Effort estimation for object-oriented system using artificial intelligence techniques

Software effort estimation is a vital task in software engineering. The importance of effort estimation becomes critical during early stage of the software life cycle when the details of the software have not been revealed yet. The effort involved in developing a software product plays an important role in determining the success or failure. With the proliferation of software projects and the heterogeneity in their genre, there is a need for efficient effort estimation techniques to enable the project managers to perform proper planning of the Software Life Cycle activates. In the context of developing software using object-oriented methodologies, traditional methods and metrics were extended to help managers in effort estimation activity. There are basically some points approach, which are available for software effort estimation such as Function Point, Use Case Point, Class Point, Object Point, etc. In this thesis, the main goal is to estimate the effort of various software projects using Class Point Approach. The parameters are optimized using various artificial intelligence (AI) techniques such as Multi-Layer Perceptron (MLP), K-Nearest Neighbor Regression (KNN) and Radial Basis Function Network(RBFN), fuzzy logic with various clustering algorithms such as the Fuzzy C-means (FCM) algorithm, K-means clustering algorithm and Subtractive Clustering (SC) algorithm, such as to achieve better accuracy. Furthermore, a comparative analysis of software effort estimation using these various AI techniques has been provided. By estimating the software projects accurately, we can have software with acceptable quality within budget and on planned schedules

Software Development Effort Estimation Using Regression Fuzzy Models

Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingly popular in the field. Fuzzy logic models, in particular, are widely used to deal with imprecise and inaccurate data. The main goal of this research was to design and compare three different fuzzy logic models for predicting software estimation effort: Mamdani, Sugeno with constant output and Sugeno with linear output. To assist in the design of the fuzzy logic models, we conducted regression analysis, an approach we call regression fuzzy logic. State-of-the-art and unbiased performance evaluation criteria such as standardized accuracy, effect size and mean balanced relative error were used to evaluate the models, as well as statistical tests. Models were trained and tested using industrial projects from the International Software Benchmarking Standards Group (ISBSG) dataset. Results showed that data heteroscedasticity affected model performance. Fuzzy logic models were found to be very sensitive to outliers. We concluded that when regression analysis was used to design the model, the Sugeno fuzzy inference system with linear output outperformed the other models.Comment: This paper has been accepted in January 2019 in Computational Intelligence and Neuroscience Journal (In Press

Rule Optimization of Fuzzy Inference System Sugeno using Evolution Strategy for Electricity Consumption Forecasting

The need for accurate load forecasts will increase in the future because of the dramatic changes occurring in the electricity consumption. Sugeno fuzzy inference system (FIS) can be used for short-term load forecasting. However, challenges in the electrical load forecasting are the data used the data trend. Therefore, it is difficult to develop appropriate fuzzy rules for Sugeno FIS. This paper proposes Evolution Strategy method to determine appropriate rules for Sugeno FIS that have minimum forecasting error. Root Mean Square Error (RMSE) is used to evaluate the goodness of the forecasting result. The numerical experiments show the effectiveness of the proposed optimized Sugeno FIS for several test-case problems. The optimized Sugeno FIS produce lower RMSE comparable to those achieved by other well-known method in the literature

A Technique to Stock Market Prediction Using Fuzzy Clustering and Artificial Neural Networks

Stock market prediction is essential and of great interest because successful prediction of stock prices may promise smart benefits. These tasks are highly complicated and very difficult. Many researchers have made valiant attempts in data mining to devise an efficient system for stock market movement analysis. In this paper, we have developed an efficient approach to stock market prediction by employing fuzzy C-means clustering and artificial neural network. This research has been encouraged by the need of predicting the stock market to facilitate the investors about buy and hold strategy and to make profit. Firstly, the original stock market data are converted into interpreted historical (financial) data i.e. via technical indicators. Based on these technical indicators, datasets that are required for analysis are created. Subsequently, fuzzy-clustering technique is used to generate different training subsets. Subsequently, based on different training subsets, different ANN models are trained to formulate different base models. Finally, a meta-learner, fuzzy system module, is employed to predict the stock price. The results for the stock market prediction are validated through evaluation metrics, namely mean absolute deviation, mean square error, root mean square error, mean absolute percentage error used to estimate the forecasting accuracy in the stock market. Comparative analysis is carried out for single Neural Network (NN) and existing technique neural. The obtained results show that the proposed approach produces better results than the other techniques in terms of accuracy

FLANN Based Model to Predict Stock Price Movements of Stock Indices

Financial Forecasting or specifically Stock Market prediction is one of the hottest fields of research lately due to its commercial applications owing to the high stakes and the kinds of attractive benefits that it has to offer. Forecasting the price movements in stock markets has been a major challenge for common investors, businesses, brokers and speculators. As more and more money is being invested the investors get anxious of the future trends of the stock prices in the market. The primary area of concern is to determine the appropriate time to buy, hold or sell. In their quest to forecast, the investors assume that the future trends in the stock market are based at least in part on present and past events and data [1]. However financial time-series is one of the most ‘noisiest’ and ‘non-stationary’ signals present and hence very difficult to forecas

Comparison of Performance Analysis using Different Neural Network and Fuzzy Logic Models for Prediction of Stock Price

Analysis and prediction of stock market is very interesting as this helps the financial experts in decision making and in turn profit making. In this thesis simple feed forward neural network (FFNN) model is initially considered for stock market prediction and its result is compared with Radial basis function network (RBFN) model, fuzzy logic model and Elman network model. A FFNN model can fit into any finite input-output mapping problem where the FFNN consists of one hidden layer and enough neurons in the hidden layer. RBFN are the Artificial Neural Networks (ANN) in which Radial Basis Functions (RBF) are used as activation functions. In this thesis, Levenberg-Marquardt Backpropagation algorithm is used to train the data for both FFNN and Elman network. For Fuzzy Logic, Sugeno type Fuzzy Inference System (FIS) is used to model the prediction process. Different Clustering methods are used to nd the optimal parameters of RBF. These techniques were tested with published stock market data of National Stock Exchange of India Ltd. for validation

Dynamic neuro-fuzzy systems for rainfall-runoff modelling

Urbanization has significant impact on the hydrological processes that have caused an increase in magnitude and frequency of floods; therefore, a reliable rainfall-runoff model will be helpful to estimate discharge for any watershed management plans. Beside physically-based models, the data driven approaches have been also used frequently to model the rainfall-runoff processes. Neuro-fuzzy systems (NFS) as one of the main category of data-driven models are common in hydrological time series modeling. Among the different algorithms, Adaptive network-based fuzzy inference system (ANFIS) is well-practiced in hydrological modeling. ANFIS is an offline model and needs to be retrained periodically to be updated. Therefore, an NFS model that can employ different learning process to overcome such problem is needed. This study developed dynamic evolving neuro fuzzy inference system (DENFIS) model for event based and continuous rainfallrunoff modeling and the results were compared with the existing models to check model capabilities. DENFIS evolves through incremental learning in which the rulebase is evolved after accommodating each individual new input data and benefitted from local learning implemented through the clustering method, Evolving Clustering Method (ECM). In this study, extreme events were extracted from the historical hourly data of selected tropical catchments of Malaysia. The DENFIS model performances were compared with ANFIS, the hydrologic modeling system (HECHMS) and autoregressive model with exogenous inputs (ARX) for event based rainfall-runoff modeling. DENFIS model was also evaluated against ANFIS for continuous rainfall-runoff modeling on a daily and hourly basis, multi-step ahead runoff forecasting and simulation of the river stage. The average coefficients of efficiency (CE) obtained from DENFIS model for the events in testing phase were 0.81, 0.79 and 0.65 for Lui, Semenyih and Klang catchments respectively which were comparable with ANFIS and HEC-HMS and were better than ARX. The CEs obtained from DENFIS model for hourly continuous were 0.93, 0.92 and 0.62 and for daily continuous were 0.73, 0.67 and 0.54 for Lui, Semenyih and Klang catchments respectively which were comparable to the ones obtained from ANFIS. The performances of DENFIS and ANFIS were also comparable for multistep ahead prediction and river stage simulation. This study concluded that less training time and flexibility of the rule-base in DENFIS is an advantage compared to an offline model such as ANFIS despite the fact that the results of the two models are generally comparable. However, the learning algorithm in DENFIS was found to be potentially useful to develop adaptable runoff forecasting tools