Estimating Development Time and Effort of Software Projects by using a Neuro_Fuzzy Approach

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Fuzzy Multiple Regression Model for Estimating Software Development Time

As software becomes more complex and its scope dramatically increase, the importance of research on developing methods for estimating software development time has perpetually increased, so accurate estimation is the main goal of software managers for reducing risks of projects. The purpose of this article is to introduce a new Fuzzy Multiple Regression approach, which has the higher accurate than other methods for estimating. Furthermore, we compare Fuzzy Multiple Regression model with Fuzzy Logic model & Multiple Regression model based on their accuracy

Improving Effort Estimation by Voting Software Estimation Models

Estimating software development effort is an important task in the management of large software projects. The task is challenging, and it has been receiving the attentions of researchers ever since software was developed for commercial purpose. A number of estimation models exist for effort prediction. However, there is a need for novel models to obtain more accurate estimations. The primary purpose of this study is to propose a precise method of estimation by selecting the most popular models in order to improve accuracy. Consequently, the final results are very precise and reliable when they are applied to a real dataset in a software project. Empirical validation of this approach uses the International Software Benchmarking Standards Group (ISBSG) Data Repository Version 10 to demonstrate the improvement in software estimation accuracy

When it comes to assessing the impact of e-cigarettes, estimates of device prevalence matter: the BIDI Stick disposable device

Abstract Background While e-cigarettes have been identified as an effective means of tobacco harm reduction, the degree to which these devices will realise their harm reduction potential will be determined in large part by how available these products are to adults who smoke. One of the key factors determining that availability is the regulatory regime around these products. Within the US e-cigarettes have become the most commonly used tobacco product by middle and high school students, with disposable e-cigarettes now the most popular type of device used by youth. In this paper, we report data on the prevalence with which one of the most popular disposable e-cigarettes within the US is being used by youth (aged 13–17) and underage young adults (aged 18–20). Methods A cross-sectional online survey of a probability-based sample of 1215 youth (13–17 years) recruited via Ipsos’ KnowledgePanel and 3370 young adults aged 18–24, among whom 1125 were aged 18–20, recruited via Ipsos’ KnowledgePanel and online consumer research panels. Results Among youth, 3.50% (95% CI: 2.46–4.83) reported smoking combustible cigarettes in the past 30 days, and 6.73% (95% CI: 5.26–8.48) reported using an e-cigarette in the past 30 days. Among underage young adults, 7.22% (95% CI: 5.24–9.67) reported smoking combustible cigarettes every day or some days, and 15.90% (95% CI: 12.96–19.21) reported using e-cigarettes every day or some days. Despite the scale of e-cigarette use in general among the US youth, only 0.04% (95% CI: 0.00–0.38) of 13–17 years old reported using the BIDI® Stick disposable e-cigarette in the past 30 days. Conclusions While disposable e-cigarettes have become the most popular type of e-cigarette used by the US youth, it is likely that the prevalence of use of individual devices varies significantly. There is a need to regularly monitor the use of e-cigarettes by type of device and brand, in order to determine which products have the greatest potential to reduce smoking-related harm among adults who smoke and which products are being used by youth and underage young adults