Journal of Algorithms & Computational Technology 

About: Journal of Algorithms & Computational Technology is an academic journal published by SAGE Publishing. The journal publishes majorly in the area(s): Image segmentation & Multi-swarm optimization. It has an ISSN identifier of 1748-3018. Over the lifetime, 512 publications have been published receiving 2955 citations. The journal is also known as: Journal of algorithms and computational technology.

Prediction of benign and malignant breast cancer using data mining techniques

Abstract: Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industr...

Parallel Support Vector Machines Applied to the Prediction of Multiple Buildings Energy Consumption

Abstract: Analyzing the energy performance in a building is an important task in energy conservation. To accurately predict the energy consumption is difficult in practice since the building is a complex system with many parameters involved. To obtain enough historical data of energy uses and to find out an approach to analyze them become mandatory. In this paper, we propose a simulation method with the aim of obtaining energy data for multiple buildings. Support vector machines method with Gaussian kernel is applied to obtain the prediction model. For the first time, a parallel implementation of support vector machines is used to accelerate the model training process. Our experimental results show very good performance of this approach, paving the way for further applications of support vector machines method on large energy consumption datasets.

Feature Selection for Predicting Building Energy Consumption Based on Statistical Learning Method

Abstract: Machine learning methods are widely studied and applied to predict building energy consumption. Since the factors associated with building energy behaviors are quite abundant and complex, this paper investigates for the first time how the selection of subsets of features influence the model performance when statistical learning method is adopted to derive the model. In this paper the optimal features are selected based on the feasibility of obtaining them and on the scores they provide under the evaluation of some filter methods. The selected subset is then evaluated on three data sets by support vector regression involving two kernel functions: radial basis function and polynomial function. Experimental results confirm the validity of the selected subset and show that the proposed feature selection method can guarantee the prediction accuracy and reduces the computational time for data analyzing.

Sigmis: A Feature Selection Algorithm Using Correlation Based Method

Abstract: Feature Selection is one of the preprocessing steps in machine learning tasks. Feature Selection is effective in reducing the dimensionality, removing irrelevant and redundant feature. In this paper, we propose a new feature selection algorithm (Sigmis) based on Correlation method for handling the continuous features and the missing data. Empirical comparison with three existing feature selection algorithms using UCI data sets shows that the proposed system is very effective and efficient in selecting the feature set.

Finite difference approximation for two-dimensional time fractional diffusion equation

Abstract: Fractional diffusion equations have recently been used to model problems in physics, hydrology, biology and other areas of application. In this paper, we consider a two-dimensional time fractional diffusion equation (2D-TFDE) on a finite domain. An implicit difference approximation for the 2D-TFDE is presented. Stability and convergence of the method are discussed using mathematical induction. Finally, a numerical example is given. The numerical result is in excellent agreement with our theoretical analysis.