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: Control theory & CMOS. The organization has 1138 authors who have published 1381 publications receiving 7798 citations.

Numerical and experimental nonlinear dynamic response reduction of smart composite curved structure using collocation and non-collocation configuration:

Abstract: In this work, a generic geometrical nonlinear mathematical model of smart composite curved shell panels has been developed for the evaluation of the linear and nonlinear dynamic responses. Further,...

FERNet: A Deep CNN Architecture for Facial Expression Recognition in the Wild

Abstract: Facial expression recognition is an intriguing and demanding subject in the realm of computer vision. In this paper, we propose a novel deep learning-based strategy to address the challenges of facial expression recognition from images. Our model is developed in such a manner that it learns hidden nonlinearity from the input facial images, which is critical for discriminating the type of emotion a person is expressing. We developed a deep convolutional neural network model composed of a sequence of blocks, each consists of multiple convolutional layers and sub-sampling layers. Investigations on the benchmark FER2013 dataset indicate that the proposed facial expression recognition network (FERNet) surpasses existing approaches in terms of performance and model complexity. We trained our model on the FER2013 dataset, which is the most challenging of all the available datasets for this task, and achieve an accuracy of around 69.57%. Furthermore, we investigate the effects of dropout, batch normalization, and augmentation, as well as how they aid in the reduction of over-fitting and improved performance.

Recent Advances in Probiotics as Live Biotherapeutics Against Gastrointestinal Diseases.

Abstract: Background Gastrointestinal (GI) diseases are a major cause of emergency department visits requiring hospitalizations leading to considerable burden on global economy Several factors contribute to the onset of gastrointestinal diseases such as pathogens (parasites, bacteria, virus, toxins etc), autoimmune disorders and severe inflammation of intestine Objective One common feature among all these diseases is the dysentery and alteration of gut microbiota composition (gut dysbiosis) Apart from conventional therapies such as antibiotics and ORS supplementation, gut microbiota modulation with probiotic supplementation has emerged as a successful and healthy alternative in mitigating GI diseases In this review our goal is to discuss the causes of gastrointestinal diseases and the present state of various therapeutic strategies such as probiotics as live biotherapeutics and Fecal Microbial Transplants (FMT's) Conclusion Several reports and clinical trials point out to the beneficial effects of probiotics in modulating the gut microbiota and improving the side effects of gastrointestinal diseases Live biotherapeutics and FMT's could be suitable and successful alternatives to conventional therapies in mitigating the gastrointestinal pathogens

Facial expression classification using Kernel based PCA with fused DCT and GWT features

Abstract: Although many approaches for facial expression recognition have been proposed in the past, most of them yielding poor recognition performance with single feature extraction method The objective of this paper is to propose an innovative method based on fusion of local and global features for better classification rate Gabor wavelets(GWT) are used to extract Local features and Discrete Cosine Transform (DCT) is used to extract global features from facial expression images To reduce dimensionality of extracted features and better classification performance Kernel Principal Components Analysis (KPCA) is applied Wavelet fusion method is used to fuse the features extracted from GWT and DCT Finally the images are classified into 6 different basic emotions like surprise, fear, sad, joy, anger and disgust using Radial Basis Function(RBF) Neural Network classifier The performance of the proposed method is evaluated on Cohn-Kanade database The results of proposed algorithm exhibit high performance rate of about 99% in person dependent facial expression recognition

An Efficient Stock Market Prediction Method Based on Kalman Filter

Abstract: Stock Market Prediction system provides an overview for the business to gain high profit in the share market. The rise of a large volume of data related to the financial market makes it difficult to analyze and predict the stock market effectively. This research focuses on to improve the effectiveness of the stock market prediction based on the Kalman filter. The financial data from Yahoo and Twitter are used to forecast the stock market values. The technical indices are extracted from the data to investigate the stock values. Twitter data related to the company’s stock value are extracted and analyzed the sentiment. Next, the Kalman filter is applied to reduce the errors from data. Kalman filter is used here to smoothen the noise created by sudden peaks in the data or to filter out the abnormal incidents in the data for training. Hence, the classification algorithm is not trained with irrelevant features value. The Accelerated Gradient Long-Short Term Memory (AG-LSTM) is used for stock market prediction. The output of the method is analyzed with and without Kalman filter and this showed that the Kalman filter technique increased the performance of the stock market prediction. In Microsoft stock data, the accuracy of the proposed AG-LSTM with Kalman filter model has achieved accuracy of 90.42%, while existing AG-LSTM model has achieved 57.53%.