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Modern medical diagnosis equipments included with digital signal processing capabilities have been used for fast and accurate diagnosis of brain structure abnormalities. In this paper a multi resolution based noise removal in magnetic resonance images for abnormality detection and recognition within the brain has been proposed. Wavelet and curvelet based multi resolution approximation has been used to decompose the inter-object relationships into different levels of detail. Contourlet based multi resolution approximation is presented in this work for better abnormality detection. Comparison of extracted feature points between the reference image and the image under study has been made in detection of the abnormality.

In order to succeed in our everyday life, efficient performance of the power system is of utmost importance, and hence, all the sub-sectors of the power system should be analyzed for the purpose of achieving efficiency and accuracy. It must also be remembered that load forecasting assists a lot to improve the power system. Moreover, it contributes substantially to formulate logical approaches for emerging short-term load forecasting (STLF) for all days including the distinct days and make them follow a uniform standard. Of all the techniques which have been applied so far, honey bee-optimized Euclidean norm, based on fuzzy inference system, is used for identifying the problems, and in addition, support vector classifier is utilized to prepare the STLF models. Parameters — temperature, humidity, monsoons, wind, cloud density, dew point, season, hour of the day, day of the week, distinct day, and holiday — have been taken into account for the current study. A well-prepared database can be used for regression which will be of immense help to forecast the load using artificial intelligence. For every day of a month, the MAPE is computed (using the forecasted and actual hourly values) in order to observe the accuracy of STLF. The planned method has been very successful for the load forecast of all days for all seasons. The forecast has been done using the technique for a real time data of one year (test forecast year) with a historical dataset collected for a period of two years, and the results obtained for all seasons have been found to be satisfactory. STLF has helped to find better values due to its pace, and become healthier than other methods already in practice. With the advent of smart grid, the data will be accessible at more granular level as smart meters have capability to provide consumer load, usage data on-line and this facility will be of great help to utility operators and planners for operations on-line. How to use the data available from smart meters for better STLF is a challenging task and it would draw much attention for future research.

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The usage rate of electric vehicles (EVs) is gradually increasing. Recharging of EVs should be carried out repeatedly over time, and the energy needed for this is high and increasing. With the present infrastructure, we cannot supply the required energy, and therefore, we need to implement a model that expands the power grid to satisfy our energy requirements. This paper proposes a convolutional neural network-based dynamic capacity expansion (CNN-DCE) for EV charging. Flower pollination optimization algorithm (FPOA) was used to improve the hyperparameters of CNN during training. The main aim is to reduce the cost of installing additional capacity resources and to reduce the operational cost. To cope with the load growth, different capacity resources are installed at different years of the planning boundary. Five statistical indices, such as mean squared error, mean absolute error, correlation coefficient, and scatter index, are used to evaluate the performance of CNN. The capacity expansion plan in the microgrid is achieved by expanding the energy of battery energy storage systems, microturbines, and solar and wind energy systems. The queuing delay for the EVs waiting in a queue for recharging has been considered. The performance of the proposed CNN-DCE is studied and compared with three other state-of-the-art methods. The results show that the resources reduce the planning cost to 26% for the short-term planning horizon, the long-term plan has 150% of the expansion, and the wind energy system covers 48% of the expansion cost.