In this paper we propose a Neural Net-PMRS hybrid for forecasting time-series data. The neural network model uses the traditional MLP architecture and backpropagation method of tr...
Software engineering researchers have long been interested in where and why bugs occur in code, and in predicting where they might turn up next. Historical bug-occurence data has ...
Christian Bird, Adrian Bachmann, Eirik Aune, John ...
—Recently, Bayesian probabilistic models have been used for predicting software development effort. One of the reasons for the interest in the use of Bayesian probabilistic model...
Parag C. Pendharkar, Girish H. Subramanian, James ...
Background: Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also r...
Richard Judson, Fathi Elloumi, R. Woodrow Setzer, ...
Abstract. Accurately modeling and predicting performance for largescale applications becomes increasingly difficult as system complexity scales dramatically. Analytic predictive mo...
Engin Ipek, Bronis R. de Supinski, Martin Schulz, ...