Geetha Rani. Edupuganti
@mvjce.edu.in
Assistant Professor
MVJCE
E. GEETHA RANI is working as Assistant Professor in the Department of Computer Science and Engineering, MVJ College of Engineering, Whitefield, Karnataka, Bengaluru. Received Bachelor degree in Information Technology from Koneru Lakshmaiah College of Engineering, Master degree in Computer Science and Engineering from Acharya Nagarjuna University and Pursuing Ph.D in Computer Science and Engineering from GITAM University. Reviewer for many Conferences and Journals. Has 14 years of experience in Teaching and Research and Industry. Having IEEE and SCRS membership. Area of research includes Data Analytics, Communication Network, Cloud Computing and Deep Learning.
EDUCATION
Ph.D (pursuing)(Computer Science Engineering) from December 2019
Gitam University,Nagandenahalli, Bengaluru
Present Status: About to Submit Thesis
#Title of the Ph.D “Data Storage in Cloud Computing for improving security and privacy using Encryption Techniques”
Master of Business Administration (Human Resources)
Acharya Nagarjuna University, Guntur, Andhra Pradesh, Year: 2011 - 2013
M.Tech (Computer Science Engineering)
Acharya Nagarjuna University (ANU), Guntur, Andhra Pradesh, Year: 2008 - 2010
B.Tech (Information Technology)
KL College of Engineering, (ANU), Andhra Pradesh, Year: 2005 - 2008
Diploma (Electronics and Communication Engineering)
KES Polytechnic College for Women, Vijayawada, Andhra Pradesh, Year: 2002 - 2005
SSC (State)
Bishop Azariah School, Vijayawada, Andhra pradesh, Year: 2002
RESEARCH INTERESTS
Cloud Computing
Artificial Intelligence
Machine Learning
Internet of Things
Scopus Publications
Scopus Publications
Geetha Rani E and Chetana Tukkoji
Springer Science and Business Media LLC
M. Dhanalakshmi, K P Vyshali Rao, Bhuvaneshwari, and E Geetha Rani
IEEE
The increase in the pollution of air has led to the generation of many health risks. In the present-day much study has been going on to analyse the standard of air circulated in atmosphere has incited. The Inter-networking of Things (IoT) is been used vastly in various domains to boost the standard of the life of people. This paper describes the Internet of Things (IoT) integrated with low-cost sensor networking for monitoring the quality of air. The sensor networks helps in accumulating the values of each component of air. Considering the difficulty of air quality prediction, an ETM model has been proposed. Firstly, a single factor ETM model is designed which obtain the prediction value of each air component. Then, an ETM model with multifactor is designed. The ETM model considers important factors like data on the surrounding environment and weather data. The integrating booster integrates the two models. The results are obtained by assembling the prediction from the sub-nodes.
E. Geetha Rani and D. T. Chetana
Springer Nature Singapore
E Geetha Rani, Mohammed Afeef Hussain, Mohammed Azeezulla, Mayank Shandilya, and Preethi Susan Varughese
IEEE
Skin infections are highly common today. A small circular or haphazardly shaped spot on the patient’s skin might be detected because of skin illness. When this condition develops into skin cancer, it can sometimes be quite hazardous. By querying an open-source dataset for skin diseases, this project searches for signs of probable skin abrasion or infection using neural networks or machine learning algorithms. Based on the model’s accuracy, it delivers the closest match to the patient’s condition. Including the finest treatments available that can aid in treating the detected disease with a tailored dataset, we will use CNN transfer learning, either VGG19 or different Resnet approaches, for our project. 23 classes, but we are seeking to meet at least 12+ classes. Accuracy of 90% above on train and 85% above on validation. The Project identifies for potential skin abrasion or an infection using Neural Network or Machine Learning Algorithms, based on the accuracy of the model it provides closest match to possible condition of the patient by querying open-source skin disease dataset. Also providing the best possible remedies which can help in curing the identified disease. A person’s quality of life may be significantly impacted by skin conditions. They can cause physical pain, emotional distress, and social isolation. They can also lead to financial burden, as many skin diseases require expensive treatment. In severe cases, skin diseases can be life-threatening. The skin diseases application is a great resource for anyone with a skin condition. It provides information on different skin diseases, as well as treatment options and tips for managing your condition.
Geetha Rani E, Roshan Jose S, Joel Thomas Chacko, Joshua Paul C, and Jeanette Krizelda K
IEEE
This work studies a peer-to-peer file streaming system with the following characteristics. The file streaming capacity is dynamic and depends on the storage capacity of the individual peers' devices. The large file is split into multiple chunks to stream it across peers. These chunks are not stored on the cloud or any intermediate channel, eliminating the need for a cloud storage bucket. To achieve this real-time streaming among peers, the Secure WebSocket (WSS) protocol is used, which has a minimal buffering delay. Depending on the internet bandwidth of the sender, the chunk size is dynamically modified to achieve a faster streaming rate. Before the chunks are received, the system allocates the required storage space for the incoming file stream. As these chunks arrive, they are written directly to the native storage of the device. Each room's name is unique. The creator of the room shares the name with his peers to start a streaming session. From this point, any peer in the room can act as a sender or a receiver.
Geetha Rani E, Mounika E, Gopala Krisnan C, Tanuep Bellam, Bhuvaneswari P, and Kanagavalli Rengaraju
IEEE
Human beings have the most distinctive feature that is human face. We can exchange somebody faces with anybody else's faces that appear realistic because many have another type of algo is based upon deepfake tech. Deepfake videos / photos is revolutionary subdual of AI tech by using someones human face can overwrite of someones face. More generously, with many different methods based on productive pictures. Unwillingly the overuse of smartphone and organizing by multiple internet web using AI manipulated data is reaching quicker in something which can we see in the 20th century, global danger is made up by these products Deepfakes are digital manipulation techniques that use machine learning to produce misleading videos. Identification is most difficult part to find from the original. Previously, CNN networks were used to perform identify the deep fake verification. Due to the increasing popularity of deep fakes identification of real one is more important find ways to detect manipulated videos that are presented as real ones. In this project, we will study different methods that can be used to detect such images as well as videos. This study shows that they can also be done using a convolutional algorithm known as Efficient Net and Inception Net. In this Paper, we compare various versions of Convolutional Inception Net with various versions of convolutional Efficient Net combined with Vision Transformers and different Data files to obtain best possible results in Deepfake detection. To get the highly accurate percentage to identify the video is fake or real by using efficient net and by inception net. tract)
Geetha Rani E, Tanuep Bellam, Mounika E, Bhuvaneswari P, Gopala Krishnan C, and Anusha D
IEEE
As a result of many technological developments and growing usage of artificial intelligence in our daily routines, the number of autonomous and self-driving vehicles has expanded dramatically. To be effective and efficient, autonomous cars must be able to recognize and interpret a variety of traffic signs and take appropriate responses. A technology known as Traffic Sign Recognition can be used to determine a large number of different traffic signs. Traffic Sign Recognition is a technique that enables self-driving or autonomous vehicles to recognize traffic signs on the road. We must classify the images into their appropriate categories or groupings once they have been recognized. We accomplish this by creating a Convolutional Neural Network model. We must apply a discipline of artificial intelligence known as computer vision to derive information from the images acquired and recognized by the Traffic Sign Recognition and make recommendations based on that knowledge. In our paper, we'll use a Convolutional Neural Network model to create distinct indications in the image that may be sorted into several categories. As a result, the system can read and understand traffic signs, which is a crucial duty in the creation and improvement of autonomous vehicles. Because this would be used in a real-time setting, we have included blurry images in our dataset to emulate real-time capturing scenarios like as when the vehicle is moving or when direct light is shining on the subject.
E Geetha Rani and D T Chetana
IEEE
In this paper an asymmetric key algorithm is used towards provide effective safety measures. At first new files encrypted with the QAES algorithm at the end of the data holder and then stored in the cloud. Then the encrypted data request delivered towards data owner. The user and the sender analyses data then direct encoded documents towards cloud. Later data will be cleared of encryption using the key. The data user receives data that has already been encrypted. If a data user fails to retrieve data, they should re-request. The effect of this method is to provide improved security. Similarly, Period it takes on the way to compile files faster than it even now is. Brute force attacks are not possible in this way, as we use a QAES algorithm that provides additional security.
Publications
RESEARCH ACTIVITIES (SCI/ SCOPUS/WEB OF SCIENCE)
INTERNATIONAL CONFERENCES
GEETHA RANI E, CHETANA D T “Using github and grafana: Data visualization in big data”, 2nd International Conference on Computer Vision and Robotics (CVR 2022) During May 21-22 published in “Algorithms for Intelligent Systems” in the Web of Science.
GEETHA RANI E, CHETANA D T “To increase security and privacy, the QAES encryption algorithm is used for storage of data for cloud computing” INDICON 2022 IEEE 19th India Council International Conference, scheduled, during Nov 24-26, 2022 at Cochin, Kerala.
DOI: 10.1109/
GEETHA RANI E, CHETANA D T “A Survey of Recent Cloud Computing Data Security and Privacy Disputes and Defending Strategies”, Congress on Smart Computing Technologies (CSCT 2022) During Dec 11-12 Published in”Smart Innovation and Technologies” in the SCOPUS (Q3) and DBLP.
GEETHA RANI E, MOUNIKA E, Gopala Krishnan C,Tanup Bellam, Bhuvaneswari P, Kanagavalli Rengaraju “Comparative Analysis of Deepfake Video Detection Using Inception Net and Efficient Net (won BEST paper Award)” Fourth International Conference on “Emerging Research in Electronics, Computer Science and Technology” ICERECT – 2022,Financially sponsored by AICTE, New Delhi and Technical Co- sponsored by IEEE Bangalore Section during 26-27, December 2022 at P. E. S. College of Engineering, Mandya
RESEARCH OUTPUTS (PATENTS, SOFTWARE, PUBLICATIONS, PRODUCTS)
Published a BOOK on 18/11/22 entitled as The Security Essentials - Data, Network & Computer on Flipkart and
Published a PATENT on 7/11/22 “ IoT and AI based Smart health monitoring wrist device to connect doctor-patient to assist immediate medical attention/guidance for BP, sugar, HB by exchanging data in hybrid cloud” with Application Number-202241063449
Published a PATENT on 5/1/23 “ Machine Learning based non-invasive detection and prevention of anemia at early stages using, image processing and Deep Learning algorithms for all ages of people for Healthy life” with Application Number-202311001191