimensions. Experimental results demonstrate the efficiency of the proposed algorithm.

Generating Tree-Lists by Fusing Individual Tree Detection and Nearest Neighbor Imputation Using Airborne LiDAR Data

Individual tree detection (ITD) and the area-based approach (ABA) are combined to generate tree-lists using airborne LiDAR data. ITD based on the Canopy Height Model (CHM) was applied for overstory trees, while ABA based on nearest neighbor (NN) imputation was applied for understory trees. Our approach is intended to compensate for the weakness of LiDAR data and ITD in estimating understory trees, keeping the strength of ITD in estimating overstory trees in tree-level. We investigated the effects of three parameters on the performance of our proposed approach: smoothing of CHM, resolution of CHM, and height cutoff (a specific height that classifies trees into overstory and understory). There was no single combination of those parameters that produced the best performance for estimating stems per ha, mean tree height, basal area, diameter distribution and height distribution. The trees in the lowest LiDAR height class yielded the largest relative bias and relative root mean squared error. Although ITD and ABA showed limited explanatory powers to estimate stems per hectare and basal area, there could be improvements from methods such as using LiDAR data with higher density, applying better algorithms for ITD and decreasing distortion of the structure of LiDAR data. Automating the procedure of finding optimal combinations of those parameters is essential to expedite forest management decisions across forest landscapes using remote sensing data.

Assessment of the State of Forests Based on Joint Statistical Processing of Sentinel-2B Remote Sensing Data and the Data from Network of Ground-Based ICP-Forests Sample Plots

The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied territory mainly in a regular manner, laid and surveyed according to the ICP-Forests methodology with some additions. The total area of the sample plots is a small part of the entire study area. One of the objectives of the study was to determine the possibility of using the k-NN (nearest neighbor method) to assess the state of forests throughout the whole studied territory by joint statistical processing of data from ground sample plots and Sentinel-2B imagery. The data of the ground-based sample plots were divided into 2 equal parts, one for the application of the k-NN method, the second for checking the results of the method application. The systematic error in determining the mean damage class of the tree stands on sample plots by the k-NN method turned out to be zero, the random error is equal to one point. These results offer a possibility to determine the state of the forest in the entire study area. The second objective of the study was to examine the possibility of using the short-wave vegetation index (SWVI) to assess the state of forests. As a result, a close statistically reliable dependence of the average score of the state of plantations and the value of the SWVI index was established, which makes it possible to use the established relationship to determine the state of forests throughout the studied territory. The joint use and statistical processing of remotely sensed data and ground-based test areas by the two studied methods make it possible to assess the state of forests throughout the large studied area within the image. The results obtained can be used to monitor the state of forests in large areas and design appropriate forestry protective measures.

Nearest neighbor search algorithm for GBD tree spatial data structure

This paper describes the nearest neighbor (NN) search algorithm on the GBD(generalized BD) tree. The GBD tree is a spatial data structure suitable for two-or three-dimensional data and has good performance characteristics with respect to the dynamic data environment. On GIS and CAD systems, the R-tree and its successors have been used. In addition, the NN search algorithm is also proposed in an attempt to obtain good performance from the R-tree. On the other hand, the GBD tree is superior to the R-tree with respect to exact match retrieval, because the GBD tree has auxiliary data that uniquely determines the position of the object in the structure. The proposed NN search algorithm depends on the property of the GBD tree described above. The NN search algorithm on the GBD tree was studied and the performance thereof was evaluated through experiments.

基于覆盖树的自适应均值漂移聚类算法

为解决均值漂移聚类算法聚类效果依赖于带宽参数的主观选取,以及处理密度变化大的数据集时聚类结果精确度问题,提出一种基于覆盖树的自适应均值漂移聚类算法MSCT(MeanShift based on Cover-Tree)。构建一个覆盖树数据集,在计算漂移向量过程中结合覆盖树数据集获得新的漂移向量结果KnnShift,在不同数据密度分布的数据集上都能自适应产生带宽参数,所有数据点完成漂移过程后获得聚类结果。实验结果表明,MSCT算法的聚类效果整体上优于MS、DBSCAN等算法。

基于集成学习的交通事故严重程度预测研究与应用

目前自动驾驶技术重点是关注如何主动避免碰撞,然而在面对其他交通参与者入侵而导致不可避免的碰撞事故场景时,预测车辆在不同行驶模式下的碰撞严重程度来降低事故严重程度的研究却很少。为此,提出一种双层Stacking事故严重程度预测模型。基于真实交通事故数据集NASS-CDS完成训练,模型输入为车辆传感器可感知得到的事故相关特征,输出为车内乘员最高受伤级别。在第1层中,通过实验对不同学习器组合进行训练,最终综合考虑预测性能以及耗时挑选K近邻、自适应提升树、极度梯度提升树作为基学习器;在第2层中,为降低过拟合,采用逻辑回归作为元学习器。实验结果表明,该方法准确率达到85.01%,在精确率、召回率和F1值方面优于其他个体模型和集成模型,该预测结果可作为智能车辆决策规划模块先验信息,帮助车辆做出正确的决策,减缓事故损害。最后阐述了模型在L_(2)辅助驾驶与L_(4)自动驾驶车辆中的应用,在常规车辆安全防护的基础上进一步提升车辆的安全性。

应用非线性KNN数据搜索的三维叠前自由表面多次波预测

自由表面多次波预测(SRMP)是自由表面多次波消除(SRME)以及成像的重要环节。SRME技术尽管有效,但理论上需要规则而密集的地震数据采集方式。然而实际炮点、检波点空间分布稀疏,地震数据不能满足SRME理论要求,常规的做法是在SRME之前将地震数据规则化。为了避免数据规则化环节,首先建立索引数据树管理三维叠前地震数据,并采用基于树形数据结构的非线性K近邻算法(KNN)从地震数据中实时搜索两道近似地震数据;然后利用动校—反动校消除实时搜索得到的近似地震道与实际地震道之间的旅行时误差;由以上两步获得单道孔径内任意向下反射点(DRP)所需要的两道地震数据用于SRMP。单道孔径内任意DRP均可由SRMP预测对应的多次波模型道,叠加所有DRP对应的预测结果可获得该道稳定的多次波模型数据。将该方法用于扩展的三维Pluto模型数据,结果表明该方法能有效预测三维自由表面多次波,从而保证高质量的自由表面多次波衰减结果。实际地震数据的应用证明了方法的实用性。

结合K均值聚类和KD-Tree搜索的快速分形编码方法

利用部分失真搜索求解传统K均值聚类算法中的最近邻搜索问题,显著地减少了传统算法的乘法次数,从而提高了聚类速度;然后用改进后的聚类算法来加速分形编码:首先将定义域块聚类并为每个类建立一棵KD-Tree,编码时对每个值域块先后用部分失真搜索与近似最近邻搜索得到与其距离最近的若干KD-Tree及其上的若干最近邻,而其最优匹配块即由后者产生.实验结果表明,相对于全局搜索,该方法能大幅度地提高编码速度和较大地提高压缩比,而解码质量只有很小的下降;相对于同类方法,在相同压缩比下有更好的加速效果和解码质量.

基于KD-Tree搜索和SURF特征的图像匹配算法研究

针对图像匹配时进行特征检测和匹配的搜索时间长的问题,文章研究了基于KD-Tree搜索和SURF特征的图像匹配算法。该算法首先提取得到图像的SURF特征并生成特征描述向量,然后为这些特征描述向量建立KD-Tree索引,最后通过计算每个特征点的与其距离最近的若干个KD-Tree上的最近邻点,完成特征匹配工作。实验结果表明,与SIFT算法相比,SURF算法进行特征检测的速度要快2～3倍;与全局最近邻搜索相比,基于KD-Tree索引的近似最近邻搜索大大减少了计算量,较大地提高了SURF算法的匹配速度。

山西榆社北魏孙龙石椁研究

孙龙石椁是目前所见北魏洛阳时代纪年最早的石质葬具,其文字结合图像的主题表达形式颇具特点,具有重要的标本学价值。本文回顾了孙龙石椁的发现与研究历程,纠正了一些认识上的偏差,对石椁形制、题铭与榜题进行了分析,认为这些文字表达的是一种旌表孝行的意图。文章重点对图像进行了全面系统的研究。结合大量考古新发现,对图像布局及题材渊源、主题表达等进行了探讨,认为墓主人夫妇宴飨图、四神图、升天图为主题图像;射猎图、杂技图、牛车鞍马出行图、树下并坐图等居于次要地位。其在文化面貌上表现出受洛阳主流文化的影响,又留有许多平城传统的印记,体现出大变革时代文化交融、过渡的特征。

一种基于数据空间自适应规则网格划分的Skd-tree最近邻算法

针对现有kd-tree KNN算法的不足,提出了一种基于规则数据空间网格划分的Skdtree KNN算法。该树形结构把数据置于空间网格内部,能更好地利用数据的空间分布特性,可以在更小的范围内对被查询数据定位,有效避免对部分无关数据的计算或回溯;同时,为了适应网格空间的规则性,算法中采用了超方体而非超球体来查询局域空间中的最优结果,避免了空间异构带来的缺点。数字实验的结果证明:Skd-tree KNN比kd-tree KNN具备更好的索引定位精度、更少的无关数据回溯和计算、更短的查询时间,尤其适用于数据样本较大或高维度数据的最近邻查询。

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.

空间网络数据库中基于M-tree索引的反最近邻查询算法

欧式空间中的反最近邻查询算法不适用于空间网络环境,故采用任意度量空间中的M-tree索引结构,进行空间网络数据库中的反最近邻查询处理。首先通过预计算的方法得到网络距离信息,依据此距离信息,对空间网络对象建立M-tree索引结构。然后,给出并证明了M-tree中间结点修剪定理,提出一种适用于空间网络环境的反最近邻查询算法。最后实验验证了该算法的有效性。

数字报版面布局自动生成方法

报纸版面对新闻有一个价值排序合理且美观新颖的展示,让读者面对众多新闻,在短时间获取最具价值的讯息和浏览乐趣。然而,对于排版人员而言,手动制作美观易读的报纸版面布局需耗费大量时间成本。本文结合贝叶斯网络推断和约束规划技术,提出一种数字报版面布局自动生成方法。该方法首先基于历史版面数据驱动和专家经验对数字报版面的结构和属性建立推断模型,使得新生成的版面具有历史特定风格;然后利用推断结果建立混合整数约束规划模型计算版面布局,从而显著减少模型求解空间,提高布局质量。此外,推断模型提供多种可用候选结构为生成结果提供多样性,规划模型具有良好的对齐性能。为了训练和验证模型,本文构建并公开了一个中文版面数据集,包括详细版面新闻属性标签数据。用户研究结果表明版面布局自动生成方法的有效性。

道路网中基于RRN-Tree的CKNN查询

现有针对基于道路网络的CKNN查询研究，主要是将道路网络以路段和节点的形式进行建模，转化成基于内存的有向／无向图，该模型存在2个问题：一个是道路网络中路段数据量大，导致索引结构分支过多、移动对象更新频繁；另一个是图表示方法不能很好地处理十字路口转向、U型转弯等交通规则。针对此问题，提出道路网中基于RRN—Tree的移动对象CKNN查询算法，包括索引结构设计和移动对象查询算法设计，采用路线对道路网建模，基于网络边扩展方式，实现复杂条件下的道路网络CKNN查询。实验结果表明，在各种网络密度和兴趣点对象分布密度下，与经典的IMA／GMA算法相比，基于RRN—Tree索引方法的查询性能提高1．5倍-2．13倍。

基于P-trees kNN算法的毒物分类方法

中毒是一种发生机率较大、对人体危害大的病症,而及时明确诊断,正确、规范的治疗既是抢救成功的关键,又是至今没有很好解决的难题。毒物层出不穷,基层急救医生的毒物知识和中毒抢救知识又明显不足,因此临床急需一种辅助系统以帮助各级急救医生提高中毒诊治水平及中毒抢救成功率。利用不同中毒表现对应不同毒物的权值向量构成"中毒表现加权向量表",并将它作为训练数据集的属性值。构建中毒表现加权向量表的P树,并选择HOBBit距离作为距离度量标准,运用P-trees kNN分类算法进行毒物分类。将该方法应用到毒物分类系统中,运行效果良好。

集成多方法的废酸装置风机K7200轴承故障诊断

在废酸装置风机K7200中,轴承作为重要的机械部件,准确判断其故障(健康状态、内圈故障、外圈故障和滚动体故障)可以提高维修效率。克服实际作业场景中人工诊断的缺点,提出了集成多方法的轴承故障诊断策略:分别采用K最近邻算法(简称KNN)、逻辑回归(简称LR)和决策树(简称DT)进行诊断,对结果进行投票集成。实验结果表明,采用集成多方法的故障诊断法较KNN、LR和DT算法,故障诊断的准确率分别提升了3.69%、5.03%、6.3%。