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Tuberculosis (TB) is a highly infectious disease and is one of the major health problems all over the world. The accurate detection of TB is a major challenge faced by most of the existing methods. This work addresses these issues and developed an effective mechanism for detecting TB using deep learning. Here, the color space transformation is applied for transforming the red green and blue image to LUV space, where L stands for luminance, U and V represent chromaticity values of color images. Then, adaptive thresholding is carried out for image segmentation and various features, like coverage, density, color histogram, area, length, and texture features, are extracted to enable effective classification. After the feature extraction, the size of the features is reduced using principal component analysis. The extracted features are subjected to fractional crow search‐based deep convolutional neural network (FC‐SVNN) for the classification. Then, the image level features, like bacilli count, bacilli area, scattering coefficients and skeleton features are considered to perform severity detection using proposed adaptive fractional crow (AFC)‐deep CNN. Finally, the inflection level is determined using entropy, density and detection percentage. The proposed AFC‐Deep CNN algorithm is designed by modifying FC algorithm using self‐adaptive concept. The proposed AFC‐Deep CNN shows better performance with maximum accuracy value as 0.935.

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Increased electric energy demand is the major factor that cracks the researchers to focus on electrical energy demand to meet the challenge. Accordingly, the power loss should be minimized. Due to this, Distribution Flexible Alternating Current Transmission System (DFACTS) and Distributed Generation (DG) must be placed in an appropriate way in distribution systems. Fuzzy-Lightning Search Algorithm (FLSA) has been proposed to minimize the power loss in the radial distribution network by solving the optimal placement problem of Distribution STATic COMpensator (DSTATCOM) as DFACTS device and photo voltaic (PV) array unit as DG. The improved voltage profile values, less power loss and reduced stability problems have been attained by using FLSA. The proposed FLSA technique is evaluated through IEEE 30-bus system with MATLAB software. The power flow investigation has been done by Newton Raphson method. Optimum siting of DSTATCOM and PV array induced outstandingly decreased the losses to 7.0073 kW. The accomplished objectives and results have validated the FLSA is more advantageous than other optimization techniques.

The fundamental and important preprocessing stage in image processing is the image contrast enhancement technique. Histogram equalization is an effective contrast enhancement technique. In this paper, a histogram equalization based technique called quadrant dynamic with automatic plateau limit histogram equalization (QDAPLHE) is introduced. In this method, a hybrid of dynamic and clipped histogram equalization methods are used to increase the brightness preservation and to reduce the overenhancement. Initially, the proposed QDAPLHE algorithm passes the input image through a median filter to remove the noises present in the image. Then the histogram of the filtered image is divided into four subhistograms while maintaining second separated point as the mean brightness. Then the clipping process is implemented by calculating automatically the plateau limit as the clipped level. The clipped portion of the histogram is modified to reduce the loss of image intensity value. Finally the clipped portion is redistributed uniformly to the entire dynamic range and the conventional histogram equalization is executed in each subhistogram independently. Based on the qualitative and the quantitative analysis, the QDAPLHE method outperforms some existing methods in literature.