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.

Gabor based face recognition with dynamic time warping

Abstract: Many approaches are proposed for face recognition from last three decades but they are challenging the problems like illumination, pose and scaling variations. In this paper a new methodology has been proposed for improving the efficiency of face recognition based on local feature extraction using Gabor Wavelets. The extracted features are classified using non linear matching algorithm like Dynamic Time Warping (DTW). DTW is a technique which is used to identify an optimal warp between two feature vectors. Based on the constraints of DTW it provides better accuracy than the existing methods with Euclidean distance. The proposed method has given 96.19% recognition rate on Grimace face database, 86.38% of recognition rate on ORL and 90.67% on Yale like standard bench mark face databases.

Compounding and the mechanical properties of catla fish scales reinforced-polypropylene composite–from biowaste to biomaterial

Abstract: In this study, bio-waste catla fish scale (CFS) was used as a reinforcing agent in polypropylene (PP) for the fabrication of biomaterials by using a twin-screw extrusion technique. Tensile strength...

Evaluation of in Vitro Antibacterial Activity of Caralluma Lasiantha for Scientific Validation of Indian Traditional Medicine

Abstract: Caralluma lasiantha is used as a traditional medicine in India to heal body heat and inflammations. In order to find out a scientific validation for the Indian traditional knowledge, antibacterial activity of C. lasiantha extracts was studied against inflammation causing bacteria (viz., Staphylococcus aureus, Escherichia coli, Streptococcus Sp., Bacillus subtilis, Enterobacter aerogenes, Klebsiella pneumoniae) along with other Gram-positive and Gram-negative bacteria. Solvents with different polarity were used for extraction from dry roots and stems. Minimum inhibitory concentrations (MIC) were also studied. Differential antibacterial activity was exhibited by extracts and higher inhibition potential against Gram-positive bacteria was explained. The observed antibacterial activities were correlated with the chemical structures of phytochemicals present in C. lasiantha. Anti-inflammation activities are related to C. lasiantha extracts through their antibacterial activities.

Performance analysis of CNN fusion based brain tumour detection using Chan-Vese and level set segmentation algorithms

Abstract: Early diagnosis of a brain tumour may increase life expectancy. Magnetic resonance imaging (MRI) accompanied by several segmentation algorithms is preferred as a reliable method for assessment. In this study, first noise removed by median filter and dimensionality of datasets reduced by using random projection transformation (RPT). Next, the pre-processed images are clustered by using K-means and fuzzy c-means (FCM). In the very next step, the clustered images multi-features are fused by different data fusion approaches, and then segment the exact tumour area by using the active contour models such as level set method (LSM) and Chan-Vese (C-V). The performance of clustered based segmentation and fusion-based segmentation in terms of various fusion metrics. The results of both clustered based and fusion-based methods revealed that the CNN fusion-based segmentation performs better than clustered- based segmentation to detect the tumour with low segmentation error and minimal loss of information.