A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification

Due to the advancements in information technologies,massive quantity of data is being produced by social media,smartphones,and sensor devices.The investigation of data stream by the use of machine learning(ML)approaches to address regression,prediction,and classification problems have received consid-erable interest.At the same time,the detection of anomalies or outliers and feature selection(FS)processes becomes important.This study develops an outlier detec-tion with feature selection technique for streaming data classification,named ODFST-SDC technique.Initially,streaming data is pre-processed in two ways namely categorical encoding and null value removal.In addition,Local Correla-tion Integral(LOCI)is used which is significant in the detection and removal of outliers.Besides,red deer algorithm(RDA)based FS approach is employed to derive an optimal subset of features.Finally,kernel extreme learning machine(KELM)classifier is used for streaming data classification.The design of LOCI based outlier detection and RDA based FS shows the novelty of the work.In order to assess the classification outcomes of the ODFST-SDC technique,a series of simulations were performed using three benchmark datasets.The experimental results reported the promising outcomes of the ODFST-SDC technique over the recent approaches.

A Novel Handcrafted with Deep Features Based Brain Tumor Diagnosis Model

In healthcare sector,image classification is one of the crucial problems that impact the quality output from image processing domain.The purpose of image classification is to categorize different healthcare images under various class labels which in turn helps in the detection and management of diseases.Magnetic Resonance Imaging(MRI)is one of the effective non-invasive strate-gies that generate a huge and distinct number of tissue contrasts in every imaging modality.This technique is commonly utilized by healthcare professionals for Brain Tumor(BT)diagnosis.With recent advancements in Machine Learning(ML)and Deep Learning(DL)models,it is possible to detect the tumor from images automatically,using a computer-aided design.The current study focuses on the design of automated Deep Learning-based BT Detection and Classification model using MRI images(DLBTDC-MRI).The proposed DLBTDC-MRI techni-que aims at detecting and classifying different stages of BT.The proposed DLBTDC-MRI technique involves medianfiltering technique to remove the noise and enhance the quality of MRI images.Besides,morphological operations-based image segmentation approach is also applied to determine the BT-affected regions in brain MRI image.Moreover,a fusion of handcrafted deep features using VGGNet is utilized to derive a valuable set of feature vectors.Finally,Artificial Fish Swarm Optimization(AFSO)with Artificial Neural Network(ANN)model is utilized as a classifier to decide the presence of BT.In order to assess the enhanced BT classification performance of the proposed model,a comprehensive set of simulations was performed on benchmark dataset and the results were vali-dated under several measures.

User Behaviors Analysis in Website Identification Registration

Nowadays, an increasing number of web applications require identification registration. However, the behavior of website registration has not ever been thoroughly studied. We use the database provided by the Chinese Software Develop Net (CSDN) to provide a complete perspective on this research point. We concentrate on the following three aspects: complexity, correlation, and preference. From these analyses, we draw the following conclusions: firstly, a considerable number of users have not realized the importance of identification and are using very simple identifications that can be attacked very easily. Secondly, there is a strong complexity correlation among the three parts of identification. Thirdly, the top three passwords that users like are 123456789, 12345678 and 11111111, and the top three email providers that they prefer are NETEASE, qq and sina. Further, we provide some suggestions to improve the quality of user passwords.

Optimal Deep Belief Network Enabled Malware Detection and Classification Model

Cybercrime has increased considerably in recent times by creating new methods of stealing,changing,and destroying data in daily lives.Portable Docu-ment Format(PDF)has been traditionally utilized as a popular way of spreading malware.The recent advances of machine learning(ML)and deep learning(DL)models are utilized to detect and classify malware.With this motivation,this study focuses on the design of mayfly optimization with a deep belief network for PDF malware detection and classification(MFODBN-MDC)technique.The major intention of the MFODBN-MDC technique is for identifying and classify-ing the presence of malware exist in the PDFs.The proposed MFODBN-MDC method derives a new MFO algorithm for the optimal selection of feature subsets.In addition,Adamax optimizer with the DBN model is used for PDF malware detection and classification.The design of the MFO algorithm to select features and Adamax based hyperparameter tuning for PDF malware detection and classi-fication demonstrates the novelty of the work.For demonstrating the improved outcomes of the MFODBN-MDC model,a wide range of simulations are exe-cuted,and the results are assessed in various aspects.The comparison study high-lighted the enhanced outcomes of the MFODBN-MDC model over the existing techniques with maximum precision,recall,and F1 score of 97.42%,97.33%,and 97.33%,respectively.

Deep LearningModel for Big Data Classification in Apache Spark Environment

Big data analytics is a popular research topic due to its applicability in various real time applications.The recent advent of machine learning and deep learning models can be applied to analyze big data with better performance.Since big data involves numerous features and necessitates high computational time,feature selection methodologies using metaheuristic optimization algorithms can be adopted to choose optimum set of features and thereby improves the overall classification performance.This study proposes a new sigmoid butterfly optimization method with an optimum gated recurrent unit(SBOA-OGRU)model for big data classification in Apache Spark.The SBOA-OGRU technique involves the design of SBOA based feature selection technique to choose an optimum subset of features.In addition,OGRU based classification model is employed to classify the big data into appropriate classes.Besides,the hyperparameter tuning of the GRU model takes place using Adam optimizer.Furthermore,the Apache Spark platform is applied for processing big data in an effective way.In order to ensure the betterment of the SBOA-OGRU technique,a wide range of experiments were performed and the experimental results highlighted the supremacy of the SBOA-OGRU technique.

A Secure IoT-Cloud Based Healthcare System for Disease Classification Using Neural Network

The integration of the Internet of Things(IoT)and cloud computing is the most popular growing technology in the IT world.IoT integrated cloud com-puting technology can be used in smart cities,health care,smart homes,environ-mental monitoring,etc.In recent days,IoT integrated cloud can be used in the health care system for remote patient care,emergency care,disease prediction,pharmacy management,etc.but,still,security of patient data and disease predic-tion accuracy is a major concern.Numerous machine learning approaches were used for effective early disease prediction.However,machine learning takes more time and less performance while classification.In this research work,the Attribute based Searchable Honey Encryption with Functional Neural Network(ABSHE-FNN)framework is proposed to analyze the disease and provide stronger security in IoT-cloud healthcare data.In this work,the Cardiovascular Disease and Pima Indians diabetes dataset are used for heart and diabetic disease classification.Initi-ally,means-mode normalization removes the noise and normalizes the IoT data,which helps to enhance the quality of data.Rectified Linear Unit(RLU)was applied to adjust the feature weight to reduce the training cost and error classifi-cation.This proposed ABSHE-FNN technique provides better security and achieves 92.79%disease classification accuracy compared to existing techniques.

Identifying Cancer Disease Using Softmax-Feed Forward Recurrent Neural Classification

In today’s growing modern world environment,as human food activities are changing,it is affecting human health,thus leading to diseases like cancer.Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death.So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observa-tion,which has become necessary to classify the type in cancer research.The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment.This paper introduces a Maximal Region-Based Candidate Feature Selection(MRCFS)for early risk diagnosing using Soft-Max Feed Forward Neural Classification(SMF2NC)to solve the above pro-blem.The predictive model is based on a different relational feature learning model,which is possessed to candidate selection to reduce the dimensionality.The redundant features are processed marginal weight rates for observing similar features’variants and the absolute value.Softmax neural hidden layers are trained using the Sigmoid Activation Function(SAF)to create the logical condition for feed-forward layers.Further,the maximal features are introduced to invite a deep neural network con-structed on the Feed Forward Recurrent Neural Network(FFRNN).The classifier produces higher classification accuracy than the previous methods and observes the cancer detection,which is recommended for early diagnosis.

Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme

Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach.This paper presented the text document classification that has wide applications in information retrieval,which uses movie review datasets.Here the document indexing based on controlled vocabulary,adjective,word sense disambiguation,generating hierarchical cate-gorization of web pages,spam detection,topic labeling,web search,document summarization,etc.Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search opti-mization.Positive review and negative review of movie dataset is presented to get the better classification accuracy.Experimental results focused with context mining,feature analysis and classification.By comparing with the previous work,proposed work designed to achieve the efficient results.Overall design is per-formed with MATLAB 2020a tool.

笔迹鉴定的量化及统计学应用方法综述

统计学方法在笔迹鉴定领域的研究中越来越受到重视,采取有效的统计学方法对笔迹的特征进行客观量化,并对特征数据进行合理的分析,不仅可以为笔迹鉴定结论提供强有力的理论依据,更可以深入挖掘复杂数据背后的信息。本文梳理了笔迹特征量化和特征数据处理中几种常用统计学方法,介绍了它们的原理、应用以及最新进展,并对笔迹鉴定中统计学方法进行了展望。

面向薄壁多腔类结构件加工特征识别方法

针对薄壁多腔类结构件加工特征识别效率低下、相交特征识别困难的问题,提出基于规则、层与特征抑制的混合并行识别方法。该方法以腔分组和加工特征分类为基础、以腔为单元对各类加工特征进行并行识别,以提高识别效率,并及时在三维模型中抑制已识别的各特征来简化相交特征的识别。首先给出了加工特征的分类及腔分组的方法;然后详细给出了特征识别流程:利用边的属性和规则识别各个腔中的完整孔、独立筋、相交筋,将各个腔采用层与特征抑制的方法识别层特征与相交孔特征,当所有腔内的特征识别完成后回到主线程进行剩余特征的识别;最后通过实例分析了算法效率,验证了所提方法的有效性。

基于实验分析的泥质砂岩T_2截止值确定方法研究

核磁共振T2截止值(T2,cutoff)的确定主要通过离心法和经验值法得到,在实际应用中具有极大的局限性。根据泥质砂岩的渗流特性和Coates-cutoff模型建立了泥质砂岩的T2,cutoff计算模型。核磁共振T2,cutoff与岩石的孔隙结构、渗流特征有关;泥质砂岩的T2,cutoff分布范围较大。通过对渗透率模型的对比分析,得到了T2,cutoff与阳离子交换容量QV的理论模型;泥质砂岩的T2,cutoff随着QV的增大而减小,但其减小幅度随着QV的增大而不断减小。研究表明,岩石的核磁共振T2,cutoff与阳离子交换容量QV呈幂函数的关系;可通过该模型进行T2,cutoff的精确计算,为T2,cutoff的计算方法提供了一个新的思路。在实际应用中需要考虑阳离子交换量如何准确计算的问题。

一种用于农作物叶部病害图像识别的双权重协同表示分类方法

农作物病害是我国主要的农业灾害之一,准确识别病害类型是防治农作物病害的关键。因此,首先采集了小麦、玉米、花生、棉花4种农作物的22种常见叶部病害的441张图像;然后,在对每张病害图像中的叶片和病斑进行分割的基础上,分别提取了描述农作物种类的叶片特征参数和描述病害类型的病斑特征参数;其次,将这两类特征参数组合并作归一化处理,得到病害图像的数据特征向量;再次,采用所有病害图像的数据特征向量,构建了一个农作物叶部病害数据集;最后,在同时考虑数据特征重要性和数据空间局部性的基础上,提出了一种双权重协同表示分类(DWCRC)方法并将其用于农作物叶部病害识别。在农作物叶部病害数据集上的实验结果表明,提出的双权重协同表示分类方法在用于农作物叶部病害识别时具有较高的识别率。

抗肿瘤阳离子肽:特征、作用机制及展望

恶性肿瘤(癌症)是尚未被攻克的重大疾病之一,主要原因之一是临床上抗肿瘤药物效用不大.具有抗肿瘤活性的阳离子肽(CAP)主要结合肿瘤细胞膜并破坏膜结构,这是通过肽的正电荷与肿瘤细胞膜的负电荷之静电吸引来实现的.与传统化疗药物相比,绕过肿瘤细胞的耐药机制是CAP最突出的优势.本文对抗肿瘤肽的主要结构特征、抗肿瘤效果和抗肿瘤机制(以鲎素为例)进行了综述.作者认为,寻找靶向肿瘤干细胞的CAP对于治愈肿瘤具有特别重要的意义.另外,通过化学改构和新剂型研制,CAP将具有高靶向性和低毒性,给抗肿瘤药物开发提供新的动力.

合成孔径雷达图像特征关键度分析与分类算法研究

针对合成孔径雷达(SAR)图像目标分类问题,从灰度和纹理特征分析出发,提出了一种SAR图像分类特征量的关键性度量指标。利用关键系数的概念定义了关键特征量、次关键特征量、非关键特征量和关键度。从灰度模型和纹理模型的特征统计量中选择关键度高的特征量,如灰度模型中的均值和方差、纹理模型中的角二阶矩、对比度、均匀性和相关性。针对SAR图像分类往往是多类别、多特征的情况,通过构造特征向量,定义向量距离,按照最小距离方法进行目标分类。为了提高计算速度和更好地描述特征量,引入了窗口方法。仿真和计算结果表明该方法行之有效。

基于残差卷积神经网络的温度敏感负荷辨识方法研究

地区电网负荷特性易受环境温度影响,导致负荷辨识结果往往存在较大偏差,研究了基于残差卷积神经网络的温度敏感负荷辨识方法,有效提高负荷辨识准确率。首先,利用基准负荷比较法,构建了商业各企业基准日负荷曲线;其次,利用皮尔逊相关系数法,筛选出与温度相关性强的温度敏感负荷,同时采用多项式回归模型进一步分析温度敏感负荷与实时温度变化的规律,量化温度因素的影响程度;最后,针对温度敏感负荷,提出利用负荷与温度的多项式回归模型系数构建动态温度敏感负荷特征库,作为辨识模型的输入。将基于残差卷积神经网络的负荷辨识结果与传统卷积神经网络负荷辨识结果进行对比,前者的辨识准确率有较大提升。

闽东古田县杉洋话的人称代词——兼论其历时涵义

杉洋镇地处古田县东部,邻接宁德,其语音系统和词汇系统与以古田县城关口音为代表的古田话有明显的区别。文章以古田县城关话和宁德虎浿话人称代词为对比,全面梳理杉洋话的人称代词系统,分析其语用特点,并讨论了人称代词系统层次叠置的历时涵义。

鹅绒藤的鉴定学研究

本文首次报道了鹅绒藤（ＣｙｎａｎｃｈｕｍｃｈｉｎｅｎｓｅＲ．Ｂｒ．）的原植物形态、生药性状、根和茎的显微结构、花粉块的形态、有关化学成分的理化鉴别，为该药的鉴定、用药安全、寻找和扩大新药源及其开发利用提供了依据。

互信息特征选择法在《中图法》内容相似类目中的运用及改进——以E271和E712.51为例

针对内容相似的两个类目间存在大量共同特征而难以自动区分的特点,提出一种改进的互信息特征选择法,以提高两类文本自动分类的效果。以《中国图书馆分类法》中E271(中国陆军)和E712.51(美国陆军)两个类别的书目信息作为文本分类的对象,首先针对传统互信息特征选择法未考虑负相关特征、类间集中度和类内分散度等问题,引入改进的互信息特征选择法DNCF_MI;其次,针对DNCF_MI未区分不同特征对类别的贡献程度等不足,引入领域无关特征和领域相关特征,提出一种改进的互信息特征选择法DNCF_DI_MI;最后,使用knn分类器进行分类,并采用宏平均F1值和微平均F1值对分类结果进行评价。实验结果表明,本文提出方法的宏平均F1值和微平均F1值比传统互信息特征选择法分别提升24.1%和28.5%,比DNCF_MI均提升4.5%,证明本文方法对内容相似类目的分类更有效。

自身阳离子型松香施胶剂及其施胶性能

以两条路线分别合成了自身阳离子型的N - (3-松香酰氧 - 2 -羟 )丙基 -N ,N ,N -三甲基氯化铵和N - (3-松香酰氧 - 2 -羟 )丙基 -N ,N ,N -三乙基氯化铵 ,并对其性能进行了简单分析和评价。结果表明 ,虽然这两种松香季铵盐具有较高的Zeta电位 ,能够不依赖于矾土的“架桥”作用而吸附于纸张的纤维表面 ,但是由于在合成中向松香分子上引入了较强的亲水基团 ,使其疏水性能下降 ,成纸施胶度较低 ,且耐老化性能略有下降 ,加上合成过程使成本增加 ,松香季铵盐不适于作施胶剂产品主要成分。

乳化剂对阳离子乳液聚合及乳胶粒性能的影响

以苯乙烯、丙烯酸丁酯为非离子单体,甲基丙烯酰氧乙基三甲基氯化铵(DMC)为阳离子单体,偶氮二异丁基脒盐酸盐(AIBA)为引发剂,十六烷基三甲基氯化铵(CTAC)和乙撑基-双(十六烷基二甲基氯化铵)(G16-2-16)为乳化剂,采用半连续种子乳液聚合法进行阳离子乳液聚合。探讨了乳化剂的分子结构和用量对反应速率、单体转化率以及乳胶粒粒径、Zeta电位等的影响。结果表明:乳化剂的用量越大,反应速率越大,单体转化率越高,而乳胶粒粒径越小;使用G16-2-16作乳化剂时,单体转化率较高,乳胶粒粒径较大,Zeta电位较高。