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 development effort estimation is one of the most major activities insoftware project management. A number of models have been proposed to constructa relationship between software size and effort; however we still have problemsfor effort estimation. This is because project data, available in the initialstages of project is often incomplete, inconsistent, uncertain and unclear. Theneed for accurate effort estimation in software industry is still a challenge.Artificial Neural Network models are more suitable in such situations. Thepresent paper is concerned with developing software effort estimation modelsbased on artificial neural networks. The models are designed to improve theperformance of the network that suits to the COCOMO Model. Artificial NeuralNetwork models are created using Radial Basis and Generalized Regression. Acase study based on the COCOMO81 database compares the proposed neural networkmodels with the Intermediate COCOMO. The results were analyzed using fivedifferent criterions MMRE, MARE, VARE, Mean BRE and Prediction. It is observedthat the Radial Basis Neural Network provided better results

Software Effort Estimation using Radial Basis and Generalized Regression Neural Networks