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Background; Magnesium has been implicated in the pathogenesis of acute myocardial infarction and its related complication like arrhythmia. Magnesium helps in myocardial metabolism, inhibits calcium accumulation and myocardial cell death. It has role in improving the vascular tone, peripheral vascular resistance, after load and cardiac output, reduces cardiac arrhythmias and improves lipid metabolism. Magnesium reduces vulnerability to oxygen derived free radicals, improves endothelial function and inhibits platelet function including platelet aggregation and adhesion. Objective: To know the relationship between the serum magnesium levels and arrhythmias in patients with acute myocardial infarction.70 cases of acute myocardial infarction, admitted in a tertiary care hospital over a period of 2 years i.e., between October 2015 to October 2017. Results: There is a signicant difference in the magnesium levels in patients with arrhythmias and without arrhythmias. Conclusion: In acute myocardial infarction, patients with low magnesium levels are more prone to get arrhythmias. That is the reason magnesium treatment can be considered in patients of acute myocardial infarction with low magnesium levels.

Aim of study software defect is a flaw, miscalculation, or failure, in a computer program or framework delivering an inappropriate or surprising result, or making it perform in an unintended way. Software Defect Prediction (SDP) finds defective modules in software. The final product ought to have as few defects as possible to create top notch software. Early software defects discovery prompts diminished development costs and rework effort and better software. Software metrics guarantee quantitative methods to survey software quality. Software metrics are helpful to software process and product metrics. Thus, a defect prediction study is critical to guarantee quality software and software metric aggregation. In this study, the efficiency of classifier for SDP is assessed. Diverse classifiers like Naive Bayes, K Nearest Neighbor (KNN), C4.5 and Multilayer Perceptrons Neural Network (MLPNN) are assessed for SDP.

The need for a general purpose Content Based Image Retrieval (CBIR) system for huge image databases has attracted information-technology researchers and institutions for CBIR techniques development. These techniques include image feature extraction, segmentation, feature mapping, representation, semantics, indexing and storage, image similarity-distance measurement and retrieval making CBIR system development a challenge. Since medical images are large in size running to megabits of data they are compressed to reduce their size for storage and transmission. This paper investigates medical image retrieval problem for compressed images. An improved image classification algorithm for CBIR is proposed. In the proposed method, RAW images are compressed using Haar wavelet. Features are extracted using Gabor filter and Sobel edge detector. The extracted features are classified using Partial Recurrent Neural Network (PRNN). Since training parameters in Neural Network are NP hard, a hybrid Particle Swarm Optimization (PSO) – Cuckoo Search algorithm (CS) is proposed to optimize the learning rate of the neural network.

Microarray technology allows the simultaneous monitoring of thousands of genes in parallel. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Enhancement, Gridding, Segmentation and Intensity extraction are important steps in microarray image analysis. This paper presents a noise removal method in microarray images based on Variational Mode Decomposition (VMD). VMD is a signal processing method which decomposes any input signal into discrete number of sub-signals (called Variational Mode Functions) with each mode chosen to be its band width in spectral domain. First the noisy image is processed using 2-D VMD to produce 2-D VMFs. Then Discrete Wavelet Transform (DWT) thresholding technique is applied to each VMF for denoising. The denoised microarray image is reconstructed by the summation of VMFs. This method is named as 2-D VMD and DWT thresholding method. The proposed method is compared with DWT thresholding and BEMD and DWT thresholding methods. The qualitative and quantitative analysis shows that 2-D VMD and DWT thresholding method produces better noise removal than other two methods.

Management of large software projects includes estimating software development effort as the software industry is unable to provide a proper estimate of effort, time and development cost. Though many estimation models exist for effort prediction, a novel model is required to obtain highly accurate estimations. This paper proposes a Generalized Regression Neural Network to utilize improved software effort estimation for COCOMO dataset. In this paper, the Mean Magnitude Relative Error (MMRE) and Median Magnitude Relative Error (MdMRE) are used as the evaluation criteria. The proposed Generalized Regression Neural Network is compared with various techniques such as M5, Linear regression, SMO Polykernel and RBF kernel.