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.

IoT based State of the Art Dam Management System in Indian Scenario

Abstract: As per Indian scenario, most of the water reservoirs are managed manually which becomes troublesome during emergencies (Abnormal inflow, overcast) which results in submergence of the villages and agriculture fields that surround those reservoirs. The vital reservoir life parameters are difficult to measure manually due to its large geographic area and depth. So, this research work proposes a state of art reservoir management system which consists of suitable sensors to measure parameters such as pressure, water level, inflow velocity, outflow velocity, tilt, vibration etc., all integrated through Arduino Uno and transmitting the live stream data to Microsoft power BI where each of the parameters with suitable format of visualization are shown. The alarm is activated for emergency water level increase. The process starts with data acquisition from sensors and ends-up being displayed in dashboard in the control room positions in the remote location which is directed to a website where the accessed can see the necessary details.

IoT based Smart Cold Storage System for Efficient Stock Management

Abstract: In the era of smart technology Internet of things interconnect real world sensors to the internet. Today’s cold storages are far more than just a facility to store inventory. In this paper we propose a “smart cold storage” by leveraging the latest supply chain technology and the IOT, which will serve as a hub to improve the efficiency and speedup the process throughout the entire supply chain. This proto type incorporates an IOT based smart cold storage that interacts with the items stored within, collects the information about them and process this information into relevant data. The objects placed inside the smart cold storage will be detected and identified using a web camera. Load cell with HX711 IC driver is used to calculate the Weight of the objects. Raspberry Pi-3 B+ collects data from the ARDUINO and analyze the data using python programming and transmit the stock information to the users through mobile application. It gives an alert to the users to place an order if the weight falls below the threshold value, i.e If there is any shortage or out of stock of the objects. LM35 IC Temperature sensor is used to monitor the Temperature of the storage system. This proto type will reduce the use of manual labour increasing speed and shipping accuracy, and offer retailers an opportunity to obtain unparalleled visibility into inventory and supply chains.

Effective Use of Deep Learning and Image Processing for Cancer Diagnosis

Abstract: The area of medical image processing obtains its significance with the requirement of precise and effective disease diagnosis over a short period. With manual processing becoming more complicated, stagnant and unfeasible with higher data size, there necessitates automatic processing that can transform contemporary medicine. Deep learning mechanisms can arrive at a higher rate of accuracy in processing and classifying images in comparison with human-level performance. Deep learning not only assist in selecting and extracting features but also possesses the potentiality of measuring predictive target audience and bestows prediction in a more action format to help doctors significantly. Unsupervised Deep Learning for cancer diagnosis is advantageous whenever the involvement of unlabeled data is huge. By bestowing unsupervised deep learning techniques to such unlabeled data, features of pixels that are superior compared to manually obtained features of pixels are said to be learned. Supervised Discriminating Deep Learning directly provides discriminating potentiality for cancer diagnosis purposes. Finally, hybrid deep learning for labeled and unlabeled data is specifically used for cancer diagnosis with a resource or poor pixel representations and hence early detection and diagnosis performed via bank features. Deep Neural Network, as the name implies includes several layers, emphasizing the complex non-linear relationships between the features present in the images, therefore contributing to higher accuracy. Deep Belief Network used in both supervised and unsupervised deep learning adopting greedy mechanism, maximizing the likelihood nature of detection and diagnosis at an early stage. Sequential event analysis is said to be performed by Recurrent Neural Network with the weights being shared across all neurons, contributing diagnosis accuracy. Certain fine-tuned learning parameters of consideration for better and precise learning are Interaction and Non-linear Rectified Activation function, Circumventing over-fitting via Dropout and Optimal Epoch Batch Normalization. In the last section, challenges about the application of deep learning for cancer diagnosis are discussed.

Gabor filter based entropy and energy features for basic scene recognition

Abstract: Our research objective is to develop a supervised learning based hierarchical classification framework built upon Gabor features. Specifically, we experimented on the Oliva Tor alba data-set from the Corel stock photo library. This data set consists of 2688 natural and artificial scene color images, of size (256X256X3) each, from 8 sub-categories. In this paper, we restrict our goal to categorization of images into natural and artificial groups. The methodology consists of feature extraction and binary classification stages. In this, we propose to use the complex Gabor (CG) filter based global features (depending on the overall layout and scene structure, but invariant to object details) from each image. Initially, in the feature extraction process, a 20-CG filter is applied to images for producing Gabor output images in terms of spatial frequency and orientation. Guided by Gabor uncertainty principle, we choose the resolution of spatial frequencies and orientations. At each of these coordinates, energy and entropy features are computed from image's real and imaginary components. We investigate the viability.ofusing the global features with a SVM classifier for basic scene categorization. Next, after applying the PCA based dimensionality reduction, a 2 class SVM discriminant function based on quadratic kernel is applied to the Gabor features for image classification. By using a 10-fold cross validation, we obtained a classification accuracy of 95.79 % and kappa accuracy of 0.9148.

Detection of Permission Driven Malware in Android Using Deep Learning Techniques

Abstract: In today’s busy world, usage of mobile applications is increasing in all aspects of life including banking and finance. Taking this as an opportunity, cyber crimes are taking place in the form of hacking and malware. While installing the mobile apps, everyone may not be aware of which permissions to accept and which one to deny. If the user start accepting all permissions, malware or malicious apk files may enter the mobile through some of them. Many machine learning techniques have been introduced to resolve this problem but failed to get considerable accuracy in real-time applications. The present research concentrates on detecting the malware which can enter through permissions in android using deep neural network model. The proposed approach detects the permission driven malware in real time android apk files with more than 85% accuracy.