In this paper, we propose an automated approach that combines the generalized discriminant analysis (GDA) as feature reduction scheme with radial basis function (RBF) kernel and the online sequential extreme learning machine (OSELM) having Sigmoid, Hardlim, RBF and Sine activation function as binary classifier for detection of congestive heart failure (CHF) and coronary artery disease (CAD). For this analysis, 13 nonlinear features as Correlation Dimension (CD), Detrended Fluctuation Analysis (DFA) as DFA-[Formula: see text]1 and DFA-[Formula: see text]2, Bubble Entropy (BBEn), Sample Entropy (SampEn), Dispersion Entropy (DISEn), Lempel–Ziv Complexity (LZ), Sinai Entropy (SIEn), Improved Multiscale Permutation Entropy (IMPE), Hurst Exponent (HE), Permutation Entropy (PE), Approximate Entropy (ApEn) and Standard Deviation (SD1/SD2) were extracted from Heart Rate Variability (HRV) signals. For validation of proposed method, HRV data were obtained from standard database of normal sinus rhythm (NSR), CHF and CAD subjects. Numerical experiments were done on the combination of database sets such as NSR-CAD, CHF-CAD and NSR-CHF subjects. The simulation results show a clear difference in combination of database sets by using GDA having RBF, Gaussian kernel function and OSELM binary classifier having Sigmoid, RBF and Sine activation function and achieved an accuracy of 98.17% for NSR-CAD, 100% for NSR-CHF and CAD-CHF subjects.

In the age of information technology, a large number of images are generated at 24/7 which leads to a growing interest for searching out similar images from the large databases/ data warehouses. For searching an image from the database, images need to be described by certain features. The most important feature to describe an image is its shape. Now-adays, shape is used for image retrieval.

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In this paper, Artificial Neural Network, one of the Artificial Intelligence (AI) techniques, for the Volt / Var control in power distribution systems with dispersed generation (DG) is proposed. Artificial neural networks have been considered due to their ability for real time control, simpler calculations and adaptability to different operating conditions. Neuro-controllers are much more effective, fast acting than conventional controllers. Neural network for controlling Step voltage regulator (SVR) with line rise compensation (LRC) /line drop compensation (LDC) function has been presented. The neural network based controller has been simulated for a radial distribution system with DG and the neurocontroller shows promising results after testing.

In this paper, one of the evolutionary algorithm based method, Non-Dominated Sorting Genetic Algorithm (NSGA) has been presented for the Volt / Var control in power distribution systems with dispersed generation (DG). The proposed method is better suited for volt/var control problems. Genetic algorithm approach is used due to its broad applicability, ease of use and high accuracy. A multi-objective optimization problem has been formulated for the volt/var control of the distribution system. The non-dominated sorting genetic algorithm based method proposed in this paper, alleviates the problem of tuning the weighting factors required in solving the multi-objective volt/var control optimization problems. Based on the simulation studies carried out on the distribution system, the proposed scheme has been found to be simple, accurate and easy to apply to solve the multiobjective volt/var control optimization problem of the distribution system with dispersed generation.

Background: Finding region of interest in an image and content-based image analysis has been a challenging task for last two decades. With the advancement in image processing, computer vision field and huge amount of image data generation, to manage this huge amount of data Content-Based Image Retrieval System (CBIR) has attracted several researchers as a common technique to manage this huge amount of data. It is an approach of searching user interest, based on visual information present in an image. The requirement of high computation power and huge memory limits deployment of CBIR technique in real-time scenarios. Objective: In this paper an advanced deep learning model is applied for CBIR on facial image data. We design a deep convolution neural network architecture where activation of convolution layer is used for feature representation and include max pooling as feature reduction technique. Furthermore, our model uses partial feature mapping as image descriptor to incorporate the property that facial image contains repeated information. Method: Existing CBIR approaches primarily consider colour, texture and low-level features for mapping and localizing image segments. While deep learning has shown high performance in numerous fields of research, its application in CBIR is still very limited. Human face contains significant information to be used in a content driven task and applicable to various applications of computer vision and multimedia systems. In this research work, a deep learning-based model has been discussed for content-based image retrieval (CBIR). In CBIR, there are two important things 1) classification and 2) retrieval of image based on similarity. For the classification purpose a four-convolution layer model has been proposed. For the calculation of the similarity Euclidian distance measure has been used between the images. Results: Proposed model is completely unsupervised, and it is fast and accurate in comparison to other deep learning models applied for CBIR over facial dataset. The proposed method provided satisfactory results from the experiment. It outperforms other CNN-based models and other unsupervised techniques used for CBIR. The proposed method provided satisfactory results from the experiment and it outperforms other CNN-based models such as VGG16, Inception V3, ResNet50 and MobileNet. Moreover, the performance of proposed model has been compared with pre-trained models in terms of accuracy, storage space and inference time.

A solar collector is a simple and cheap device that is capable of collecting and converting solar radiation into useful heat energy. The thermal performance of the solar collectors can be enhanced significantly with the suspension of nanoparticles in the base fluid. A novel design for a solar-assisted water heater (SWH) is proposed in the current study and the effect of nanofluid has been investigated on the thermal efficiency of the SWH. The use of nanofluid is one of the prominent methods in comparison to other techniques for improving the performance of solar collectors and therefore, the base working fluid i.e. water is mixed with the alumina nanoparticles of average particle size of 30 nm, and they are assumed to be spherical. The flow and thermal characteristics of nanofluid through the solar water heater are simulated numerically with the help of the Eulerian-Eulerian two-phase model using the finite volume method (FVM). The commercial package i.e. ANSYS Fluent is used for modeling the problem under transient conditions with a pressure-based solver. In comparison to a conventional flat plate collector, the proposed solar water heater consists corrugated absorber-plate and the effect of radius of curvature of corrugation has been investigated on the heat transfer and collector efficiency. With the proposed design the heat transfer area available with the riser tubes increases remarkably and it leads to a 43% and 14% increase in heat transfer augmentation and collector efficiency, in comparison to the conventional solar water heater.

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