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In the electricity sector, new sides have emerged with the development of technology and the increasing the electric energy need. Today, electricity has become a product that is bought and sold in the market environment. Forecasting which is the first step of plans and planning have become much more important and have been made mandatory for the market participants by energy market regulators. In...
In software project management, software development effort estimation (SDEE) is one of the critical activities. Analogy-Based Estimation (ABE) is most popular estimation technique suggested in SDEE literature [1, 7, 22]. Researchers have proposed various methods to improve the accuracy of ABE by adjusting the retrieved solution. The research suggests all published calibration methods depend on linear...
The paper presents application of STATISTICA v6.0 and STATISTICA NEURAL NETWORKS software for electrical load forecasting. Relevance of forecasting is influenced by the fact that extraction of minerals in oil and gas industry is increasing. As oil extraction and transportation is very power intensive, the problem of load growth has arisen. Then, a task for forecasting of load growth occurs. The results...
Software defect prediction (SDP) is a most dynamic research area in software engineering. SDP is a process used to predict the deformities in the software. To identifying the defects before the arrival of item or aimed the software improvement, to make software dependable, defect prediction model is utilized. It is always desirable to predict the defects at early stages of life cycle. Hence to predict...
Application of neural networks for direct prediction of lateral-directional force and moments coefficients from the measured flight data of the research aircraft is proposed in this paper. Proposed model of neural networks appears to be a suitable practical approach to develop relationship between flight variables. This relationship eliminates the need of aerodynamic model as well as thrust model...
For a successful software project, accurate prediction of its overall effort and cost estimation is a very much essential task. Software projects have evolved through a number of development models over the last few decades. Hence, to cover an accurate measurement of the effort and cost for different software projects based on different development models having new and innovative phases of software...
This paper proves the dependence of successful of software project implementation on the software requirements specification (SRS), the actuality and importance of the skill to evaluate the possible success of software project based on the specifications. The neural network model of prediction of the software project characteristics for evaluating the success of its implementation based on analysis...
This paper adopts a novel methodology to predict China's grain production. Using a grey model to capture the main trend, this paper establishes a modified model of BP neural networks and then analyzes the irregular events and its influencing direction and degree with Delphi methods. By testing the validity of the final model, the result shows an encouraging conclusion that the model is effective and...
Software project managers need information such as cumulative number of failures present in a software after testing a certain period of time to determine release time of software. In this paper, an artificial neural network (ANN) based model which uses a new network architecture is proposed to predict cumulative number of failures in software. An extra layer is added between input layer and hidden...
Software has gained popularity in daily activities ranging from small scale applications running on handheld devices to complex application and big data processing. The software is critical in nature as it has become the most vital part of a system resulting in risks related to software failures. The risk estimate associated with a system can be calculated using different techniques. The performance...
A severe problem that impacts the software project is inaccurate estimation of the effort. Estimation of the software development effort remains an intricate problem. The complexity of the software and its scope are increasing alarmingly which attracts many researchers. Past the decades numerous techniques have been introduced and implemented. Many of them have given good results with acceptable error...
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...
For more than three decades, Box and Jenkins' Auto-Regressive Integrated Moving Average (ARIMA) technique has been one of the most widely used linear models in time series forecasting. However, it is well documented that many software failure observations are nonlinear and ARIMA is a general univariate model developed based on the assumption that the time series data being predicted are linear. Therefore,...
the prediction method of workload of oil production program is studied and the forecasting software of oil production program is compiled by using C/S mode and integrated application Odac, SQL, OLE and other technologies that provides users with simple, user-friendly work environment. The application of the software will enhance efficiency of oil production program for the preparation and reduce labor...
Statistical regression and neural networks have frequently been used to estimate the development effort of both short and large software projects. In this paper, a genetic programming technique is used with the goal of estimating the effort required in the development of short-scale projects. Results obtained are compared with those generated using the first two techniques. A sample of 132 short-scale...
Software reliability assessment has been a vital factor to characterize the quality of any software product quantitatively during testing phase. Over the years many analytical models have been proposed for modeling software reliability growth trends with different predictive capabilities at different phases of testing. Yet we need to develop such single model that can be applied for accurate prediction...
Non-parametric statistical methods are applied to verdict that early failure behavior of the testing process may have less impact on later failure process, so it happens in software failure time prediction that one does not have enough information to estimate the software failure process well but do have enough information to estimate the failure data at given instance. The prediction accuracy of...
Relevance vector machines have been successfully used in many domains, while their application in software reliability prediction is still quite rare. In this work, we propose to apply support vector regression (SVR) to build software reliability prediction model (RVMSRPM). We also compare the prediction accuracy of software reliability prediction models based on RVM, SVM, ANN and three traditional...
Software fault detection is an important factor for quantitatively characterizing software quality. One of the proposed methods for software fault detection is neural networks. Fault detection is actually a pattern recognition task. Faulty and fault free data are different patterns which must be recognized. In this paper we propose a new framework for modeling software testing and fault detection...
In this paper, the forecasting algorithm employs wavelet function to replace sigmoid function in the hidden layer of Back-Propagation Neural Network. And a Wavelet Neural Network prediction model is established to predict Anode Effect (the most typical fault) through forecasting the change rate of cell resistance. The authors have developed forecasting software based on platform of Visual Basic 6...
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