Velagapudi Ramakrishna Siddhartha Engineering College

About: Velagapudi Ramakrishna Siddhartha Engineering College is a based out in . It is known for research contribution in the topics: Computer science & Antenna (radio). The organization has 1307 authors who have published 1155 publications receiving 6163 citations.

Correlation between corrosion indices and corrosiveness of groundwater: a study with reference to selected areas of Krishna District, Andhra Pradesh, India

Abstract: The aim of the present study was to determine the aggressiveness of groundwater samples towards corrosion of various commercially important metals and to study the correlation between theoretical and experimental values of corrosiveness. For this purpose, 20 groundwater samples were collected in different mandals of Krishna District, Andhra Pradesh, India, and the metals selected were carbon steel, aluminium, copper and cupronickel alloy. The corrosiveness was determined by using gravimetric measurements. Further, corrosion and scaling indices, namely Langelier index (LI), aggressive index (AI), Ryznar index (RI), Puckorius index (PI) and Larson–Skold index (LS), were calculated with the help of various water quality parameters of all the samples to estimate their corrosivity. The experimental values of corrosion rates were attempted to correlate with various indices calculated and quality parameters determined in case of all the samples. It is observed that there is a significant lack of correlation between the indices and corrosion rates. Among the five indices taken into account, LI, AI and RI indicate scaling tendency of water samples, as inferred by their mean values, 0.657, 12.576 and 6.612, respectively, while the other two indices, PI and LS with the mean values of 8.141 and 1.188, respectively, suggest the corrosive behaviour. Further, the mean values of correlation coefficients, 0.18 and 0.27 for PI and LS, respectively, are higher compared with those for LI, AI and RI. The mean values of correlation coefficients of dissolved oxygen, chlorides and electrolytic conductivity are 0.33, 0.25 and 0.25, respectively, which are relatively higher compared with those of other quality parameters. The probable reasons for low correlation coefficients are explored based on the statistical data. Also, it was concluded that one of the significant factors, namely dissolved oxygen which has strong influence on corrosion of metals, was considered in none of the indices, leading to very low correlation between the theoretical and experimental values of corrosiveness.

Intelligent Secure Smart Locking System using Face Biometrics

Abstract: The rapid growth of technology in the modern society has raised many questions on the terms like security and privacy. Due to the evolution in the technology and industrialization the terms like security and privacy has become imperative for a common person. Authentication is a key factor which helps for the identification of authorized people and helps in eradicating fraudulent activities, robberies, and many other social crimes. Most of the crimes are due to the vulnerabilities in the door locking systems which can be easily accessible by the outsiders. Though there are solutions like smart doorbells and video streaming, which have limitations like heavy cost, complex and have loopholes in the security issues. To diminish the limitations and to enhance the security Smart door unlock systems using face recognition is proposed. The proposed system consists of a camera sensor popularly known as esp32-cam for storing the pictures of persons and for live streaming. The proposed system recognizes the face of the person standing in front of the door with the help AI-Thinker in the esp32-cam. The face of the person is compared with the faces of the authorized persons which are stored in the SD card of esp32-cam. If the person is an authorized person then the door gets unlocked which can be achieved with the hardware component solenoid lock. If the person is an unauthorized person then the door will be locked. The proposed system helps in adapting from traditional mechanical lock methods to enhanced security methods. It also helps in case of losing keys and helpful for disabled persons with easier access.

Deming least square regressed feature selection and Gaussian neuro-fuzzy multi-layered data classifier for early COVID prediction

Abstract: Coronavirus disease (COVID-19) is a harmful disease caused by the new SARS-CoV-2 virus. COVID-19 disease comprises symptoms such as cold, cough, fever, and difficulty in breathing. COVID-19 has affected many countries and their spread in the world has put humanity at risk. Due to the increasing number of cases and their stress on administration as well as health professionals, different prediction techniques were introduced to predict the coronavirus disease existence in patients. However, the accuracy was not improved, and time consumption was not minimized during the disease prediction. To address these problems, least square regressive Gaussian neuro-fuzzy multi-layered data classification (LSRGNFM-LDC) technique is introduced in this article. LSRGNFM-LDC technique performs efficient COVID prediction with better accuracy and lesser time consumption through feature selection and classification. The preprocessing is used to eliminate the unwanted data in input features. Preprocessing is applied to reduce the time complexity. Next, Deming Least Square Regressive Feature Selection process is carried out for selecting the most relevant features through identifying the line of best fit. After the feature selection process, Gaussian neuro-fuzzy classifier in LSRGNFM-LDC technique performs the data classification process with help of fuzzy if-then rules for performing prediction process. Finally, the fuzzy if-then rule classifies the patient data as lower risk level, medium risk level and higher risk level with higher accuracy and lesser time consumption. Experimental evaluation is performed by Novel Corona Virus 2019 Dataset using different metrics like prediction accuracy, prediction time, and error rate. The result shows that LSRGNFM-LDC technique improves the accuracy and minimizes the time consumption as well as error rate than existing works during COVID prediction.

Military object detection in defense using multi-level capsule networks

Abstract: Automatic target detection plays a significant role during war operations. The concept behind automatic target detection in war is military object recognition from the captured images. For object recognition in the given image, convolutional neural network (CNN) architectures are used efficiently. However, CNN suffers from a problem of location invariant and their performance depends mainly on the size of the training set. Generally, the available training data will be in less proportion for military objects due to its operational and security issues. Due to these two issues of CNN, the performance of CNN may degrade abruptly. To address military object recognition, a relatively new neural network architecture based on capsule network (CapsNet) is introduced in this paper. A variant of CapsNet called the multi-level CapsNet framework is projecting in this paper for efficient military object recognition under the case of a small training dataset. The validation of the introduced framework is done on a dataset of military objects collected from the Internet. The dataset contains particularly five different military objects and similar civil ones. Experiments demonstrate that the proposed framework can accomplish a high recognition precision, superior to many other algorithms such as conventional support vector machines and transfer learning-based CNNs.