VIJAYAKRISHNA RAPAKA E
@rgcet.edu.in
Professor and Mechanical Engineering
RAJIV GANDHI COLLEGE OF ENGINEERING AND TECHNOLOGY, PUDUCHERRY
EDUCATION
Ph.D. in Ocean Engineering from IIT Madras
M. Tech. in Energy Technology from Pondicherry University
B. Tech. in Mechanical Engineering from Pondicherry University
RESEARCH INTERESTS
Ocean Energy, Solar Energy
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
T. S. Karthik, Naziya Hussain, N K Anushkannan, Rajasekhar Pinnamaneni, Vijayakrishna Rapaka E, and Shyamali Das
IEEE
Intracranial haemorrhage (ICH) refers to a pathological disorder that requires quick decision-making and diagnosis. Computed tomography (CT) can be accurate and dependable diagnosis method for identifying haemorrhages. Automated recognition of ICH through CT scans with a computer-aided diagnosis (CAD) method will be useful to classify and detect the distinct grades of ICH. Due to the latest development of deep learning (DL) techniques in image processing applications, numerous medical imaging methods use it. Thus, this article develops an automated ICH detection and classification using Rider Optimization with Deep Learning (ICHDC-RODL) model. The presented ICHDC-RODL technique mainly determines the presence of ICH using DL concepts. In the presented ICHDCRODL technique, the features are generated by the use of Xtended Central Symmetric Local Binary Pattern (XCS-LBP) model. Moreover, the bidirectional long short-term memory (BiLSTM) method is employed for ICH diagnosis. At last, the rider optimization algorithm (ROA) is exploited for the hyperparameter tuning procedure of the BiLSTM method. To demonstrate the enhanced outcomes of the ICHDC-RODL technique, a series of simulations were performed and the results are examined under various aspects. The simulation outcomes indicate the enhancements of the ICHDC-RODL technique over recent approaches.
A. Praveena, N. Senthamilarasi, T. S. Karthik, Abirami S.K, Vijayakrishna Rapaka E, and Shyamali Das
IEEE
Autism Spectrum Disorder (ASD) is a developing disorder if the symptoms develop obvious in the initial years of age but it could be present in some age groups. ASD is mental health problem that affects communicational, social, and non-verbal performances. It could not be cured entirely but is decreased when identified initially. The primary analysis was hampered by the difference and severity of ASD symptoms and containing symptoms usually realized in other mental health problems as well. With the application of machine learning (ML) for the predictive and recognition of several diseases with optimum accuracy, a ray of hope to initial recognition of ASD dependent upon many physiological and physical parameters is projected. This article designs an Equilibrium Optimizer with Deep Learning Model for Autism Spectral Disorder Classification (EODL-ASDC) technique. The presented EODL-ASDC technique mainly focuses on the identification and classification of ASD. To attain this, the presented EODL-ASDC technique exploits the deep belief network (DBN) system to perform the classification procedure. In addition, the EO algorithm is employed for the optimal hyperparameter tuning of the DBN approach. To demonstrate the enhanced ASD classification result of the EODL-ASDC approach, an extensive range of experimental evaluates was executed. The experimental results demonstrate the improvements of the EODL-ASDC technique over other approaches.
Nirmal Adhikari, Nihar Ranjan Behera, Vijayakrishna Rapaka E, Er. S. John Pimo, Vaibhav Chaturvedi, and Vikas Tripathi
IEEE
Object detection in unmanned aerial vehicle (UAV) images becomes a persistent problem in the domain of computer vision. Particularly, object detection in drone images is a difficult process because of the object of different scales namely, hills, buildings, and water bodies. The study presents an execution of ensemble transfer learning to improve the efficiency of the fundamental model for multi-scale object recognition in drone imagery. This study develops an Optimal Deep Learning Enabled Object Detection and Classification on Drone Imagery (ODL-ODCDI) technique. The presented ODL-ODCDI technique can recognize and classify the objects present in the images collected by the drones. It follows a two stage process. In the first level, the ODL-ODCDI technique employed YOLO-v5 as object detector with Nadam optimizer. Next, in the latter level, the ODL-ODCDI technique makes use of random forest (RF) classifier to identify objects in the drone images. To establish the enhanced performance of the ODL-ODCDI approach, a series of experiments were performed. The experimental values depicted the improved outcomes of the ODL-ODCDI method over other DL models.
E Vijayakrishna Rapaka, R Natarajan, and S Neelamani
Elsevier BV
E. Vijayakrishna Rapaka, R. Natarajan, and S. Neelamani
ASMEDC
A detailed experimental investigation conducted on a moored Oscillating Water Column (OWC) wave energy device has been reported in this paper. The experiments were conducted on 1:20 scale model of the wave energy device, which was moored to the bed using 6 mooring lines in a 2m wide (deep and shallow water) wave flume at Ocean Engineering Department, IITM, Chennai. A range of hydrodynamic parameters with different damping ratio of the OWC chamber at scope 4 (length of the mooring line/depth of water) for a constant water depth was used. The effect of non-dimensionalized parameters like non-dimensionlized wave frequency parameter (ω2B/2g) and device breadth to wave length ratio (B/L) on the mooring force and on the efficiency of the wave energy device has been studied. The motion responses and mooring forces were measured and the test results are analysed and presented with discussions in this paper.
E. Vijayakrishna Rapaka, S. Neelamani, and R. Natarajan
ASMEDC
Wave transmission and pneumatic efficiency of an oscillating water column (OWC) type wave energy device resting on group of piles is investigated using physical model study. The caisson blocks 45% of the water depth. The co-efficient of transmission of the device varies from 0.1 to 0.4 for B/L range of 0.1 to 0.7, where ‘B’ is the width of the caisson in the direction of wave propagation and ‘L’ is the wavelength. The pneumatic efficiency varies from 20% to 50% with an average value of 0.35. The results of the present study can be used in the design of OWC caisson used for both wave energy conversion and breakwater in deeper water.