A package-SFERCB-“Segmentation,feature extraction,reduction and classification analysis by both SVM and ANN for brain tumors”

The objective of this experimentation is to develop an interactive CAD system for assisting radiologists in multiclass brain tumor classification. The study is performed on a diversified dataset of 428 post contrast T1-weighted MR images of 55 patients and publically available dataset of 260 post contrast T1-weighted MR images of 10 patients. The first dataset includes primary brain tumors such as Astrocytoma (AS), Glioblastoma Multiforme (GBM), childhood tumor-Medulloblastoma (MED) and Meningioma (MEN), along with secondary tumor-Metastatic (MET). The second dataset consists of Astrocytoma (AS), Low Grade Glioma (LGL) and Meningioma (MEN). The tumor regions are marked by content based active contour (CBAC) model. The regions are than saved as segmented regions of interest (SROIs). 71 intensity and texture feature set is extracted from these SROIs. The features are specifically selected based on the pathological details of brain tumors provided by the radiologist. Genetic Algorithm (GA) selects the set of optimal features from this input set. Two hybrid machine learning models are implemented using GA with support vector machine (SVM) and artificial neural network (ANN) (GA-SVM and GA-ANN) and are tested on two different datasets. GA-SVM is proposed for finding preliminary probability in identifying tumor class and GA-ANN is used for confirmation of accuracy. Test results of the first dataset show that the GA optimization technique has enhanced the overall accuracy of SVM from 79.3% to 91.7% and of ANN from 75.6% to 94.9%. Individual class accuracies delivered by GA-SVM are: AS-89.8%, GBM-83.3%, MED-95.6%, MEN-91.8%, and MET-97.1%. Individual class accuracies delivered by GA-ANN classifier are: AS-96.6%, GBM-86.6%, MED-93.3%, MEN-96%, MET-100%. Similar results are obtained for the second dataset. The overall accuracy of SVM has increased from 80.8% to 89% and that of ANN has increased from 77.5% to 94.1%. Individual class accuracies delivered by GA-SVM are: AS-85.3%, LGL-88.8%, MEN-93%. Individual class accuracies delivered by GA-ANN classifier are: AS-92.6%, LGL-94.4%, MED-95.3%. It is observed from the experiments that GA-ANN classifier has provided better results than GA-SVM. Further, it is observed that along with providing finer results, GA-SVM provides advantage in speed whereas GA-ANN provides advantage in accuracy. The combined results from both the classifiers will benefit the radiologists in forming a better decision for classifying brain tumors.     

基于融合特征与GA-SVM算法的脑疾病基因预测

单一生物数据网络提供的特征信息是十分受限的,针对这一问题,提出了一种基于半监督自编码器的多网络特征融合方法,丰富特征信息。此外,为解决在人为设置模型的超参数时,易出现模型性能较低、陷入局部最优等问题,进一步提出了利用遗传算法优化支持向量机（GA-SVM算法）模型的方法,提高脑部疾病基因的预测性能。构建来自不同数据源的相似性数据网络,利用重启随机游走算法从四个数据网络中提取特征,通过半监督自编码器进行处理及融合,在十折交叉验证的策略下使用GA-SVM算法模型预测脑部疾病基因,并与其他算法进行比较。实验结果表明,在PD数据集上的AUC和AUPR值分别为0.805、0.792,而在MDD数据集上的AUC和AUPR值分别为0.825、0.823,均优于已有的预测模型,有效证明了该方法能够提高脑部疾病基因的预测效果。     

Credit scoring with a data mining approach based on support vector machines

The credit card industry has been growing rapidly recently, and thus huge numbers of consumers’ credit data are collected by the credit department of the bank. The credit scoring manager often evaluates the consumer’s credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant’s credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant’s credit score from the applicant’s input features. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the SVM classifier. Compared with neural networks, genetic programming, and decision tree classifiers, the SVM classifier achieved an identical classificatory accuracy with relatively few input features. Additionally, combining genetic algorithms with SVM classifier, the proposed hybrid GA-SVM strategy can simultaneously perform feature selection task and model parameters optimization. Experimental results show that SVM is a promising addition to the existing data mining methods.     

Credit scoring with a data mining approach based on support vector machines

The credit card industry has been growing rapidly recently, and thus huge numbers of consumers’ credit data are collected by the credit department of the bank. The credit scoring manager often evaluates the consumer’s credit with intuitive experience. However, with the support of the credit classification model, the manager can accurately evaluate the applicant’s credit score. Support Vector Machine (SVM) classification is currently an active research area and successfully solves classification problems in many domains. This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant’s credit score from the applicant’s input features. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the SVM classifier. Compared with neural networks, genetic programming, and decision tree classifiers, the SVM classifier achieved an identical classificatory accuracy with relatively few input features. Additionally, combining genetic algorithms with SVM classifier, the proposed hybrid GA-SVM strategy can simultaneously perform feature selection task and model parameters optimization. Experimental results show that SVM is a promising addition to the existing data mining methods.     

A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy

Two parameters, C and σ, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GA-SVM) model that can automatically determine the optimal parameters, C and σ, of SVM with the highest predictive accuracy and generalization ability simultaneously. This paper pioneered on employing a real-valued genetic algorithm (GA) to optimize the parameters of SVM for predicting bankruptcy. Additionally, the proposed GA-SVM model was tested on the prediction of financial crisis in Taiwan to compare the accuracy of the proposed GA-SVM model with that of other models in multivariate statistics (DA, logit, and probit) and artificial intelligence (NN and SVM). Experimental results show that the GA-SVM model performs the best predictive accuracy, implying that integrating the RGA with traditional SVM model is very successful.     

高炉焦炭质量的GA-SVM模型预测

焦炭的质量对高炉冶炼的生产有着重要的影响,为保证焦炭质量产量的稳定以及优化焦炭质量,针对线性回归预测方法难以解决配合煤与焦炭质量指标之间的非线性问题,通过分析焦炭质量影响因素,提出了一种基于GA-SVM模型的焦炭质量预测,解决了模型中惩罚因子C、基函数参数σ和不敏感损失参数ε难以确定的问题.最后基于某炼焦企业数据进行仿真实验,与BP神经网络预测比较,结果表明优化后的GA-SVM模型具有较高预测精度,对焦炭生产具有一定的应用价值.     

基于优化的支持向量机的机械设备多故障诊断模型

提出了一种基于遗传算法优化支持向量机的故障诊断模型.它利用遗传算法对支持向量机同时对传统的时域特征参量子集和核参数同时优化,以达到选择最优的设备故障主导特征参数组合的目的,实现对机器不同类型故障的识别.对齿轮故障诊断的结果表明它有效提高了多分类支持向量机的故障分类准确性.     

面向企业的数据挖掘方法研究

文章研究企业在数字化时代面临业务数据膨胀的形势下,如何有效分析和处理业务数据,从中提取对企业发展有利的信息。提高企业竞争力。基于数据挖掘技术提出了分析企业业务数据的方法,并基于某知名企业的真实业务数据,分析了企业业务量的数据特征,为企业的资源调配提供理论帮助。     

基于邻域保持学习的无监督特征选择算法

近邻法对不相关特征的敏感性很高,利用邻域重构系数可以保持原有数据结构的优点,为此,文中提出基于邻域保持学习的无监督特征选择算法.首先根据数据样本和邻域的相似性构造相似矩阵,并引入中间矩阵构造低维空间.然后利用拉普拉斯乘子法选择有效特征子集.在4个公开数据集上的实验表明,文中算法可以有效识别代表性特征.     

基于多模式特征聚合的未来商业预测

准确预测商业销售量未来趋势对于企业开发经营、政府宏观调控等至关重要.传统的数据预测方法计算时间开销大,具有主观性,而现有基于数据驱动的未来商业预测方法没有考虑到数据集中的特征多样.商业销售量数据是一个时序数据,时序数据中包含了丰富的时间窗特征、滞后历史特征和价格变化趋势特征等众多特征,先前的研究往往只注重于其中的某些特征,对于特征的融合和增强探究偏少,现有的未来商业预测方法的预测精度仍然有待提高.为此,本文提出了一种基于多模式特征聚合的未来商业预测方法,该方法首先将商业销售量数据进行预处理;然后基于特征工程提取数据集的5组不同的时间窗特征和其他特征;在机器学习上对于5组时间窗特征采用硬投票机制选择合适的模型训练,同时也采用神经网络的优化模型提取时序特征和预测结果,然后分析销售量数据集和某些特征之间的依赖关系;最后基于软投票模型完整地模型融合实现了商业销售量的高精度预测.一系列实验结果表明,本文提出的方法具有较高预测精度和效率,明显优于现有预测方法.     

基于遗传算法的支持向量机预测含能材料密度的研究

基于遗传算法(genetic algorithm,GA)的变量筛选和支持向量机(support vector machine,SVM),提出了一种改进的定量结构-性质相关(quantitative structure detonation relationship,QSPR)建模方法——遗传-支持向量机(GA-SVM),并用其建立含能材料的定量结构-爆轰性能关系(QSDR)模型,此外还应用标准SVM方法建立了QSDR模型,并用这2种模型进行呋咱系含能化合物密度的预测,随机选取85%化合物作为训练集,用来建立模型,其余化合物作为测试集来测试模型的预测能力。预测结果的交互检验的相关系数平方分别为0.9887和0.9885,平均相对误差分别为1.16%和2.12%,表明了2种建模方法的有效性。通过对2种模型的预测能力进行比较,GA-SVM方法建立的QSDR模型能更好地预测呋咱系含能化合物的密度,更利于实际应用。     

遗传优化核极限学习机的数据分类算法

为了提高核极限学习机(KELM)数据分类的精度,提出了一种结合K折交叉验证(K-CV)与遗传算法(GA)的KELM分类器参数优化方法(GA-KELM),将CV训练所得多个模型的平均精度作为GA的适应度评价函数,为KELM的参数优化提供评价标准,用获得GA优化最优参数的KELM算法进行数据分类.利用UCI中数据集进行仿真,实验结果表明:所提方法在整体性能上优于GA结合支持向量机法(GA-SVM)和GA结合反向传播(GA-BP)算法,具有更高的分类精度.     

基于角色的工作流系统存取控制模型研究

工作流系统中不同的业务流程之间资源的共享必然会引起一系列安全问题,安全策略在工作流系统中集中表现为存取控制策略。基于工作流系统的安全需求,给出了基于角色的工作流系统存取控制模型（WfRBAC）。在该模型中,引入任务来扩充RBAC模型的动态性。WfRBAC的6要素是用户、角色、任务、客体、权限和约束,约束分为动态约束和静态约束,能够满足工作流系统中的静态性和动态性存取控制要求。     

基于业务流的混合模式数据监视平台的设计与实现

在对当前数据监视平台主要设计模式分析的基础上,结合业务流的概念,提出一种基于业务流的混合模式数据监视平台设计方法。基于该方法,按照中国气象局的实际需求,实现一个气象服务业务数据监视平台,该平台设计灵活,具有一定的通用性,系统运行稳定可靠。     

Temporal data management systems: a comparative view

Characteristics and requirements of systems for temporal data management in the areas of data and knowledge bases, artificial intelligence, and software engineering are investigated and discussed on the basis of a case study. Six representative approaches were selected for this analysis, with the goal of identifying particular features of systems proposed in different areas. The six approaches are: Allen's interval-based logic, Dean and McDermott's time map management, Kowalski and Sergot's event calculus, Maiocchi and Pernici's TSOS. Snodgrass' TQuel, and Hagelstein's ERAE. The characteristics of each system are classified and compared. On the basis of this analysis, a framework for the evaluation of temporal systems and for the specification of temporal data management systems is proposed     

UML建模机制在领域电子商务系统中的应用研究

针对目前国内面向领域的B2B电子商务中缺乏稳定、系统的建模机制的现状，在分析UML的建模元索、扩展机制和元模型互换机制的基础上，提出了面向领域的电子商务系统的建模需求，包括用例建模、工作流建模、业务过程建模、业务词汇表建模、数据交换文档建模、消息建模等6个方面，并给出了利用开放的国际工业标准UML的用例图、活动图(包括泳道技术)、顺序图、类图以及文档、关系数据管理机制、XML、SOAP等在这6个建模方面的应用，最后给出了该建模机制在石油化工领域内的应用实例。     

Novel recommendation system based on long-term composition for adaptive web services

In the era of digital web services, composition of features on the fly is inevitable. The Long-term Composed Service (LCS) entertains the composition of features to any extent, since it has an open-ended lifetime. In the proposed research work, we have intended to provide service support to run the business toward a long time commitment. Structure-based recommended system for LCSs (RS-LCSs) is proposed, where user queries and recent updation/requirements are considered for exhibiting the response through the system. In the proposed system, business has been regulated according to the time constraints. We have tested our proposed system on the standard benchmark dataset and quantitative metrics show our proposed method has performed well against the compared methods. The forecasting of business has been done through our model to address the recent queries and new requirements issues to provide an adaptive web service for the business development.     

Improved churn prediction in telecommunication industry using data mining techniques

To survive in today's telecommunication business it is imperative to distinguish customers who are not reluctant to move toward a competitor. Therefore, customer churn prediction has become an essential issue in telecommunication business. In such competitive business a reliable customer predictor will be regarded priceless. This paper has employed data mining classification techniques including Decision Tree, Artificial Neural Networks, K-Nearest Neighbors, and Support Vector Machine so as to compare their performances. Using the data of an Iranian mobile company, not only were these techniques experienced and compared to one another, but also we have drawn a parallel between some different prominent data mining software. Analyzing the techniques’ behavior and coming to know their specialties, we proposed a hybrid methodology which made considerable improvements to the value of some of the evaluations metrics. The proposed methodology results showed that above 95% accuracy for Recall and Precision is easily achievable. Apart from that a new methodology for extracting influential features in dataset was introduced and experienced.     

一种国际业务信息安全防护模型

企业国际化经营是进一步拓展发展空间与提升可持续发展能力的有效途径,国际业务及其信息化建设的发展同时也给企业信息安全提出了新的挑战。文中针对电网企业国际业务及其信息安全的特点,提出了一种国际业务信息安全防护模型。在分析电网企业国际业务安全风险的基础上,从安全防护模型的主站层、网络层和终端层三个层次研究了安全防护技术措施,并提出了安全管理思路及措施。