An Improved Bayesian with Application to Anti-Spam Email

Along with the wide application of e-mail nowadays, many spam e-mails flood into people’s email-boxes and cause catastrophes to their study and life. In anti-spam e-mails campaign, we depend on not only legal measures but also technological approaches. The Bayesian classifier provides a simple and effective approach to discriminate classification. This paper presents a new improved Bayesian-based anti-spam e-mail filter. We adopt a way of attribute selection based on word entropy, use vector weights which are represented by word frequency, and deduce its corresponding formula. It is proved that our filter improves total performances apparently in our experiment.

HIP基本交换机制的研究与应用

针对基本交换的安全性和时间开销的正反两个方面,论述了密题计算机制,利用不同的组网方式测量了基本交换的时间开销,通过计算样本均值和标准差分析了其性能的优劣,提出了HIP BE Anti-Spam模型体系结构。该结构根据HIP基本交换的特点及其内建的密题机制,结合出站和入站的双重过滤来防范和阻止垃圾邮件;利用HIP基本交换机制增大垃圾邮件发送源头的代价和成本,防御对邮件服务的DoS攻击,明确了进一步优化HIP BE Anti-Spam模型及算化的研究方向。

Intelligent approach of score-based artificial fish swarm algorithm(SAFSA)for Parkinson’s disease diagnosis

Purpose-Conventional diagnostic techniques,on the other hand,may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify,potentially resulting in misdiagnosis.Meanwhile,early nonmotor signs of Parkinson’s disease(PD)can be mild and may be due to variety of other conditions.As a result,these signs are usually ignored,making early PD diagnosis difficult.Machine learning approaches for PD classification and healthy controls or individuals with similar medical symptoms have been introduced to solve these problems and to enhance the diagnostic and assessment processes of PD(like,movement disorders or other Parkinsonian syndromes).Design/methodology/approach-Medical observations and evaluation of medical symptoms,including characterization of a wide range of motor indications,are commonly used to diagnose PD.The quantity of the data being processed has grown in the last five years;feature selection has become a prerequisite before any classification.This study introduces a feature selection method based on the score-based artificial fish swarm algorithm(SAFSA)to overcome this issue.Findings-This study adds to the accuracy of PD identification by reducing the amount of chosen vocal features while to use the most recent and largest publicly accessible database.Feature subset selection in PD detection techniques starts by eliminating features that are not relevant or redundant.According to a few objective functions,features subset chosen should provide the best performance.Research limitations/implications-In many situations,this is an Nondeterministic Polynomial Time(NPHard)issue.This method enhances the PD detection rate by selecting the most essential features from the database.To begin,the data set’s dimensionality is reduced using Singular Value Decomposition dimensionality technique.Next,Biogeography-Based Optimization(BBO)for feature selection;the weight value is a vital parameter for finding the best features in PD classification.Originality/value-PD classification is done by using ensemble learning classification approaches such as hybrid classifier of fuzzy K-nearest neighbor,kernel support vector machines,fuzzy convolutional neural network and random forest.The suggested classifiers are trained using data from UCIMLrepository,and their results are verified using leave-one-person-out cross validation.The measures employed to assess the classifier efficiency include accuracy,F-measure,Matthews correlation coefficient.

Efficient Spam Filtering System Based on Smart Cooperative Subjective and Objective Methods

Most of the spam filtering techniques are based on objective methods such as the content filtering and DNS/reverse DNS checks. Recently, some cooperative subjective spam filtering techniques are proposed. Objective methods suffer from the false positive and false negative classification. Objective methods based on the content filtering are time consuming and resource demanding. They are inaccurate and require continuous update to cope with newly invented spammer’s tricks. On the other side, the existing subjective proposals have some drawbacks like the attacks from malicious users that make them unreliable and the privacy. In this paper, we propose an efficient spam filtering system that is based on a smart cooperative subjective technique for content filtering in addition to the fastest and the most reliable non-content-based objective methods. The system combines several applications. The first is a web-based system that we have developed based on the proposed technique. A server application having extra features suitable for the enterprises and closed work groups is a second part of the system. Another part is a set of standard web services that allow any existing email server or email client to interact with the system. It allows the email servers to query the system for email filtering. They can also allow the users via the mail user agents to participate in the subjective spam filtering problem.

Spam: One Pozzled Problem for E-Commerce

E-commerce has advantages of low bargaining cost, full field service, and convenient. Its development forms and promotes the key factor of the global economic growth, and the security problem of e-commerce becomes more and more important thereupon. It is the subject of a great meaning to structure the safe, convenient e-commerce running environment. The task of anti-spam is a concrete content among them. This paper discusses the source and danger of spam, and the precautionary and radical cure measures of spam are put forwarded, and the technologies of the anti-spam are discussed especially.

CA发布两款新产品

6月20日，CA公司发布了eTrust Anti—Spam及eTrust Identity and Access Management Toolkit（eTrust IAM Toolkit)]~款产品。eTrust Anti—Spam这款操作简单的反垃圾邮件及网络欺诈侦查产品同时还包括企业级杀毒软件、反间谍软件和防火墙软件等。eTrust IAM Toolkit将帮助开发人员在其商业应用程序中打造强大、持续，并更具管理性的基于身份的安全技术。

Diffusionmodels for time-series applications: a survey

Diffusion models, a family of generative models based on deep learning, have become increasinglyprominent in cutting-edge machine learning research. With distinguished performance in generating samples thatresemble the observed data, diffusion models are widely used in image, video, and text synthesis nowadays. Inrecent years, the concept of diffusion has been extended to time-series applications, and many powerful models havebeen developed. Considering the deficiency of a methodical summary and discourse on these models, we providethis survey as an elementary resource for new researchers in this area and to provide inspiration to motivate futureresearch. For better understanding, we include an introduction about the basics of diffusion models. Except forthis, we primarily focus on diffusion-based methods for time-series forecasting, imputation, and generation, andpresent them, separately, in three individual sections. We also compare different methods for the same applicationand highlight their connections if applicable. Finally, we conclude with the common limitation of diffusion-basedmethods and highlight potential future research directions.

A Meta-Analysis of the Genome-Wide Association Studies on Two Genetically Correlated Phenotypes Suggests Four New Risk Loci for Headaches

Headache is one of the commonest complaints that doctors need to address in clinical settings.The genetic mechanisms of different types of headache are not well understood while it has been suggested that self-reported headache and self-reported migraine were genetically correlated.In this study,we performed a meta-analysis of genome-wide association studies(GWAS)on the self-reported headache phenotype from the UK Biobank and the self-reported migraine phenotype from the 23andMe using the Unified Score-based Association Test(metaUSAT)software for genetically correlated phenotypes(N=397,385).We identified 38 loci for headaches,of which 34 loci have been reported before and four loci were newly suggested.The LDL receptor related protein 1(LRP1)-Signal Transducer and Activator of Transcription 6(STAT6)-Short chain Dehydrogenase/Reductase family 9C member 7(SDR9C7)region in chromosome 12 was the most significantly associated locus with a leading p value of 1.24×10^(-62)of rs11172113.The One Cut homeobox 2(ONECUT2)gene locus in chromosome 18 was the strongest signal among the four new loci with a p value of 1.29×10^(-9)of rs673939.Our study demonstrated that the genetically correlated phenotypes of self-reported headache and self-reported migraine can be meta-analysed together in theory and in practice to boost study power to identify more variants for headaches.This study has paved way for a large GWAS meta-analysis involving cohorts of different while genetically correlated headache phenotypes.