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In this paper, we propose a novel Artificial Neural Network (ANN) to predict software effort from use case diagrams based on the Use Case Point (UCP) model. The inputs of this model are software size, productivity and complexity, while the output is the predicted software effort. A multiple linear regression model with three independent variables (same inputs of the ANN) and one dependent variable...
Learning with imbalanced datasets has been a major topic of study for many years. In this paper, we focus on a type of imbalance called imbalance due to rare instances. Such imbalances occur in a variety of domains. Rare instances have received less focus in prediction problems and we wish to draw attention to how accuracy can be improved in the presence of rare data. We discuss an approach to regression...
Building prediction models for suggestive knowledge from multiple sources dynamically is of great interest from a clinical decision support point of view. This is valuable in situations where the local clinical data repository does not have sufficient number of records to draw conclusions from. However, due to privacy concerns, hospitals are reluctant to divulge patient records. Consequently, a distributed...
One of the key feature of modern architectures is deep memory hierarchies. In order to exploit this feature, one has to expose data locality with-in a program. Loop tiling is an optimization phase in modern compilers which is used to transform a loop for exposing data locality. Selecting the best tile size for a given architecture and compiler is known as Optimal Tile Size Selection Problem. It is...
High dimensionality and class imbalance are the two main problems affecting many software defect prediction. In this paper, we propose a new technique, named SelectRUSBoost, which is a form of ensemble learning that in-corporates data sampling to alleviate class imbalance and feature selection to resolve high dimensionality. To evaluate the effectiveness of the new technique, we apply it to a group...
The aim of this paper is to investigate the quality of methodology in software fault prediction studies using machine learning. Over two hundred studies of fault prediction have been published in the last 10 years. There is evidence to suggest that the quality of methodology used in some of these studies does not allow us to have confidence in the predictions reported by them. We evaluate the machine...
Software effort prediction is an important task in the software development life cycle. Many models including regression models, machine learning models, algorithmic models, expert judgment and estimation by analogy have been widely used to estimate software effort and cost. In this work, a Tree boost (Stochastic Gradient Boosting) model is put forward to predict software effort based on the Use Case...
This paper introduces an unsupervised model for melodic segmentation that extends a method initially proposed in computational biology. In the model segments are identified as sections of maximal contrast within a musical piece, using for this the Jensen-Shannon divergence. The model is extended upon its original formulation, and experiments to test its performance are carried out for a small set...
Social media sites are now the most popular destination for Internet users, providing social scientists with a great opportunity to understand online behaviour. There are a growing number of research papers related to social media, a small number of which focus on personality prediction. To date, studies have typically focused on the Big Five traits of personality, but one area which is relatively...
Very often important process variables cannot be measured online due to low sampling rate of sensors or because their values have to be obtained by laboratory analysis. In order to enable continuous process monitoring and efficient process control in such cases, soft sensors are usually used to estimate these difficult-to-measure process variables. Most industrial processes exhibit some kind of time-varying...
This paper introduces a neural fuzzy network approach for evolving system modeling. The approach uses neofuzzy neurons and a neural fuzzy structure monished with an incremental learning algorithm that includes adaptive feature selection. The feature selection mechanism starts considering one or more input variables from a given set of variables, and decides if a new variable should be added, or if...
In this paper, two well-known algorithms: ARIMA and TDNN (Time Delay Neural Network) are applied to conduct the short term prediction of solar radiation. For the daily solar radiation series is non-stable due to the fast weather changing, monthly average solar radiation is adopted as the data source. As ARIMA model requires the time series to be stationary, first order difference is performed on the...
Excitation current of a synchronous motor has a key role in reactive power compensation. For this purpose, the k-nearest neighbor (k-NN) classifier designed in this paper predicts the excitation current parameter using n-tupled inputs. Load current, power factor, power factor error and the change of excitation current parameters were utilized in n-tupled inputs. Moreover, Euclidean, Manhattan and...
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