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.

MapReduce based Heart Disease Prediction System

Abstract: Decision tree is the traditional method to process health-related data. However, there are challenges in developing the distributed decision tree algorithms for massive health data using Hadoop. First, as the datasets are massive, the process of building a decision tree is time-consuming. The second problem is splitting. To solve these issues, we propose MapReduce-based Heart Disease Prediction System (MRHDP) for constructing a decision tree to predict heart disease with the Map Reduce programming model using the Hadoop platform. We develop distributed algorithms using the MapReduce framework, and also we conducted a large-scale experiment on massive data sets. Finally, the results indicate that the MRHDP algorithm exhibits better prediction results for heart disease, indicates better processing with less time productivity and versatility.

Tracking Missing Objects in a Video Using YOLO3 in Cloudlet Network

Abstract: In real time, people are using CCTV for monitoring activities continuously but sometimes theft is taking place. In this scenario, people need to roll back the video and need to identify when it happened. But in practice, it is a difficult and time-consuming process to identify a missing object in the video within the local machine because of the lack of computing resources. To solve this problem, we are presenting an algorithm in this paper to notify missing objects in a video offloaded from a mobile or CCTV using YOLO3 object detection in a cloudlet network. In the area of cloud computing, a cloudlet is a data center in the local network with a rich set of computing resources available for mobile users.

Road Deformation Detection

Abstract: There's one death every 4 minutes due to road accidents in India. One of the main reasons for these road accidents to happen is poor road conditions. Lately, the roads are becoming harder to maintain due to the extreme weather conditions of India. It is a challenging task for the municipality to patrol across the city to check the condition of the roads. People complain about bad roads all the time but have no way to detect or report them at scale. To tackle this problem, we will be mounting a camera connected to a raspberry pi, onto a vehicle. This camera is going to capture the image of the road in front of the vehicle. An onboard object detection model will be run on the input images from the camera for detecting any road deformations like potholes, cracks, or ruts. When the model detects any kind of deformation on the road, the latitude and longitude of that location will be immediately logged into an onboard or online database. A GPS module can be used to detect the lat long coordinates. This information can then be passed on to the civic authorities with proper visualizations on a 2D map, which can be helpful for the construction workers to directly go to the location and do the necessary repair works, which means workers can spend less time finding potholes and more time fixing them.

Performance analysis of neural network, NMF and statistical approaches for speech enhancement

Abstract: Bayesian Estimators are very useful in speech enhancement and noise reduction. But, it is noted that the traditional estimators process only amplitudes and the phase is left unprocessed. Among the Bayesian estimators, Super- Gaussian based estimators provide improved noise reduction. Super-Gaussian Bayesian estimators, which uses processed phase information for estimation of amplitudes provides further improved results. In this work, the Complex speech coefficients given Uncertain Phase (CUP) based Bayesian estimators like CUP-GG (CUP Estimator with speech spectral coefficients assumed as Gamma and noise spectral coefficients as Generalized Gamma), CUP-NG (Speech as Nakagami) are compared under white noise, pink noise, Babble noise and Non-Stationary factory noise conditions. The statistical estimators show less effective results under completely non-stationary assumptions like non-stationary factory noise, babble noise etc. Non-negative Matrix Factorization (NMF) based algorithms show better performance for non stationary noises. The drawback of NMF is, it requires apriori knowledge about speech. This drawback can be overcome by taking the advantages of both statistical approaches and NMF approaches. NR-NMF and WR-NMF speech enhancement methods are developed by providing posteriori regularization based on statistical assumption of speech and noise DFT coefficients distribution. Also a speech enhancement method which uses CUP-GG estimator and NMF with online noise bases update are considered for comparison. The progress in neural network based approaches for speech enhancement further shown that with large dataset and better training, the speech enhancement algorithms results in improved results. In this work, the neural network approach for speech enhancement is implemented and compared the method with traditional estimators and NMF approaches. For generalization of unseen noise types the proposed neural network approach uses dropout. Also for training the network, the features obtained from apriori SNR and aposteriori SNR is used in this method. The objective of this paper is to analyze the performance of speech enhancement methods based on Neural Network, NMF and statistical based. The objective performance measures Perceptual Evaluation of Speech Quality (PESQ), Short-Time Objective Intelligibility (STOI), Signal to Noise Ratio (SNR), Segmental SNR (Seg SNR) are considered for comparison.

High utility itemset mining using path encoding and constrained subset generation

Abstract: In this paper a two phase approach for high utility itemset mining has been proposed. In the first phase potential high utility itemsets are generated using potential high utility maximal supersets. The transaction weighted utility measure is used in ascertaining the potential high utility itemsets. The maximal supersets are obtained from high utility paths ending in the items in the transaction database. The supersets are constructed without using any tree structures. The prefix information of an item in a transaction is stored in the form of binary codes. Thus, the prefix information of a path in a transaction is encoded as binary codes and stored in the node containing the item information. The potential high utility itemsets are generated from the maximal supersets using a modified set enumeration tree. The high utility itemsets are then obtained from the set enumeration tree by calculating the actual utility by scanning the transaction database. The experiments highlight the superior performance of the system compared to other similar systems in the literature.