Vignan University

About: Vignan University is a education organization based out in Guntur, Andhra Pradesh, India. It is known for research contribution in the topics: Computer science & Control theory. The organization has 1138 authors who have published 1381 publications receiving 7798 citations.

Experimental investigation on solid lubricant supply methodology in turning process

Abstract: Textured cutting insert filled with solid lubricants is one of the best alternative cooling methods in metal cutting industries. The problem associated with textured insert is the supply of solid l...

In-Vitro Studies on Antitumour and Antimicrobial Activities of Methanolic Kernel Extract of Mangifera Indica L. Cultivar Banganapalli

Abstract: Mangifera indica L. belongs to the family Anacardiaceae and is considered as “King of all Fruits”. Mango kernels are discarded as waste after the industrial processing and it has several proven medicinal benefits. Attempts were made to study its antitumour and antimicrobial activities. In the current research work, 15 local cultivars of Mangifera indica L. were collected with a motto to screen the best cultivar having high total phenolic content, flavonoid content, antitumour property and antimicrobial activity. Banganapalli cultivar of mango showed high total phenolic content and total flavonoid content i.e. 63.5±1.1 mg GAE/g and 16.7±0.5 mg quercetin/g followed by Royal special cultivar (TPC-58.7±0.6 mg GAE/g TFC-16.2±0.6 mg quercetin/g). Mangifera indica L. cultivar Banganapalli which showed highest total phenolic content and total flavonoid content was screened for its antitumour and antimicrobial properties. Antitumour property was tested by using potato disc assay which recorded 40.12% tumour inhibition percentage. Antimicrobial activity was assessed by agar diffusion method by taking 3 test microorganisms viz. Bacillus subtilis subsp. subtilis DSM 10, Staphylococcus aureus MTCC 737 and Escherichia coli MTCC 46. The measured area of inhibition is around Staphylococcus aureus MTCC 737 in 8.5±0.3 mm followed by E.coli MTCC 46 (8.2±0.3 mm) and Bacillus subtilis sub subtilis (6.6±0.5 mm). The present study showed that the mango kernels which were generally discarded as waste has antitumour and antibacterial properties and further studies need to be carried out.

S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

Abstract: This study forecasts the return and volatility dynamics of SP tests including variance ratio, Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski Phillips Schmidt and Shin; and Autoregressive Integrated Moving Average (ARIMA). The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series, using the ARIMA model. The results reveal that the mean returns of both indices are positive but near zero. This is indicative of a regressive tendency in the long-term. The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values, with few deviations. Hence, the ARIMA model is capable of predicting medium- or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

Development of GA-based models for simulating the ground vibration in mine blasting

Abstract: Rock blasting is a well-known and common method for the removal of rock masses from an excavation in surface mines and civil projects. Ground vibration is the most hazardous effect induced by blasting operations. Therefore, the level of the blast-induced ground vibration needs to be predicted with a good level of the accuracy. The goal of this paper is to propose two novel practical intelligent models to approximate the ground vibration through genetic algorithm (GA). For comparison aims, the Roy and Rai-Singh empirical models were also employed. The requirement datasets were collected from the Shur river dam, in Iran. Specific charge, distance from the blast face and weight charge per delay were used as the input/independent parameters and peak particle velocity (PPV) was used as the output/dependent parameter. In total, 85 datasets including the mentioned parameters were prepared. Then, the models performance was assessed using statistical indicators, i.e., coefficient correlation (R2) and root mean squared error. According to the obtained results, it was concluded that GA-based models, with the R2 of 0.977 and 0.974 obtained from GA-power and GA-linear models, provide relatively closer predictions as compared to Roy and Rai-Singh empirical models, with the R2 of 0.936 and 0.923, respectively.

An Efficient Automatic Brain Tumor Classification Using LBP Features and SVM-Based Classifier

Abstract: Brain tumor detection is a tedious task which involves a lot of time and expertise. With each passing year, the world has always witnessed an increase in the number of cases of brain tumor. It is thereby apparent; that it is becoming difficult for the doctors to detect tumors in MRI scans, not only because of the increase in numbers but also, because of the complexity of the cases. So, the research in this domain is still ongoing as the world is in search of an exemplary and flawless method for an automated brain tumor detection technique. In this paper, we introduced a novel architecture for brain tumor detection which detects whether the given MR image is malignant or benign. Preprocessing, segmentation, dimension reduction, and classification are the major phases of our proposed architecture. On the MR images, T2-weighted preprocessing is applied to convert into grayscale images. In the next stage, features are extracted from the preprocessed images by applying local binary pattern (LBP) technique. Principal component analysis (PCA) is used to discard uncorrelated features. This reduced feature set is fed to the support vector machine (SVM) classifier to predict whether the given MR image is normal (benign) or abnormal (malignant). Experimental results on benchmark MR image datasets exhibit that the proposed method gives promising accuracy when compared to the existing work though it is simple.