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Regression analysis is one of the components of data mining techniques. Various regression algorithms have been proposed to mine the data efficiently and to propose a suitable business model. Every algorithm caters to a particular need and not necessarily produces the best fit futuristic model for all types of data. On the other hand, todays Web is expanding rapidly and affecting all aspects of our...
The goal of this research is to identify the significant factors affecting the firm performance and estimate the system behavior in different operating conditions. By determining the statistical relations of the productivity and effectiveness of the firm with these factors, a decision-making framework can be provided to improve the system performance within the competitive strategy of the whole supply...
Regression analysis, which is an effective tool of scientific prediction and management, is an important branch of statistics. According to the analysis of the current regression, we establish a quasi-linear regression model (QRM) based on the concept of quasi-linear function. Then, we got the solution of QRM by combining with genetic algorithm (GA). And its characteristic is proved through an example...
Regression models have been defined with the help of case studies, carried out for residential and commercial buildings in Sweden, for energy consumption patterns and predictions. Regression models are used as an input to Monte Carlo simulation technique to carry out energy predictions and future energy demand and supply patterns. Model is developed on the basis of historical data for four years:...
Decision making under uncertainty is a critical problem in the field of software engineering. Predicting the software quality or the cost/ effort requires high level expertise. AI based predictor models, on the other hand, are useful decision making tools that learn from past projects' data. In this study, we have built an effort estimation model for a multinational bank to predict the effort prior...
Three methods are used to forecast the Chinese Civil Aviation Transportation Market over the next 20 years, namely international and domestic comparison, elastic coefficient method, and the regression method. The results show average annual growth rates, reasonable waving Intervals and the prediction values of the total transport turnover volume, the passenger traffic turnover volume, and freighter...
By combining information visualization theory, multiple regression analysis theory and Java programming language, this paper constructed a cost prediction and visualization control system for enterprises. With the characteristic of human-computer interaction, this system can predict the costs at a specific point of time, which can help decision makers with their decision-making. In addition, the system...
The Remaining Useful Life(RUL) prediction of the equipment plays a significant role in maintenance management. The accurate RUL prediction based on the current and previous health condition of the equipment is essential to make a timely maintenance decision for failure avoidance. In this paper, we presented a novel RUL forecasting method of Proportional Hazards Model (PHM) assembled with Support Vector...
Forecasting is inferring its future according to evolution law of the things and it can offer scientific basis for decision-making. This paper first introduces four popular forecasting methods, that is time series forecasting, grey forecasting, regression forecasting and combined forecasting, then applies them to the traffic volume and freight volume in Tianjin's transportation, at last, compares...
The pedestrian violation crossing behavior (PVCB) of urban arterial road is the main object of the study. On the basis of measured data and qualitative analysis of PVCB influence factors (IF), the main factors are extracted by using factor analysis and the PVCB IF indexes system is established. Finally, adopting logistics regression model, a decision-making model of PVCB in crosswalk of signalized...
Evaluation of construction projects is an important task for management of construction projects. An accurate forecast is required to enable supporting the investment decision and to ensure the project's feasible at the minimal cost. So controlling and rationally determining the construction cost plays the most important roles in the budget management of the construction project. Ways and means have...
This article sets up a supply chain performance prediction model based on rough sets and support vector regression machine (SVR) from knowledge discovery and data mining perspectives. According to a supply chain performance prediction example, the index can be reduced based on balanced scorecard system, and input the reduction index to SVR for training. Then, the forecast sample are put in the model...
Defect prediction is an important task in the mining of software repositories, but the quality of predictions varies strongly within and across software projects. In this paper we investigate the reasons why the prediction quality is so fluctuating due to the altering nature of the bug (or defect) fixing process. Therefore, we adopt the notion of a concept drift, which denotes that the defect prediction...
This paper employs data warehouse theory, theory of decision support system, and the ASP.Net technology to implement the power marketing DSS (decision-making support system) and put forward the realization of entire system architecture. In the data layer, various data from applications which are running for the power business are integrated to establish the data warehouse, and then we build the multi-dimensional...
In deregulated power markets, forecasting electricity loads is one of the most essential tasks for system planning, operation and decision making. Based on an integration of two machine learning techniques: a hybrid evolutionary algorithm which combines PSO and Artificial Fish Swarm Algorithm Search approach based on test-sample error estimate criterion (PSO-AFSAS-TEE) and support vector regression...
In construction cost forecasting system, a great many uncertain factors effect the cost decision-making, so it is difficult to do effective forecasting by using traditional methods such as time series approach, regression analysis. In this paper, a nonlinear model based on RBF neural network is presented. There are some ameliorated measures in leaning algorithm of radial basis function (RBF) neural...
With the deterioration of primary energy market supply, it is important to optimize the raw material buying and dispatching. The annual electric power consumption is one of the most important decision making basis to realize this. Because of the characters of observations, OLS method and neural network model are all not suit for this. PLS extract variables one by one from few historical data. Under...
To improve the predictive ability of a fuzzy neural network prediction model, the re-selection is made, by means the rough set attribute reduction, of the correlated prognostic factors that have been chosen and the re-selected factors are treated by blurring as model input, thereby establishing a new-type fuzzy neural network predictive model. Experiments are conducted for approximately two months...
Knowledge of the condition of power transformer winding insulation paper is fundamental to making optimum asset replacement decisions in the power industry. The ability to assess the aged condition of Kraft paper quickly and non-destructively using portable instrumentation would significantly increase the opportunities for gaining this knowledge. Insulation paper degrades over time in-service and...
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