English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Software effort estimation plays an important role in the software development process: inaccurate estimation leads to poorutilization of resources and possibly to software project failure. Many software effort estimation techniques have been tried inan effort to develop models that generate optimal estimation accuracy, one of which is machine learning. It is crucial in machinelearning to use a model that will maximize accuracy and minimize uncertainty for the purposes of software effort estimation.However, the process of selecting the best algorithm for estimation is complex and expert-dependent. This paper proposes anapproach to analyzing datasets, automatically building estimation models with various machine learning techniques, and evaluatingand comparing their results to find the model that produces the most accurate and surest estimates for a specific dataset.The proposed approach to automated model selection combines the Bayesian information criterion, correlation coefficients, andPRED measures.

Automated selection of a software effort estimation model based on accuracy and uncertainty