Sir M. Visvesvaraya Institute of Technology

About: Sir M. Visvesvaraya Institute of Technology is a based out in . It is known for research contribution in the topics: Composite number & Ultimate tensile strength. The organization has 191 authors who have published 168 publications receiving 1223 citations.

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

Abstract: In medical image processing, Brain tumor segmentation plays an important role. Early detection of these tumors is highly required to give Treatment of patients. The patient’s life chances are improved by the early detection of it. The process of diagnosing the brain tumoursby the physicians is normally carried out using a manual way of segmentation. It is time consuming and a difficult one. To solve these problems, Enhanced Convolutional Neural Networks (ECNN) is proposed with loss function optimization by BAT algorithm for automatic segmentation method. The primary aim is to present optimization based MRIs image segmentation. Small kernels allow the design in a deep architecture. It has a positive consequence with respect to overfitting provided the lesser weights are assigned to the network. Skull stripping and image enhancement algorithms are used for pre-processing. The experimental result shows the better performance while comparing with the existing methods. The compared parameters are precision, recall and accuracy. In future, different selecting schemes can be adopted to improve the accuracy.

Analysis of Statistical Time-Domain Features Effectiveness in Identification of Bearing Faults From Vibration Signal

Abstract: The frequency of rolling element failures in induction motor is high and may lead to losses due to sudden downtime of machine. Researchers are fervent to identify an effective fault diagnosing scheme with less computational burden using optimum number of good discriminating features. We attempted time domain features, namely, waveform length (WL), slope sign changes (SSC), simple sign integral and Wilson amplitude for the first time in addition to established mean absolute value and zero crossing (ZC) for identification of mechanical faults of induction motor. Ten data sets are derived from publicly available vibration database of Case Western Reserve University to identify the capability of features in identification of faults under various conditions. The results are compared with six conventional features for tenfold cross validation using linear discriminant analysis, naive Bayes, and support vector machine classifiers. The results have shown that WL, WAMP, ZC, and SSC outperform other features. Furthermore, area under receiver operator characteristics curve analyses showed an average of 0.9987 with the proposed statistical features and 0.97618 with six conventional features. We also attempted to study the effect of data length and percentage of overlap in classification and found accuracy improves with increase in length but not significant beyond the window length of 3000 with 50% overlap. The proposed statistical features are validated using the brute force method and Laplaician method of feature selection and shown an average accuracy rate of 0.9936 and 0.9894, respectively.

In vitro antibacterial and cytotoxicity studies of ZnO nanopowders prepared by combustion assisted facile green synthesis

Abstract: This research work presents the synthesis, characterization, evaluation of the antibacterial and anticancer activity of ZnO nanopowders prepared by solution combustion method using the bio fuels Punica granatum L and Tamarindus indica L. The X-ray diffractograms of all the samples revealed the hexagonal Wurtzite structure with the standard JCPDS pattern of zincite [36–1451]. Surface morphology of the samples was studied by SEM. Particle shapes and sizes were determined by TEM. Qualitative phytochemical screening of the aqueous fruit extracts of Punica granatum L and T. indica L revealed the presence of many phyto-components in them. Toxicity of the nanopowders was tested on Gram-negative Escherichia coli MTCC 7410 and Pseudomonas aeruginosa MTCC 7903 by disk diffusion method. Minimum inhibitory concentration was determined by microbroth dilution technique. Anticancer activity of ZnO powders was tested against breast cancer cell line MCF-7 by MTT assay. The cytotoxicity was assessed by hemolytic activity.

Prediction of heart disease at early stage using data mining and big data analytics: A survey

Abstract: In this paper, the various technologies of data mining (DM) models for forecast of heart disease are discussed. Data mining plays an important role in building an intelligent model for medical systems to detect heart disease (HD) using data sets of the patients, which involves risk factor associated with heart disease. Medical practitioners can help the patients by predicting the heart disease before occurring. The large data available from medical diagnosis is analyzed by using data mining tools and useful information known as knowledge is extracted. Mining is a method of exploring massive sets of data to take out patterns which are hidden and previously unknown relationships and knowledge detection to help the better understanding of medical data to prevent heart disease. There are many DM techniques available namely Classification techniques involving Naive Bayes (NB), Decision tree (DT), Neural network (NN), Genetic algorithm (GA), Artificial intelligence (AI) and Clustering algorithms like K-NN, and Support vector machine (SVM). Several studies have been carried out for developing prediction model using individual technique and also by combining two or more techniques. This paper provides a quick and easy review and understanding of available prediction models using data mining from 2004 to 2016. The comparison shows the accuracy level of each model given by different researchers.