New Spam Filtering Method with Hadoop Tuning-Based MapReduce Naïve Bayes

As the importance of email increases,the amount of malicious email is also increasing,so the need for malicious email filtering is growing.Since it is more economical to combine commodity hardware consisting of a medium server or PC with a virtual environment to use as a single server resource and filter malicious email using machine learning techniques,we used a Hadoop MapReduce framework and Naïve Bayes among machine learning methods for malicious email filtering.Naïve Bayes was selected because it is one of the top machine learning methods(Support Vector Machine(SVM),Naïve Bayes,K-Nearest Neighbor(KNN),and Decision Tree)in terms of execution time and accuracy.Malicious email was filtered with MapReduce programming using the Naïve Bayes technique,which is a supervised machine learning method,in a Hadoop framework with optimized performance and also with the Python program technique with the Naïve Bayes technique applied in a bare metal server environment with the Hadoop environment not applied.According to the results of a comparison of the accuracy and predictive error rates of the two methods,the Hadoop MapReduce Naïve Bayes method improved the accuracy of spam and ham email identification 1.11 times and the prediction error rate 14.13 times compared to the non-Hadoop Python Naïve Bayes method.

Nave Bayes分类器制导的专业网页爬取算法

从Web中快速、准确地检索出所需信息的迫切需求催生了专业搜索引擎技术。在专业搜索引擎中,网络爬虫(Crawler)负责在Web上搜集特定专业领域的信息,是专业搜索引擎的重要核心部件。该文对中文专业网页的爬取问题进行了研究,基于KL距离验证了网页内容与链接前后文在分布上的差异,在此基础上提出了以链接锚文本及其前后文为特征、Nave Bayes分类器制导的中文专业网页爬取算法,设计了自动获取带链接类标的训练数据的算法。以金融专业网页的爬取为例,分别对所提出的算法进行了离线和在线测试,结果表明,Nave Bayes分类器制导的网络爬虫可以达到近90%的专业网页收割率。

结合特征和非特征信息改进Nave Bayes及其应用

朴素贝叶斯算法是一种常见的基于内容的垃圾邮件过滤算法,但是,传统朴素贝叶斯过滤存在判断内容的不确定性和邮件表示不完整性等问题。分析邮件信头各域在正常邮件和垃圾邮件中表现出的不同属性,提取非特征信息,结合特征信息和非特征信息改进朴素贝叶斯算法。实验结果表明,改进的朴素贝叶斯分类方法与单纯使用特征信息的方法相比,垃圾邮件的召回率和准确率更高,凸显了该方法涵盖邮件信息、克服内容判断缺陷的优势。

基于改进的Nave Bayes和BP神经网络的垃圾邮件过滤

不同用户对垃圾邮件的判定有所差别,考虑到同一用户的自认垃圾邮件相似度较大,提出对特定用户进行针对性的垃圾邮件过滤方法.系统除重点利用邮件正文信息外,还尝试加入发件人、群发信息和主题相关度信息,改进朴素贝叶斯公式用于邮件正文的概率计算,基于BP神经网络构造垃圾邮件判别系统.实验表明,改进的朴素贝叶斯公式用于本文的系统是可行的,基于BP神经网络的垃圾邮件过滤系统能有效综合以上四项数值进行全局判别,进而对特定用户的邮件产生不错的过滤效果.

黑色素瘤B16细胞通过释放外泌体诱导naïve CD4^(+) T细胞分化为Treg

目的检测小鼠黑色素瘤细胞B16通过释放外泌体诱导naïve CD4^(+)T细胞分化为调节性T细胞(regulatory T cell,Treg)的能力。方法通过试剂盒和超速离心获得B16培养上清中的外泌体,用透射电镜观察外泌体形态,Western blot检测外泌体标记性蛋白的表达水平,通过Transwell共培养验证外泌体或B16细胞对naïve CD4^(+)T细胞诱导分化的能力,流式细胞术检测小鼠脾脏、肿瘤内Th17、Treg细胞的频率,qPCR检测T细胞亚群特异性转录因子、炎症因子的mRNA水平。结果B16细胞外泌体的直径为30~100 nm,呈圆形碟状结构,外泌体高表达Tsg101、HSP60和Alix,低表达GAPDH;在体外,B16细胞外泌体可直接诱导naïve CD4^(+)T细胞分化成Treg,但不能诱导其分化为Th17;小鼠在体实验证实,B16细胞外泌体能够诱导Treg细胞并聚集于小鼠脾脏中,同时能够增加B16瘤体内Treg细胞的数量,并活化使其表达更高水平的IL-10、TGF-β和Foxp3。结论B16细胞外泌体通过诱导naïve CD4^(+)T细胞分化为Treg,并将其募集至肿瘤组织,从而维持肿瘤的免疫抑制微环境。

U87MG培养上清致Na觙ve CD4分化为Th2细胞的能力降低

目的肿瘤微环境或者肿瘤细胞本身对免疫细胞的分化具有定向诱导作用,本研究目的在于了解胶质瘤细胞株U87MG外泌分子对Na觙ve CD4细胞分化可能的影响。方法外周血PBMC中分离Na觙ve CD4细胞,U87MG培养液上清以1/5量加至Na觙veCD4细胞培养体系中,培养7 d,检测Na觙ve CD4细胞分化增殖情况以及分化为Th1、Th2、Th17、Treg四个亚群量的变化。结果加有1/5量U87MG培养上清的Na觙ve CD4细胞分化成Th2减低,较对照组有显著性差异8.17%±2.08%vs 9.63%±2.48%(P<0.05),Th1较对照组均值低,但无显著性差异,Th17、Treg细胞在2组之间无明显差异。结论观察到U87MG外泌分子Na觙ve CD4分化为Th2细胞能力减弱,同时初步观察了Na觙ve CD4细胞分化为其他亚群的变化规律,为理解肿瘤细胞对Na觙ve CD4细胞分化影响提供了实验基础。

nave HCV RNA阳性患者中自身免疫及淋巴细胞增殖的标志物

【据《Dig Liver Dis》2016年8月报道】题：无自身免疫/淋巴细胞增殖异常临床表现的nave HCV RNA阳性患者中自身免疫及淋巴细胞增殖的标志物（作者Gulli F等）HCV可导致慢性肝病和B淋巴细胞增殖异常。HCV和混合型冷球蛋白血症强相关。来自意大利天主教大学医学部的Gulli等在420例常规非肝脏专科检查者中发现50例HCV抗体阳性患者,检测这些患者的抗核抗体（ANA）、类风湿因子IgG、游离轻链（FLC）κ和FLC-λ水平以及κ/λ比值。3/50患者HCV RNA低于检测值下限,

Attribute Weighted Naïve Bayes Classifier

The naïve Bayes classifier is one of the commonly used data mining methods for classification.Despite its simplicity,naïve Bayes is effective and computationally efficient.Although the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning,this assumption may not hold in real-world applications.Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption.While these methods improve the classification performance,they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense of computational time.One approach to reduce the naïvetéof the classifier is to incorporate attribute weights in the conditional probability.In this paper,we proposed a method to incorporate attribute weights to naïve Bayes.To evaluate the performance of our method,we used the public benchmark datasets.We compared our method with the standard naïve Bayes and baseline attribute weighting methods.Experimental results show that our method to incorporate attribute weights improves the classification performance compared to both standard naïve Bayes and baseline attribute weighting methods in terms of classification accuracy and F1,especially when the independence assumption is strongly violated,which was validated using the Chi-square test of independence.

LFA-1基因缺失对小鼠Nave T细胞体外向Th17细胞分化的影响

背景:淋巴细胞功能相关抗原-1(LFA-1)参与T细胞的活化和功能调节,与炎症性肠病的发病密切相关。目的:观察LFA-1基因缺失(LFA-1-/-)对小鼠Nave T细胞体外向Th17细胞分化的影响。方法:繁殖LFA-1-/-子代小鼠,提取鼠尾DNA,PCR法鉴定基因型。LFA-1-/-子代小鼠为实验组,野生型(WT)C57BL/6J小鼠为对照组,磁珠分选脾脏单个核细胞中的CD4+CD62L+Nave T细胞并检测其纯度。体外建立不同Th17细胞诱导分化体系[转化生长因子-β(TGF-β)、TGF-β+白细胞介素-6(IL-6)和TGF-β+IL-6+IL-23],以流式细胞术检测两组分选得到的Nave T细胞在不同体系中诱导出的Th17细胞比率,荧光定量PCR法和ELISA法检测Th17细胞特异性转录因子ROR-γt和特异性标记物IL-17A表达。结果:15只子代小鼠均为LFA-1-/-小鼠,磁珠分选得到的CD4+CD62L+Nave T细胞纯度大于95%。低剂量TGF-β+IL-6即能诱导出Th17细胞,在此基础上加入IL-23能促进更多Th17细胞产生。与WT对照组相比,LFA-1-/-组Nave T细胞在TGF-β+IL-6+IL-23体系中诱导产生Th17细胞的效应更为明显(17.2%±1.4%对5.7%±0.2%,P<0.001),ROR-γt、IL-17A mRNA表达上调(P<0.001),细胞培养上清液中IL-17A浓度升高(P<0.01)。结论:LFA-1基因缺失能促进小鼠Nave T细胞体外向Th17细胞分化。

Improved Bearing Fault Diagnosis by Feature Extraction Based on GLCM, Fusion of Selection Methods, and Multiclass-Naïve Bayes Classification

The presence of bearing faults reduces the efficiency of rotating machines and thus increases energy consumption or even the total stoppage of the machine. It becomes essential to correctly diagnose the fault caused by the bearing. Hence the importance of determining an effective features extraction method that best describes the fault. The vision of this paper is to merge the features selection methods in order to define the most relevant featuresin the texture of the vibration signal images. In this study, the Gray Level Co-occurrence Matrix (GLCM) in texture analysis is applied on the vibration signal represented in images. Features selection based on the merge of PCA (Principal component Analysis) method and SFE (Sequential Features Extraction) method is done to obtain the most relevant features. The multiclass-Na?ve Bayesclassifier is used to test the proposed approach. The success rate of this classification is 98.27%. The relevant features obtained give promising results and are more efficient than the methods observed in the literature.

Immune phenotype in children with therapy-nave remitted and relapsed Crohn’s disease

AIM: To characterize the prevalence of subpopulations of CD4+ cells along with that of major inhibitor or stimulator cell types in therapy-nave childhood Crohn's disease (CD) and to test whether abnormalities of immune phenotype are normalized with the improvement of clinical signs and symptoms of disease. METHODS: We enrolled 26 pediatric patients with CD. 14 therapy-nave CD children; of those, 10 children remitted on conventional therapy and formed the remission group. We also tested another group of 12 chil-dren who relapsed with conventional therapy and were given infliximab; and 15 healthy children who served as controls. The prevalence of Th1 and Th2, nave and memory, activated and regulatory T cells, along with the members of innate immunity such as natural killer (NK), NK-T, myeloid and plasmocytoid dendritic cells (DCs), monocytes and Toll-like receptor (TLR)-2 and TLR-4 expression were determined in peripheral blood samples. RESULTS: Children with therapy-nave CD and those in relapse showed a decrease in Th1 cell prevalence. Simultaneously, an increased prevalence of memory and activated lymphocytes along with that of DCs and monocytes was observed. In addition, the ratio of myeloid /plasmocytoid DCs and the prevalence of TLR-2 or TLR-4 positive DCs and monocytes were also higher in therapy-nave CD than in controls. The majority of alterations diminished in remitted CD irrespective of whether remission was obtained by conventional or biological therapy. CONCLUSION: The finding that immune phenotype is normalized in remission suggests a link between immune phenotype and disease activity in childhood CD. Our observations support the involvement of members of the adaptive and innate immune systems in childhood CD.

Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection

Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity.Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability.Nonetheless,it is Naïve use of the mean data value for the cluster core that presents a major drawback.The chances of two circular clusters having different radius and centering at the same mean will occur.This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together.However,if the clusters are not spherical,it fails.To overcome this issue,a new integrated hybrid model by integrating expectation maximizing(EM)clustering using a Gaussian mixture model(GMM)and naïve Bays classifier have been proposed.In this model,GMM give more flexibility than K-Means in terms of cluster covariance.Also,they use probabilities function and soft clustering,that’s why they can have multiple cluster for a single data.In GMM,we can define the cluster form in GMM by two parameters:the mean and the standard deviation.This means that by using these two parameters,the cluster can take any kind of elliptical shape.EM-GMM will be used to cluster data based on data activity into the corresponding category.

Naïve Bayes Algorithm for Large Scale Text Classification

This paper proposed an improved Naïve Bayes Classifier for sentimental analysis from a large-scale dataset such as in YouTube.YouTube contains large unstructured and unorganized comments and reactions,which carry important information.Organizing large amounts of data and extracting useful information is a challenging task.The extracted information can be considered as new knowledge and can be used for deci sion-making.We extract comments from YouTube on videos and categorized them in domain-specific,and then apply the Naïve Bayes classifier with improved techniques.Our method provided a decent 80%accuracy in classifying those comments.This experiment shows that the proposed method provides excellent adaptability for large-scale text classification.

Cyberbullying Detection and Recognition with Type Determination Based on Machine Learning

Social media networks are becoming essential to our daily activities,and many issues are due to this great involvement in our lives.Cyberbullying is a social media network issue,a global crisis affecting the victims and society as a whole.It results from a misunderstanding regarding freedom of speech.In this work,we proposed a methodology for detecting such behaviors(bullying,harassment,and hate-related texts)using supervised machine learning algo-rithms(SVM,Naïve Bayes,Logistic regression,and random forest)and for predicting a topic associated with these text data using unsupervised natural language processing,such as latent Dirichlet allocation.In addition,we used accuracy,precision,recall,and F1 score to assess prior classifiers.Results show that the use of logistic regression,support vector machine,random forest model,and Naïve Bayes has 95%,94.97%,94.66%,and 93.1%accuracy,respectively.

Multitude Classifier Using Rough Set Jelinek Mercer Naïve Bayes for Disease Diagnosis

Classification model has received great attention in any domain of research and also a reliable tool for medical disease diagnosis. The domain of classification model is used in disease diagnosis, disease prediction, bio informatics, crime prediction and so on. However, an efficient disease diagnosis model was compromised the disease prediction. In this paper, a Rough Set Rule-based Multitude Classifier (RS-RMC) is developed to improve the disease prediction rate and enhance the class accuracy of disease being diagnosed. The RS-RMC involves two steps. Initially, a Rough Set model is used for Feature Selection aiming at minimizing the execution time for obtaining the disease feature set. A Multitude Classifier model is presented in second step for detection of heart disease and for efficient classification. The Na?ve Bayes Classifier algorithm is designed for efficient identification of classes to measure the relationship between disease features and improving disease prediction rate. Experimental analysis shows that RS-RMC is used to reduce the execution time for extracting the disease feature with minimum false positive rate compared to the state-of-the-art works.

Perspicacious Apprehension of HDTbNB Algorithm Opposed to Security Contravention

The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of informationflow-ing over the network.The data will always remain under the threat of technolo-gical suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights.In this paper,the authors pro-posed the HDTbNB(Hybrid Decision Tree-based Naïve Bayes)algorithm tofind the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of IDS(Intrusion Detection System)and to further analyze the performance execution of distinct machine learning algorithms as Naïve Bayes,Decision Tree,K-Nearest Neighbors and Logistic Regression over KDD 99 data-set.The performance of algorithm is evaluated by making a comparative analysis of computed parameters as accuracy,macro average,and weighted average.Thefindings were concluded as a percentage increase in accuracy,precision,sensitiv-ity,specificity,and a decrease in misclassification as 9.3%,6.4%,12.5%,5.2%and 81%.

Lung Cancer Prediction from Elvira Biomedical Dataset Using Ensemble Classifier with Principal Component Analysis

Machine learning algorithms (MLs) can potentially improve disease diagnostics, leading to early detection and treatment of these diseases. As a malignant tumor whose primary focus is located in the bronchial mucosal epithelium, lung cancer has the highest mortality and morbidity among cancer types, threatening health and life of patients suffering from the disease. Machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes (NB) have been used for lung cancer prediction. However they still face challenges such as high dimensionality of the feature space, over-fitting, high computational complexity, noise and missing data, low accuracies, low precision and high error rates. Ensemble learning, which combines classifiers, may be helpful to boost prediction on new data. However, current ensemble ML techniques rarely consider comprehensive evaluation metrics to evaluate the performance of individual classifiers. The main purpose of this study was to develop an ensemble classifier that improves lung cancer prediction. An ensemble machine learning algorithm is developed based on RF, SVM, NB, and KNN. Feature selection is done based on Principal Component Analysis (PCA) and Analysis of Variance (ANOVA). This algorithm is then executed on lung cancer data and evaluated using execution time, true positives (TP), true negatives (TN), false positives (FP), false negatives (FN), false positive rate (FPR), recall (R), precision (P) and F-measure (FM). Experimental results show that the proposed ensemble classifier has the best classification of 0.9825% with the lowest error rate of 0.0193. This is followed by SVM in which the probability of having the best classification is 0.9652% at an error rate of 0.0206. On the other hand, NB had the worst performance of 0.8475% classification at 0.0738 error rate.

哇巴因抑制钠泵β1亚单位和VE-cadherin减弱血管内皮细胞连接功能

目的探讨钠泵抑制剂哇巴因对血管内皮细胞连接的影响及机制。方法以人脐静脉内皮细胞(HUVECs)为靶细胞,Hoechst33342/PI双荧光染色法观察哇巴因作用后细胞凋亡或坏死特征,透射电镜和光镜观察细胞形态结构变化。应用半定量聚合酶链反应检测钠泵α1亚单位、β1亚单位、VE-cadherin和SnailmRNA的表达。结果0.1μmol.L-1哇巴因作用HUVECs24～48h,细胞死亡以凋亡为主,10μmol·L-1哇巴因作用24h,引起细胞坏死;对照组细胞间的细胞连接数量多,结构清晰,而经哇巴因作用后,细胞连接丧失,细胞脱落。哇巴因作用HUVECs后,钠泵α1亚单位和Snail表达上调,β1亚单位和VE-cadherin表达下降,其改变均呈剂量和时间依赖性。结论哇巴因通过下调血管内皮细胞钠泵β1亚单位和VE-cadherin的表达使细胞连接功能减弱。

ADM对LNNA诱导的高血压心肌肥大的作用及机制探讨

本工作在ＬＮＮＡ经慢性ＮＯ封闭诱导的大鼠高血压心肌肥大模型上研究了ＡＤＭ对其心肌肥大和Ｍ５ＬＶ钠一钙交换功能损伤的作用。结果：一、较之对照组，ＬＮＮＡ组ＭＡＯ和ＬＶＩ分别增加５７．４％和１８。０％（Ｐ＜０．０１）．其ＭＳＬＶ经钠－钙交换蛋白的钙摄取则明显降低（Ｐ＜０．０１或０．０５）：二．ＡＤＭ组的ＭＡＰ和ＬＶＩ则分别较ＬＮＮＡ组降低１９．６％和１１．３％，而其ＭＳＬＶ的钙摄取则高于ＬＮＮＡ组（Ｐ＜０．０５）；三、对照组、ＬＮＮＡ组和ＡＤＭ组钠－钙交换蛋白摄取钙的Ｋｍ值分别为７．５１．７．０４和７．５７ｕＭ，而Ｖｍａｘ则分别为６．３８，４．３２和５．４５ｎｍｏｌ／。ｍｇｐｒ／ｍｉｎ。表明：ＡＤＭ能拮抗ＬＮＮＡ诱导的高血压心肌肥大和减轻其ＭＳｌｌＶ钠－钙交换功能的损伤，并且后者可能是ＡＤＭ拮抗心肌肥大的机制之一。揭示内源性ＡＤＭ的诱导和外源性给予ＡＤＭ可能有预防和／或延缓高血压心肌肥大发生发展的作用。

Deep Learning-Based Classification of Rotten Fruits and Identification of Shelf Life

The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers.The impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the consumers.The overall purpose of the study is to classify rotten fruits,which can affect the taste,texture,and appearance of other fresh fruits,thereby reducing their shelf life.The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life.Hence,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten fruits.The proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and guavas.Similarly,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf life.The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model.In comparison to other pre-trained models,the visual geometry group16(VGG16)obtained a higher accuracy score of 95%.Likewise,the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf life.By developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated produce.Thereby,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.