OW‐SVM: Ontology and whale optimization‐based support vector machine for privacy‐preserved medical data classification in cloud

Cloud is a multitenant architecture that allows the cloud users to share the resources via servers and is used in various applications, including data classification. Data classification is a widely used data mining technique for big data analysis. It helps the learners to discover hidden data patterns by training massive data collected from the real world. Because this trained model is the private asset of an entity, it should be protected from all other noncollaborative entities. Therefore, it is essential to take effective measures to preserve the confidential data. The objective of this paper is to preserve the privacy of the confidential data in the cloud environment by introducing the medical data classification method. In view of that, this paper presents a method for medical data classification using a novel ontology and whale optimization‐based support vector machine (OW‐SVM) approach. Initially, privacy‐preserved data are developed adopting Kronecker product bat approach, and then, ontology is built for the feature selection process. Ontology and whale optimization‐based support vector machine is then proposed by integrating ontology and whale optimization algorithm into SVM, in which ontology and whale optimization algorithm is used for the feasible selection of kernel parameters. The experiment is done using 3 heart disease datasets, such as Cleveland, Switzerland, and Hungarian. In a comparative analysis, the performance of the OW‐SVM approach is compared with that of K‐nearest neighbor, Naive Bayes, decision tree, SVM, and OW‐SVM, using accuracy, sensitivity, specificity, and fitness, as the evaluation metrics. The OW‐SVM approach could achieve maximum performance with accuracy of 83.21%, the sensitivity of 91.49%, specificity of 73%, and fitness of 81.955, outperforming existing comparative techniques.     

自适应推荐算法在电子超市个性化服务系统中的应用研究

为了满足电子超市中用户的个性化的服务需求，提出并实现了一种基于支持向量机的自适应推荐算法。首先，将用户模型按照层次化方式组织成领域信息和原子需求信息，考虑多用户同类信息需求。采用支持向量机对领域信息节点中的原子需求信息进行分类协同推荐，然后在针对每一领域信息节点中的原子信息需求进行基于内容的过滤。该算法克服了分别采用协同推荐和基于内容的推荐单一方法的缺点，大大提高了信息的查准率和查全率，尤其适合大规模用户群的信息推荐。该算法用于基于电子超市的个性化推荐服务系统（PRSSES）中，结果表明是有效的。     

基于改进的混合模式个性化选课推荐技术研究

针对高等学校学生选课系统中存在的缺乏个性化课程推荐、选课效率较低的问题,通过对个性化推荐技术的分析研究,提出了基于内容、项目及用户属性的改进混合模式算法,并将该算法应用到选课系统中,用MACE数据集对算法进行验证。结果表明,该算法解决了个性化推荐技术中的冷启动问题,相关指标有明显提高,实现了课程与新课程的个性化推荐,并减少了选课的盲目性。     

基于本体和知识组件的高低机设计方法研究

为了提高高低机设计质量和效率,提出一种基于本体和知识组件的高低机设计方法。利用本体和知识组件开展高低机设计知识的表示与建模,构建高低机设计知识库,在明确基于本体和知识组件的高低机设计方法的基础上,以Java为平台开发了高低机设计专用系统,并以某高低机的设计实例进行验证。验证结果表明：该方法及开发的设计系统具有可行性和有效性,能够按照本体中既定设计流程,依次调用对应知识组件,实时提示相关设计知识,辅助设计人员完成繁琐与重复的计算工作。     

基于语义扩展的Web服务发现方法

提出了一种基于语义扩展的Web服务发现方法,对服务描述和服务请求中的领域概念进行了同义扩展、特征扩展和合成扩展,在输入输出的匹配上提出了“扩展的语义包含”的概念,实验表明,该方法在不降低服务查准率的前提下,提高了服务的查全率。当单个服务不能满足服务请求时,尝试服务组合匹配服务请求的方式,采用基于H*算法的启发式的服务组合方法,将问题转化为搜索从初始节点到目标节点的通路问题,该方法采用估值函数引入了启发知识,提高了搜索的效率。     

高维环网上的一种可扩展的全交换算法

全交换在并行计算领域中有着大量而且重要的应用,例如FFT和矩阵运算等.本文提出了一种适合环网结构的全交换算法.算法中采用了新的网络划分技术及通信模式,使高维环网上全交换算法的通信量的主项达到了理论下限,这是已知的其他相关算法未能达到的,且其启动次数与通信量均优于现有的其他同类算法.本文所述的算法并不要求环网每一维上的处理器结点数目是2的方幂或某一个数的平方.最后,该算法简单规范,易于硬件高效实现.     

基于语义的网络安全策略层次依从性分析

Network security policy and the automated refinement of its hierarchies aims to simplify the administration of security services in complex network environments. The semantic gap between the policy hierarchies reflects the validity of the policy hierarchies yielded by the automated policy refinement process. However, little attention has been paid to the evaluation of the compliance between the derived lower level policy and the higher level policy. We present an ontology based on Ontology Web Language (OWL) to describe the semantics of security policy and their implementation. We also propose a method of estimating the semantic similarity between a given higher level security policy and the lower level ones to evaluate the compliance for the policy refinement approach. The method is verified in the case study. The experimental results demonstrate that the proposed method evaluates the semantic similarity between policy and implementation accurately, and that the algorithm of concept similarity analysis reflects the subjective similarity judgment of policy and implementation more accurate than the other algorithms.     

基于本体的决策问题语义理解及精炼方法

理解复杂决策问题的关键在于使决策问题所包含的领域、目标、状态、结构等信息被系统所“读懂”.本文利用本体表达语义,实现了问题语义关系表示和语义计算.在保持理解结果不变的原则上,本文提出利用语义迭代方法将计算机无法理解的问题语义转化为可完全理解的语义.本文给出了语义精炼方法并形成最优闭合问题空间,在结构复杂度和内容复杂度两方面具有最低复杂度.实验分析表明,本文提出的复杂决策问题语义理解方法是有效的.     

A Robust Wearable Point-of-Care CNT-Based Strain Sensor for Wirelessly Monitoring Throat-Related Illnesses

Point-of-care testing (POC) has the ability to detect chronic and infectious diseases early or at the time of occurrence and provide a state-of-the-art personalized healthcare system. Recently, wearable and flexible sensors have been employed to analyze sweat, glucose, blood, and human skin conditions. However, a flexible sensing system that allows for the real-time monitoring of throat-related illnesses, such as salivary parotid gland swelling caused by flu and mumps, is necessary. Here, for the first time, a wearable, highly flexible, and stretchable piezoresistive sensing patch based on carbon nanotubes (CNTs) is reported, which can record muscle expansion or relaxation in real-time, and thus act as a next-generation POC sensor. The patch offers an excellent gauge factor for in-plane stretching and spatial expansion with low hysteresis. The actual extent of muscle expansion is calculated and the gauge factor for applications entailing volumetric deformations is redefined. Additionally, a bluetooth-low-energy system that tracks muscle activity in real-time and transmits the output signals wirelessly to a smartphone app is utilized. Numerical calculations verify that the low stress and strain lead to excellent mechanical reliability and repeatability. Finally, a dummy muscle is inflated using a pneumatic-based actuator to demonstrate the application of the affixed wearable next-generation POC sensor.     

基于环境变迁的构件组合模型

Internet为分布构件的重用提供了平台，准确、高效的构件组合是新型软件生产的重要环节．针对新一代的软件模型．网构软件，本文首先建立了构件的环境本体，并提出将变迁图作为刻画环境本体的模型；其次提出了基于环境及其状态变迁的构件描述框架；然后针对网构软件的特征，提出了基于环境变迁的构件组合算法，最后用具体的案例展示了算法的可行性．