No abstract

There are many approaches for accurate and automatic classification of brain MRI. In this paper, a simple approach for automatic detection and classification is presented. Artificial Neural Network has been utilized for brain MRI classification as malignant or benign. The approach consists of three stages namely pre processing, features' extraction and classification. In pre-processing stage, filters are applied for the removal of noise. In the features' extraction phase, color moments are extracted as mean features from the MRI images and the color moments extracted are presented to simple feed forward artificial neural network for classification. The method was applied using total 70 images with 25 normal images and 45 abnormal images. The classification accuracy was found to be 88.9% for training data, 94.9% for validation data and 94.2% for testing data whereas the overall accuracy of 91.8% was observed.

Abstract-In this paper, new statistical features based approach (SFBA) for hourly energy consumption prediction using Multi-Layer Perceptron is presented. The model consists of four stages: data retrieval, data preprocessing, feature extraction and prediction. In the data retrieval stage, historical hourly consumed energy data has been retrieved from the database. During data preprocessing, filters have been applied to make the data more suitable for further processing. In the feature extraction stage, mean, variance, skewness, and kurtosis are extracted. Finally, Multi-Layer Perceptron has been used for prediction. For experimentation with MultiLayer Perceptron with different training algorithms, a final model of the network was designed in which the scaled conjugate gradient (trainscg) was used as a network training function, tangent sigmoid (Tansig) as a hidden layer transfer function and linear function as an output layer transfer function. For hourly energy consumption prediction, a total of six weeks data of ten residential buildings has been used. To evaluate the performance of the proposed approach, Mean Absolute Error (MAE), Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), evaluation measurements were applied.

In order to manage efficiently the energy production, storage and management system, it is very important to analyze accurately the energy requirements for residential sector because the residential sector consumes a considerable amount of total energy produced. The main aim of the paper is the assurance of energy production according to the consumer demands in an efficient manner. The energy market is an important tool for setting prices between the energy producers, suppliers and the consumers. An excellent precision in the prediction of next day consumption is required for the suppliers to get good prices in the energy traded. The main aim of this paper is to facilitate the energy suppliers to make decisions for the provision of energy to different apartments according to their demand. In this paper, we have utilized K-Nearest Neighbors classifier for daily energy consumption prediction based on classification. The process consists of five stages namely data collection, data processing, prediction, and validation and performance evaluation. The historical data containing hourly consumption of 520 apartments of Seoul, Republic of Korea has been used in the experimentation. The data has been divided into different training and testing ratios and different qualitative and quantitative measures have been applied to find the performance and efficiency of the predictor. The highest accuracy has been observed for 60-40% training and testing ratio giving 95.9615% accurate results. The effectiveness of the model has been validated using 10-Fold and 5-Fold cross validation.

The energy management in residential buildings according to occupant’s requirement and comfort is of vital importance. There are many proposals in the literature addressing the issue of user’s comfort and energy consumption (management) with keeping different parameters in consideration. In this paper, we have utilized artificial bee colony (ABC) optimization algorithm for maximizing user comfort and minimizing energy consumption simultaneously. We propose a complete user friendly and energy efficient model with different components. The user set parameters and the environmental parameters are inputs of the ABC, and the optimized parameters are the output of the ABC. The error differences between the environmental parameters and the ABC optimized parameters are inputs of fuzzy controllers, which give the required energy as the outputs. The purpose of the optimization algorithm is to maximize the comfort index and minimize the error difference between the user set parameters and the environmental parameters, which ultimately decreases the power consumption. The experimental results show that the proposed model is efficient in achieving high comfort index along with minimized energy consumption.