Classification of brain tumor using devernay sub-pixel edge detection and k-nearest neighbours methodology

Any disease can be treated only once it is imaged,detected and classified.This paper proposes a set of algorithms for classification of a brain tumor with better accuracy and efficiency.The proposal uses a JPEG format of the DICOM image fed into three stages namely pre-processing,segmentation using sub-pixel edge detection method and using the nearest neighbor methodology for the detection and differentiation of benign and malignant tumors.

Active learning accelerated Monte-Carlo simulation based on the modified K-nearest neighbors algorithm and its application to reliability estimations

This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability.

A Hybrid Approach to Neighbour Discovery in Wireless Sensor Networks

In the contemporary era of unprecedented innovations such as Internet of Things(IoT),modern applications cannot be imagined without the presence of Wireless Sensor Network(WSN).Nodes in WSN use neighbour discovery(ND)protocols to have necessary communication among the nodes.Neighbour discovery process is crucial as it is to be done with energy efficiency and minimize discovery latency and maximize percentage of neighbours discovered.The current ND approaches that are indirect in nature are categorized into methods of removal of active slots from wake-up schedules and intelligent addition of new slots.The two methods are found to have certain drawbacks.Thefirst category disturbs original integrity of wake-up schedules leading to reduced chances of discovering new nodes in WSN as neighbours.When second category is followed,it may have inefficient slots in the wake-up schedules leading to performance degradation.Therefore,the motivation behind the work in this paper is that by combining the two categories,it is possible to reap benefits of both and get rid of the limitations of the both.Making a hybrid is achieved by introducing virtual nodes that help maximize performance by ensuring original integrity of wake-up schedules and adding of efficient active slots.Thus a Hybrid Approach to Neighbour Discovery(HAND)protocol is realized in WSN.The simulation study revealed that HAND outperforms the existing indirect ND models.

GHM-FKNN:a generalized Heronian mean based fuzzy k-nearest neighbor classifier for the stock trend prediction

Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-nearest neighbor(KNN), and fuzzy KNN(FKNN), have difficulty in accurately predicting the stock trend(uptrend or downtrend) for a given date, a generalized Heronian mean(GHM) based FKNN predictor named GHM-FKNN was proposed.GHM-FKNN combines GHM aggregation function with the ideas of the classical FKNN approach.After evaluation, the comparison results elucidated that GHM-FKNN outperformed the other best existing methods RF, PRF, KNN and FKNN on independent test datasets corresponding to three stocks, namely AAPL, AMZN and NFLX.Compared with RF, PRF, KNN and FKNN, GHM-FKNN achieved the best performance with accuracy of 62.37% for AAPL, 58.25% for AMZN, and 64.10% for NFLX.

Diagnosis of Disc Space Variation Fault Degree of Transformer Winding Based on K-Nearest Neighbor Algorithm

Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose the disc space variation(DSV)fault degree of transformer winding,this paper presents a diagnostic method of winding fault based on the K-Nearest Neighbor(KNN)algorithmand the frequency response analysis(FRA)method.First,a laboratory winding model is used,and DSV faults with four different degrees are achieved by changing disc space of the discs in the winding.Then,a series of FRA tests are conducted to obtain the FRA results and set up the FRA dataset.Second,ten different numerical indices are utilized to obtain features of FRA curves of faulted winding.Third,the 10-fold cross-validation method is employed to determine the optimal k-value of KNN.In addition,to improve the accuracy of the KNN model,a comparative analysis is made between the accuracy of the KNN algorithm and k-value under four distance functions.After getting the most appropriate distance metric and kvalue,the fault classificationmodel based on theKNN and FRA is constructed and it is used to classify the degrees of DSV faults.The identification accuracy rate of the proposed model is up to 98.30%.Finally,the performance of the model is presented by comparing with the support vector machine(SVM),SVM optimized by the particle swarmoptimization(PSO-SVM)method,and randomforest(RF).The results show that the diagnosis accuracy of the proposed model is the highest and the model can be used to accurately diagnose the DSV fault degrees of the winding.

POI数据挖掘在市场调查与分析大赛中的应用——以东北三省养猪企业数据挖掘为例

随着大数据技术的快速发展,海量数据的采集、挖掘和分析广泛应用于商业、农业、医疗等各个领域。本文基于POI数据挖掘构建市场调查方法,通过核密度和最邻近距离分析,利用POI数据分析东北三省养猪企业的分布特征,从而了解地区优势产业,并结合人口分布、企业分布等数据进行校核与验证。研究结果表明,POI数据挖掘为市场调查与分析大赛提供了有效的数据收集和市场分析方法。

基于PCA+KNN和kernal-PCA+KNN算法的废旧纺织物鉴别

该研究采集了15类废旧纺织物的4 998张近红外谱图,以7∶3的比例分为训练集和验证集,并分别采用主成分分析(PCA)与核主成分分析(kernal-PCA)两种不同降维方法对数据进行降维,并选用余弦相似度(cosine)核作为kernal-PCA的最佳核函数,最后分别将PCA和kernal-PCA降维处理后的数据进行k-近邻算法(KNN)训练。结果表明,kernal-PCA+KNN的模型准确率(95.17%)优于PCA+KNN模型的准确率(92.34%)。研究表明,kernal-PCA+KNN算法可以实现15类废旧纺织物识别准确率的提升,为废旧纺织物在线近红外自动分拣提供有力的技术支撑。

蚂蚁筑巢对森林土壤氮库组分含量和分配的影响

为阐明蚂蚁筑巢对纳帕海面山森林土壤氮循环的影响过程及机制,以该区云杉-冷杉森林群落为研究对象,比较蚁巢和非蚁巢2种野外实验处理土壤氮组分(全氮、碱解氮、硝态氮、铵态氮、微生物生物量氮)含量及其分配(碱解氮/全氮、硝态氮/全氮、铵态氮/全氮、微生物生物量氮/全氮)的差异特征,分析蚂蚁筑巢活动引起土壤理化环境变化对氮库组分积累与分配的影响。结果显示:(1)蚂蚁筑巢显著影响土壤氮组分含量积累及分配(P<0.05)。其中,蚁巢土壤的全氮、硝态氮、铵态氮、碱解氮和微生物生物量氮含量是非蚁巢的2.2、3.5、1.4、4.2、1.8倍;蚁巢土壤的氮组分(碱解氮、硝态氮、铵态氮、微生物生物量氮)占全氮比例是非蚁巢的1.88、1.47、1.38、1.58倍;土壤氮库组分及其分配受不同处理、土层及交互作用的影响显著(P<0.05)。(2)回归分析显示,土壤铵态氮和硝态氮分别解释全氮变化的87.29%、80.84%。(3)曼特尔分析表明,氮库组分积累的主要驱动因子是土壤孔隙度、全磷和pH,土壤氮库组成分配的主要驱动因子是有机质、速效钾和速效磷。可得结论:蚂蚁筑巢显著改变土壤孔隙度、酸碱性、碳磷钾养分等环境因子,进而调控纳帕海面山森林土壤氮库组分含量的积累与分配。研究结果有助于理解高原湿地面山土壤氮积累过程的土壤动物学调控机制。

蚂蚁筑巢对纳帕海面山土壤碳积累及分配的影响

【目的】揭示纳帕海面山森林蚁巢与非蚁巢土壤总有机碳储量及活性有机碳组分的分配特征,为阐明蚂蚁活动对森林土壤有机碳沉积影响的过程及机制提供关键数据支撑。【方法】以纳帕海面山云杉−冷杉森林群落为研究对象,比较蚁巢和非蚁巢2种处理土壤总有机碳储量、活性碳组分(微生物生物量碳、易氧化有机碳、颗粒有机碳、可溶性有机碳)及其碳分配(微生物生物量碳/总有机碳、易氧化有机碳/总有机碳、颗粒有机碳/总有机碳、可溶性有机碳/总有机碳)的差异,并分析蚂蚁筑巢活动引起土壤理化环境改变对总有机碳储量及活性有机碳组分分配的影响。【结果】蚂蚁筑巢显著影响土壤有机碳积累及活性碳组分分配(P<0.05)。其中,蚁巢土壤有机碳储量是非蚁巢的5.7倍;蚁巢土壤总有机碳、微生物生物量碳、易氧化有机碳、颗粒有机碳含量分别提高了3.8、2.7、4.0、3.5倍;蚁巢土壤易氧化有机碳/总有机碳均值大小比蚁巢高出1.50%,而非蚁巢土壤微生物生物量碳/总有机碳、颗粒有机碳/总有机碳、可溶性有机碳/总有机碳均值分别比蚁巢高0.43%、3.30%、3.21%;不同处理和土层仅对土壤总有机碳、微生物生物量碳、颗粒有机碳和可溶性有机碳含量存在明显的交互作用(P<0.05);回归分析结果表明土壤微生物生物量碳、颗粒有机碳、可溶性有机碳和易氧化有机碳分别解释了96.45%、96.35%、95.13%、94.27%的总有机碳变化;主成分分析表明土壤密度、全氮和速效磷是总有机碳储量的主控因子,而速效氮、速效磷、土壤密度等是活性碳组分积累的主要驱动因子;全钾、含水量分别是颗粒性有机碳与可溶性有机碳分配的主要影响因子。【结论】蚂蚁筑巢主要通过改变土壤紧实度、氮磷养分条件等环境因子,进而调控纳帕海面山森林土壤总有机碳储量与活性有机碳组分的分配,研究结果有助于理解高原湿地面山土壤碳积累过程的土壤动物学调控机制。

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.

Outsmarting Android Malware with Cutting-Edge Feature Engineering and Machine Learning Techniques

The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy breaches.This escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world.Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false positives.This meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in malwaredetection.The proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data accuracy.The Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction model.This study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert opinion.The proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,respectively.The empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both datasets.This approach has substantial potential for real-world applications and can serve asa valuable tool for preventing the spread of Androidmalware and enhancing mobile device security.

一种基于卷积神经网络的室内定位方法

针对实际室内定位场景中的无线接入点信号不稳定引起定位精度低的问题,提出一种基于卷积神经网络的室内定位方法,该方法包括离线阶段和在线阶段,其中离线阶段主要完成对无线接入点信号采集,经过预处理后作为卷积神经网络模型的训练数据。在线阶段利用训练好的模型完成粗定位,估计位置所在的区域,最后利用加权k近邻算法计算精确的位置坐标。通过与SVR、KNN算法对比,结果表明,在二维平面回归定位问题中优于其他算法。

城市轨道交通超短时客流预测模型研究及应用

超短时客流预测是城市轨道交通调度指挥中的关键基础性问题,现有的方法及模型各有优缺点,尚不能很好地满足现场实际工作需要。首先,基于上海城市轨道交通海量客流数据,对客流特征及其影响因素进行提取与分析,在此基础上引入“K最近邻算法”研究建立超短时客流预测模型。以上海城市轨道交通网络为实际背景的初步应用及结果分析表明,研究成果能对运营当天早晚高峰时段(7:00—10:00和17:00—20:00)客流做出超短时预测,具有较好的准确性、时效性和实用性,为调度指挥提供有力的客流数据支撑,助力构建城市轨道交通网络智慧客运组织调度系统。

A Novel Insertion Solution for the Travelling Salesman Problem

The studypresents theHalfMax InsertionHeuristic (HMIH) as a novel approach to solving theTravelling SalesmanProblem (TSP). The goal is to outperform existing techniques such as the Farthest Insertion Heuristic (FIH) andNearest Neighbour Heuristic (NNH). The paper discusses the limitations of current construction tour heuristics,focusing particularly on the significant margin of error in FIH. It then proposes HMIH as an alternative thatminimizes the increase in tour distance and includes more nodes. HMIH improves tour quality by starting withan initial tour consisting of a ‘minimum’ polygon and iteratively adding nodes using our novel Half Max routine.The paper thoroughly examines and compares HMIH with FIH and NNH via rigorous testing on standard TSPbenchmarks. The results indicate that HMIH consistently delivers superior performance, particularly with respectto tour cost and computational efficiency. HMIH’s tours were sometimes 16% shorter than those generated by FIHand NNH, showcasing its potential and value as a novel benchmark for TSP solutions. The study used statisticalmethods, including Friedman’s Non-parametric Test, to validate the performance of HMIH over FIH and NNH.This guarantees that the identified advantages are statistically significant and consistent in various situations. Thiscomprehensive analysis emphasizes the reliability and efficiency of the heuristic, making a compelling case for itsuse in solving TSP issues. The research shows that, in general, HMIH fared better than FIH in all cases studied,except for a few instances (pr439, eil51, and eil101) where FIH either performed equally or slightly better thanHMIH. HMIH’s efficiency is shown by its improvements in error percentage (δ) and goodness values (g) comparedto FIH and NNH. In the att48 instance, HMIH had an error rate of 6.3%, whereas FIH had 14.6% and NNH had20.9%, indicating that HMIH was closer to the optimal solution. HMIH consistently showed superior performanceacross many benchmarks, with lower percentage error and higher goodness values, suggesting a closer match tothe optimal tour costs. This study substantially contributes to combinatorial optimization by enhancing currentinsertion algorithms and presenting a more efficient solution for the Travelling Salesman Problem. It also createsnew possibilities for progress in heuristic design and optimization methodologies.

利益衡量视域下人工智能生成内容的邻接权保护

对人工智能生成内容保护与否、如何保护的争论,本质上是对既有利益如何协调的讨论。化解人工智能生成内容带来的利益冲突,需从人工智能运行的底层逻辑出发,以利益分析为主线,审视著作权法保护的条件。人工智能生成内容符合广义邻接权的保护要求。但现有邻接权制度无法对人工智能生成内容提供保护,邻接权制度需进行一定的扩张,结合人工智能生成内容的特性和关联的利益,宜新设“生成式信息使用者权”,并对权利主体、具体保护条件、权利行使限制等方面进行明确。

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

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.

A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

基于不规则区域划分方法的k-Nearest Neighbor查询算法

随着越来越多的数据累积,对数据处理能力和分析能力的要求也越来越高.传统k-Nearest Neighbor(k NN)查询算法由于其容易导致计算负载整体不均衡的规则区域划分方法及其单个进程或单台计算机运行环境的较低数据处理能力.本文提出并详细介绍了一种基于不规则区域划分方法的改进型k NN查询算法,并利用对大规模数据集进行分布式并行计算的模型Map Reduce对该算法加以实现.实验结果与分析表明,Map Reduce框架下基于不规则区域划分方法的k NN查询算法可以获得较高的数据处理效率,并可以较好的支持大数据环境下数据的高效查询.

Entanglement and quantum phase transition in alternating XY spin chain with next-nearest neighbouring interactions

By using the method of density-matrix renormalization-group to solve the different spin spin correlation functions, the nearest-neighbouring entanglement （NNE） and the next-nearest-neighbouring entanglement （NNNE） of one-dimensional alternating Heisenberg XY spin chain are investigated in the presence of alternating the-nearestneighbouring interaction of exchange couplings, external magnetic fields and the next-nearest neighbouring interaction. For a dimerised ferromagnetic spin chain, the NNNE appears only above a critical dimerized interaction, meanwhile, the dimerized interaction a effects a quantum phase transition point and improves the NNNE to a large extent. We also study the effect of ferromagnetic or antiferromagnetic next-nearest neighbouring （NNN） interaction on the dynamics of NNE and NNNE. The ferromagnetic NNN interaction increases and shrinks the NNE below and above a critical frustrated interaction respectively, while the antiferromagnetic NNN interaction always reduces the NNE. The antiferromagnetic NNN interaction results in a large value of NNNE compared with the case where the NNN interaction is ferromagnetic.