基于计算机技术在医院信息化管理系统设计

针对医院信息管理工作难度大,数据种类复杂并且对于医院管理数据利用率低等问题,设计一种医院信息管理系统,该系统软件设计采用C/S架构记性设计;针对医院数据挖掘技术,通过改进Apriori算法和增量决策树算法对数据进行处理,提高医院信息利用率;并通过设计模拟实验方案对设计的算法进行验证,其中对于改进Apriori算法与原始的Apriori算法相比起处理速度提升了 10倍;对于增量决策树算法分类的准确率比C4.5算法和ID3算法高5％以上,并且在增量学习中耗时是C4.5算法和ID3算法的40％以下.     

浅析构造决策树分类方法

决策树是分类方法中的常用之一。对当前常用构造决策树的方法ID3和c4．5,最具代表性SLIQ和SPRINT算法进行介绍,在结合实例对ID3和C4．5算法构造决策树进行分析和比较,总结了各算法的特性。     

基于4C/ID模型的scratch任务驱动法教学设计

针对传统编程教育中存在的问题,结合教学过程中的"打砖块"实际案例,提出运用基于4C/ID模型的任务驱动教学法对小学scratch课堂中遇到的复杂性知识技能进行教学设计,从分析学情、提出教学任务、分析任务、参与任务、评价任务这5个方面介绍基于4C/ID模型的任务驱动教学法在课堂中的实施过程,最后总结该教学法在课堂实施过程需注意的问题.     

4C/ID模型在Web前端开发基础课程中的应用

结合4C/ID模型和项目式学习,提出基于4C/ID模型的项目式学习模式,详细阐述该模式分析阶段、设计阶段、探究阶段和评价阶段的设计要点,以Web前端开发基础课程为例,介绍基于4C/ID模型的项目式学习实践活动。     

融合信息增益与基尼指数的决策树算法

机器学习中的决策树算法具有重要的数据分类功能,但基于信息增益的ID3算法与基于基尼指数的CART算法的分类功效还值得提高.构造信息增益与基尼指数的自适应集成度量,设计有效的决策树算法,以提升ID3与C A RT两类基本算法的性能.分析信息增益信息表示与基尼指数代数表示的异质无关性,采用基于知识的加权线性组合来建立信息增...     

基于决策树的ID3算法和C4．5算法的比较

阐明决策树分类器在用于分类的数据挖掘技术中依然重要,论述基于决策树归纳分类的ID3、C4．5算法,并且对决策属性的选取法则进行说明。通过实例解析ID3、C4．5算法实现过程,结果表明C4．5算法相比较于ID3算法的优越性．尤其在处理具有多属性值的数据时的更加合理和正确。     

基于4C/ID的综合学习设计的评述

20世纪90年代开始整体性教学设计已经成为教育领域发展的必然趋势。通过对整体性教学设计模式最具代表的4C/ID模型来对综合学习设计进行探讨。通过对国内外研究现状的分析,提出该模型存在的问题及发展趋势,以期能够为广大学者进一步深入探究面向综合学习的教学设计提供一定支持,并使基于4C/ID的综合学习设计成为教育技术学科研究的一个新热点领域。     

新一代互联网的定位符与标识符分离方案

为了实现新一代互联网定位符(identifier,ID)和标识符(locator,Loc)的分离,提议一种解决方案:结合分布式哈希表(distributed Hash table,DHT)技术和域的层次结构设计ID/Loc映射系统;采用分层聚合方法设计路由体系结构,并定义定位符和路由算法。分析了该方案如何满足对映射信息的快速查询和更新、地址重分配、多家乡的支持、快速路由等新一代互联网的要求。通过C++编写仿真程序,分析路由算法的执行时间和映射系统的查询时间,证明了该方案的可行性和算法的良好收敛性。     

改进的决策树算法在文理分科中的应用研究

文理分科是高中生面临的第一次重大选择,选文科还是理科,很多同学感到两头难。针对这种情况,通过对比决策树分类算法中的ID3和C4.5算法,提出了基于影响因子的新的分类算法,构造了"文理分科分类器"。实验证明该方法在文理分科问题上比传统的ID3和C4.5算法有更高的分类精确度,该分类器可以辅助学生和家长进行文理科的选择,降低选择的错误性。     

决策树分类算法——ID3算法及其改进算法C4.5讨论

本文讨论了基于互信息的决策树分类算法——ID3算法及其改进算法C4．5。     

基于gm/ID参数的运算放大器设计

介绍一种基于gm/ID参数特性的模拟电路优化设计方法,并以CMOS密勒补偿运算放大器的设计为例具体阐述该方法的基本设计步骤.该方法以统一的gm/ID与ID/(W/L)的关系曲线为基本设计出发点,综合电路的其它设计要求,而提出的一种优化性能指标的设计思路.对所设计的运算放大器模拟仿真验证了这种方法的有效性.     

决策树相关算法研究

ID3算法和C4.5算法是经典的决策树算法,通过对ID3算法和C4.5算法的数据结构、算法描述和分裂属性选取等方面进行比较,为其他研究者提供参考.     

基于灰度多值化的身份证号码识别

为了方便警察在公共场所核对居民的身份证信息,实现身份证号码的自动识别,提出了一种基于灰度多值化的身份证号码识别方法。该方法的思想是先找到身份证的四条边缘在图像中的位置,根据身份证号码所处区域在身份证中是固定的,可以定位到身份证号码所在区域;采用投影法分割出身份证号码单个字符;将单个字符灰度图像进行多值化处理,提取字符的网格特征,利用模板匹配法识别出字符。该方法不仅能快速地识别出号码,而且对于比较模糊或亮度不均匀的图像也能准确地识别。     

移动平台下的身份证图像字符分割方法研究

基于移动平台的身份证识别有广阔的应用。针对智能手机拍照的二代身份证图像字符分割进行了研究,提出了基于图像对照的字符分割方法,该方法可对身份证图像在拍照过程中的旋转进行自适应校正,并能有效实现手机拍照身份证图像字符的精确分割,进而有利于身份证字符在移动平台上进行识别。     

Perspectives on problem solving and instruction

Most educators claim that problem solving is important, but they take very different perspective on it and there is little agreement on how it should be taught. This article aims to sort out the different perspectives and discusses problem solving as a goal, a method, and a skill. As a goal, problem solving should not be limited to well-structured problem solving but be extended to real-life problem solving. As a method, problem solving has clear limitations for novice learners; providing ample support to learners is of utmost importance for helping them to develop problem-solving skills. As a skill, problem solving should not be seen as something that only occurs in the early phases of a process of expertise development but as a process that develops in parallel in System 1 and System 2. The four-component instructional design model (4C/ID) is briefly discussed as an approach that is fully consistent with the conceptualization described in this article and as a preliminary answer to the question how problem solving is best taught.     

基于相对熵的决策树生成方法

决策树是一种重要的数据分类方法,在构造决策树的过程中,测试属性的选择直接影响到决策树中结点的个数和深度,基于相对熵的概念提出了一种新的决策树构造方法。实例分析的结果表明:在决策树的构造上,粗糙集理论中相对熵的方法计算量较小,构造的决策树比经典ID3,C4.5算法简洁,并且具有较高的分类精度。     

Qualitative analysis of mapping systems in the Internet architectures based on identifier/locator separation

The current Internet has several known challenges, such as routing scalability, mobility, multihoming, traffic engineering, etc. due to the overloaded semantics of IP address, i.e. it is used as a node identifier (ID) and a node locator (LOC). Thus, the research community has redesigned the Internet architecture based on ID/LOC separation to overcome the limitations of the current Internet. In all Internet architectures based on ID/LOC separation, ID to LOC mapping system is necessarily required to bind ID and its LOC, since ID is no longer dependent to its LOC logically or physically. Thus, how to design the mapping system is a key challenge in ID/LOC separation architecture. In this paper, we analyse qualitatively the mapping systems proposed in ID/LOC separation architectures to provide insights into designing a new mapping system. The main contribution in this paper is that we categorise ID to LOC mapping systems according to the mapping server structure and provide the pros and cons of the mapping systems belonging to each category. Based on our qualitative analysis, we also examine intuitively if the mapping systems in each category satisfy their requirements.     

Creating Decision Trees from Rules using RBDT‐1

Most of the methods that generate decision trees for a specific problem use the examples of data instances in the decision tree–generation process. This article proposes a method called RBDT‐1—rule‐based decision tree—for learning a decision tree from a set of decision rules that cover the data instances rather than from the data instances themselves. The goal is to create on demand a short and accurate decision tree from a stable or dynamically changing set of rules. The rules could be generated by an expert, by an inductive rule learning program that induces decision rules from the examples of decision instances such as AQ‐type rule induction programs, or extracted from a tree generated by another method, such as the ID3 or C4.5. In terms of tree complexity (number of nodes and leaves in the decision tree), RBDT‐1 compares favorably with AQDT‐1 and AQDT‐2, which are methods that create decision trees from rules. RBDT‐1 also compares favorably with ID3 while it is as effective as C4.5 where both (ID3 and C4.5) are well‐known methods that generate decision trees from data examples. Experiments show that the classification accuracies of the decision trees produced by all methods under comparison are indistinguishable.