<div style="text-align:justify;"> <span style="font-family:Verdana;">Software Cost Estimation (SCE) is an essential requirement in producing software these days. Genuine accurate estima...<div style="text-align:justify;"> <span style="font-family:Verdana;">Software Cost Estimation (SCE) is an essential requirement in producing software these days. Genuine accurate estimation requires cost-and-efforts factors in delivering software by utilizing algorithmic or Ensemble Learning Methods (ELMs). Effort is estimated in terms of individual months and length. Overestimation as well as underestimation of efforts can adversely affect software development. Hence, it is the responsibility of software development managers to estimate the cost using the best possible techniques. The predominant cost for any product is the expense of figuring effort. Subsequently, effort estimation is exceptionally pivotal and there is a constant need to improve its accuracy. Fortunately, several efforts estimation models are available;however, it is difficult to determine which model is more accurate on what dataset. Hence, we use ensemble learning bagging with base learner Linear regression, SMOReg, MLP, random forest, REPTree, and M5Rule. We also implemented the feature selection algorithm to examine the effect of feature selection algorithm BestFit and Genetic Algorithm. The dataset is based on 499 projects known as China. The results show that the Mean Magnitude Relative error of Bagging M5 rule with Genetic Algorithm as Feature Selection is 10%, which makes it better than other algorithms.</span> </div>展开更多
Aims: This study aims at designing and implementing syllabus-oriented question-bank system that is capable of producing paper-based exams with multiple forms along with answer keys. The developed software tool is nam...Aims: This study aims at designing and implementing syllabus-oriented question-bank system that is capable of producing paper-based exams with multiple forms along with answer keys. The developed software tool is named Χ(Chi)-Pro Milestone and supports four types of questions, namely: Multiple-choice, True/False, Short-Answer and Free-Response Essay questions. The study is motivated by the fact that student number in schools and universities is continuously growing at high, non-linear, and uncontrolled rates. This growth, however, is not accompanied by an equivalent growth of educational resources (mainly: instructors, classrooms, and labs). A direct result of this situation is having relatively large number of students in each classroom. It is observed that providing and using online-examining systems could be intractable and expensive. As an alternative, paper-based exams can be used. One main issue is that manually produced paper-based exams are of low quality because of some human factors such as instability and relatively narrow range of topics [1]. Further, it is observed that instructors usually need to spend a lot of time and energy in composing paper-based exams with multiple forms. Therefore, the use of computers for automatic production of paper-based exams from question banks is becoming more and more important. Methodology: The design and evaluation of X-Pro Milestone are done by considering a basic set of design principles that are based on a list of identified Functional and Non-Functional Requirements. Deriving those requirements is made possible by developing X-Pro Milestone using the Iterative and Incremental model from software engineering domain. Results: We demonstrate that X-Pro Milestone has a number of excellent characteristics compared to the exam-preparation and question banks tools available in market. Some of these characteristics are: ease of use and operation, user-friendly interface and good usability, high security and protection of the question bank-items, high stability, and reliability. Further, X-Pro Milestone makes initiating, maintaining and archiving Question-Banks and produced exams possible. Putting X-Pro Milestone into real use has showed that X-Pro Milestone is easy to be learned and effectively used. We demonstrate that X-Pro Milestone is a cost-effective alternative to online examining systems with more and richer features and with low infrastructure requirements.展开更多
目的应用Image-Pro Plus 5.0图像处理和分析软件,研究鸡胚尿囊膜(chick chorioallantoic membrane,CAM)血管新生面积定量的新方法。方法20只发育良好的7日龄鸡胚,分为龙葵给药组和对照组,每组10只。将中药龙葵水提液及等量蒸馏水吸附于5...目的应用Image-Pro Plus 5.0图像处理和分析软件,研究鸡胚尿囊膜(chick chorioallantoic membrane,CAM)血管新生面积定量的新方法。方法20只发育良好的7日龄鸡胚,分为龙葵给药组和对照组,每组10只。将中药龙葵水提液及等量蒸馏水吸附于5 mm直径的定性滤纸,置于CAM上。利用Image-Pro Plus 5.0软件,定量血管新生面积、蛋壳开窗处对应的CAM面积,计算出血管新生面积与CAM面积的比值。结果用Image-Pro Plus 5.0可方便、自动、准确地进行给药前后的数据收集和面积计算。统计分析表明,受试物龙葵可明显抑制CAM血管新生,与对照组比较有极显著差异(P<0.001)。结论Image-Pro Plus 5.0图像处理和分析软件是一种高效、准确的统计血管新生面积的工具,用该软件定量蛋壳开窗部位下的血管新生面积占开窗部位所对应的CAM总面积,不仅操作简便,而且数据计算自动生成,可较准确地反映鸡胚血管新生情况。展开更多
文摘<div style="text-align:justify;"> <span style="font-family:Verdana;">Software Cost Estimation (SCE) is an essential requirement in producing software these days. Genuine accurate estimation requires cost-and-efforts factors in delivering software by utilizing algorithmic or Ensemble Learning Methods (ELMs). Effort is estimated in terms of individual months and length. Overestimation as well as underestimation of efforts can adversely affect software development. Hence, it is the responsibility of software development managers to estimate the cost using the best possible techniques. The predominant cost for any product is the expense of figuring effort. Subsequently, effort estimation is exceptionally pivotal and there is a constant need to improve its accuracy. Fortunately, several efforts estimation models are available;however, it is difficult to determine which model is more accurate on what dataset. Hence, we use ensemble learning bagging with base learner Linear regression, SMOReg, MLP, random forest, REPTree, and M5Rule. We also implemented the feature selection algorithm to examine the effect of feature selection algorithm BestFit and Genetic Algorithm. The dataset is based on 499 projects known as China. The results show that the Mean Magnitude Relative error of Bagging M5 rule with Genetic Algorithm as Feature Selection is 10%, which makes it better than other algorithms.</span> </div>
文摘Aims: This study aims at designing and implementing syllabus-oriented question-bank system that is capable of producing paper-based exams with multiple forms along with answer keys. The developed software tool is named Χ(Chi)-Pro Milestone and supports four types of questions, namely: Multiple-choice, True/False, Short-Answer and Free-Response Essay questions. The study is motivated by the fact that student number in schools and universities is continuously growing at high, non-linear, and uncontrolled rates. This growth, however, is not accompanied by an equivalent growth of educational resources (mainly: instructors, classrooms, and labs). A direct result of this situation is having relatively large number of students in each classroom. It is observed that providing and using online-examining systems could be intractable and expensive. As an alternative, paper-based exams can be used. One main issue is that manually produced paper-based exams are of low quality because of some human factors such as instability and relatively narrow range of topics [1]. Further, it is observed that instructors usually need to spend a lot of time and energy in composing paper-based exams with multiple forms. Therefore, the use of computers for automatic production of paper-based exams from question banks is becoming more and more important. Methodology: The design and evaluation of X-Pro Milestone are done by considering a basic set of design principles that are based on a list of identified Functional and Non-Functional Requirements. Deriving those requirements is made possible by developing X-Pro Milestone using the Iterative and Incremental model from software engineering domain. Results: We demonstrate that X-Pro Milestone has a number of excellent characteristics compared to the exam-preparation and question banks tools available in market. Some of these characteristics are: ease of use and operation, user-friendly interface and good usability, high security and protection of the question bank-items, high stability, and reliability. Further, X-Pro Milestone makes initiating, maintaining and archiving Question-Banks and produced exams possible. Putting X-Pro Milestone into real use has showed that X-Pro Milestone is easy to be learned and effectively used. We demonstrate that X-Pro Milestone is a cost-effective alternative to online examining systems with more and richer features and with low infrastructure requirements.
文摘目的应用Image-Pro Plus 5.0图像处理和分析软件,研究鸡胚尿囊膜(chick chorioallantoic membrane,CAM)血管新生面积定量的新方法。方法20只发育良好的7日龄鸡胚,分为龙葵给药组和对照组,每组10只。将中药龙葵水提液及等量蒸馏水吸附于5 mm直径的定性滤纸,置于CAM上。利用Image-Pro Plus 5.0软件,定量血管新生面积、蛋壳开窗处对应的CAM面积,计算出血管新生面积与CAM面积的比值。结果用Image-Pro Plus 5.0可方便、自动、准确地进行给药前后的数据收集和面积计算。统计分析表明,受试物龙葵可明显抑制CAM血管新生,与对照组比较有极显著差异(P<0.001)。结论Image-Pro Plus 5.0图像处理和分析软件是一种高效、准确的统计血管新生面积的工具,用该软件定量蛋壳开窗部位下的血管新生面积占开窗部位所对应的CAM总面积,不仅操作简便,而且数据计算自动生成,可较准确地反映鸡胚血管新生情况。
