Raveendra Gudodagi received Bachelors degree in Electronics and Communication Engineering and Masters degree in Signal Processing from Visveswaraya Technical University, Belgaum, India, in 2009 and 2013 respectively, AND Ph.D. degree in Electronics and Communication Engineering from REVA university in 2022. His research interests include Artificial Intelligence, Cognitive Sciences, Machine Learning, Genomic data processing and compression. He is a recognised mentor from Texas Instruments. He is a Member of IEEE, IAENG and IETE.
EDUCATION
B.E., M. Tech., Ph. D.
RESEARCH INTERESTS
Cyber security, Signal Processing, Data Compression, Genomics, HEVC, Machine Learning.
Enhancing Early Detection of Brain Aneurysms: A CNN-Driven, Real-Time Approach with Angiography Imaging Aruna Kumara B, Raveendra Gudodagi, Madan H T, Kavitha D D, Manjunatha H M Proceedings of 5th International Conference on Iot Based Control Networks and Intelligent Systems Icicnis 2024, 2024 Brain aneurysm is a swell or balloon in a blood vessel in the brain that can cause severe health problems to the patients. Due to its potentiality, if it ruptures it leads to serious neurological disorder such as: coma, hemorrhagic stroke, and even death. Early identification of brain aneurysm plays a crucial role in treatment. However, the traditional methods such as Computerized Tomographic Angiography (CTA), Magnetic Resonance Imaging (MRI) takes more time and complex procedures. The relentless advancement of medical imaging technologies continues to transform diagnostic paradigms in neurology. In this context, this study integrates the convolutional neural networks (CNNs) into real-time brain aneurysm detection from angiography image. This paper presents a CNN-based model designed to rapidly and accurately identify cerebral aneurysms from angiography images. The proposed method, initially prepares the dataset by collecting diverse angiography images from medical archives. Later, the images undergoes with various preprocessing methods such as: denoising, contrast enhancement, normalization, resizing, and augmentation to enhance the quality of data, and model generalization. Then, the method automatically extracts the blood vessel structure and aneurysm characteristics such as: irregular shapes, unusual bulges, and densities within the vessels as features. Finally, the extracted features passed through the connected layers and the CNN classifies the whether an image contains aneurysm or not. The CNN model is trained with stochastic gradient descent (SGD) algorithm, and the trained model is deployed on a standard Graphics Processing Unit (GPU) for real time inference. The results showed that, the performance of the method was superior compared to the traditional methods.
Contention Based MAC Collision Avoidance Technique for Mobile Adhoc Networks Pavithra K, Raveendra Gudodagi 2023 3rd International Conference on Intelligent Technologies Conit 2023, 2023 Mobile ad hoc networks (MANETs) have transformed providing services for a variety of real-time applications. In a MANET, a collection of wireless mobile nodes powered by batteries is configured into a dynamic topology and operate independently without external infrastructure. In OSI model, the Medium Access Control (MAC) sub-layer of the data link layer has the primary responsibility of managing the devices that connects with the transmission channel. Mobility and resource limitations are the two main aspects that have an impact on MANET's operation. Frequent link failures occur due to nodes mobility and sharing of single channel during data transmission results is packet collision during nodes movement. Packet collision leads to retransmissions, higher delay, packet loss and more energy consumption. In this paper we propose a collision avoidance technique using contention window (CAD-CW) for MANET’s to handle nodes unique characteristics such as mobility, link constrain and energy. CAD utilises dynamic contention window to adapt network changes and high priority data is allowed to transmit first to minimize collision. Energy efficient path is selected for reliable data transmission to handle QoS.
Deep Learning Algorithms for Secure and Efficient Compression of Genomic Sequence Data Raveendra Gudodagi, K Thirumala Akash, Mohammed Riyaz Ahmed 3rd IEEE International Conference on Technology Engineering Management for Societal Impact Using Marketing Entrepreneurship and Talent Temsmet 2023, 2023 The idea that DNA encodes the human body is uncontested. Everything about a person, including behaviour and physical characteristics, is influenced by genetic information. Genomic data is a rich source of information when it is analysed, genomic data has a significant potential for use in disease diagnosis. Despite the fact that One size does not fit all, the medical advice is usually generic. The intelligent machine may provide us with additional human body knowledge on the contrary to build intelligent robots. The ongoing development of genomic data is either beneficial for engineering as well as medical research. Our search for personalised medicine may have a solution in the overlapping fields of engineering and medicine. The introduction of new emergent multimedia applications with an increase in demand and consumption of genetic data, as well as the invasion of digital media into every aspect of diagnosis and prognosis, are compelling new challenges for next-generation sequencing. The processing of genomic data is intended to meet hitheito unrecognised requirements, including compression, robustness, security, unambiguity, and the prediction of future anomalies, in addition to delivering individualised treatment. Deep neural network based deep learning has been intensively researched in recent years as one of the possible methods for overcoming these difficulties. Because digital data must be maintained effectively, with very high quality, and shouldn’t be easily modified by computer standards. The goal is to develop a safe genome compression method that significantly outperforms existing sequencing methods in terms of all performance parameters.
Exploiting the Joint Potential of Instance Segmentation and Semantic Segmentation in Autonomous Driving Mohammed Usman, Thirumala Akash K, Mohammed Riyaz Ahmed, Raveendra Gudodagi, Nitesh Kumar T 2023 International Conference for Advancement in Technology Iconat 2023, 2023 The history of transportation spanning from 4000 BC to the present, is a mesmerizing subject that can deepen our understanding of travel from horses and camels, fixed-wheeled carts, riverboats, chariots, cycles, trains, airplanes, cars, and the development of the road locomotive. The future of transport is all about driverless cars or autonomous vehicles. The most crucial requirement for autonomous vehicles for navigation is to detect objects on the road. Autonomous vehicles provide potential answers for traffic congestion, road safety issues, and passenger comfort. The proposed model can detect and classify objects for assisting autonomous driving vehicles with the use of deep learning and neural network-based learning approaches. It aims to segment objects like people and automobiles through the Xception model, which carries out semantic segmentation, and Mask RCNN approaches, for instance, segmentation. Both these methods exhibit improvements in detection and are effective and relatively accurate with the results compared to Deep neural networks (DNN) in Contrast to Mask R-CNN and Xception model, respectively.
Predicting Thyroid Dysfunction Using Machine Learning Techniques K Thirumala Akash, F Mohammed Usman, T Nitesh Kumar, Mohammed Riyaz Ahmed, Raveendra Gudodagi 12th IEEE International Conference on Advanced Computing Icoac 2023, 2023 The history of the thyroid gland dates back to ancient times when it was recognized as a structure in the neck that was important for maintaining good health. Early detection of thyroid disorders is important because it can allow for earlier treatment and potentially improve outcomes. Artificial intelligence (AI) can improve early thyroid disorder detection accuracy and efficiency. Machine learning (ML) is a type of AI that involves training a computer to recognize patterns in data; it can be used to enhance the accuracy and efficiency of early thyroid disorder detection in several ways. Convolutional Neural Networks (CNNs) can improve early thyroid disorder detection accuracy and efficiency by providing a more objective and comprehensive image data analysis. Therefore, CNNs are primarily used for image analysis, and other types of neural networks may be more effective for analyzing other data types. Feature selection is used to identify the most relevant or important features in a dataset for a particular task. In the context of early thyroid disorder detection, feature selection could be used to identify the most important symptoms, risk factors, or other characteristics that are associated with thyroid disorders. This work exploits the various machine-learning approaches for thyroid prediction using algorithms such as Logistic regression (LR), Support Vector Machine (SVM), Decision tree (DT), etc. As a result, the best algorithm has been chosen based on accuracy.
Metaverse-A Forthcoming Lifestyle S Naresh, Shreyas A Hegde, R Siddu, Shreyas N Kokhale, Raveedra Gudodagi 3rd IEEE International Conference on Technology Engineering Management for Societal Impact Using Marketing Entrepreneurship and Talent Temsmet 2023, 2023 In this article we have given a brief information which is directly helpful to anyone looking forward to gaining a commendable knowledge in the field of Metaverse. As the 3rd gen revolution of world technological advancements are happening with the flow of time, it is critical for an individual and the community to adapt with the growing technology, this article will assist those categories of people with a very good foundational information. The role of Blockchain for developing secure platform for deployment, Web 3.0 leading path towards higher interactive websites and data security and 5G and coming gen of networks providing ideal bandwidth becomes very important and critical as they are considered as the steppingstones for the evolution of metaverse and with the extensive aid of Virtual “ Mixed ” Augmented Reality, deployment of metaverse to the real world will be more effective and efficient.
Two-Wheeler Location Tracking and Tilt Monitoring System Thirumala Akash K, Samvrudhi K, Raveendra Gudodagi, Mohammed Riyaz Ahmed, Vinay S V 2022 6th International Conference on Computing Communication Control and Automation Iccubea 2022, 2022
Investigations and Compression of Genomic Data Raveendra Gudodagi, R. Venkata Siva Reddy, Mohammed Riyaz Ahmed Proceedings of 2020 3rd International Conference on Advances in Electronics Computers and Communications Icaecc 2020, 2020
RECENT SCHOLAR PUBLICATIONS
Quantum-Resistant Cryptography for Military Communications U Sachin, R Gudodagi, HT Madan 2025 IEEE Space, Aerospace and Defence Conference (SPACE), 1-6 , 2025 2025
Enhancing Early Detection of Brain Aneurysms: A CNN-Driven, Real-Time Approach with Angiography Imaging R Gudodagi, HT Madan, DD Kavitha, HM Manjunatha 2024 International Conference on IoT Based Control Networks and Intelligent … , 2024 2024 Citations: 2
Predicting thyroid dysfunction using machine learning techniques KT Akash, FM Usman, TN Kumar, MR Ahmed, R Gudodagi 2023 12th International Conference on Advanced Computing (ICoAC), 1-8 , 2023 2023 Citations: 11
Contention Based MAC Collision Avoidance Technique for Mobile Adhoc Networks K Pavithra, R Gudodagi 2023 3rd International Conference on Intelligent Technologies (CONIT), 1-6 , 2023 2023 Citations: 1
Metaverse - A Forthcoming Lifestyle RG S Naresh, Shreyas A Hegde, R Siddu, Shreyas N Kokhale 2023 IEEE 3rd International Conference on Technology, Engineering … , 2023 2023
Deep learning algorithms for secure and efficient compression of genomic sequence data R Gudodagi, KT Akash, MR Ahmed 2023 IEEE 3rd International Conference on Technology, Engineering … , 2023 2023 Citations: 1
Exploiting the joint potential of instance segmentation and semantic segmentation in autonomous driving M Usman, MR Ahmed, R Gudodagi, N Kumar 2023 International Conference for Advancement in Technology (ICONAT), 1-7 , 2023 2023 Citations: 4
A Novel Algorithm for Reconfigurable HDN Urs, RVS Reddy, R Gudodagi, KM Sudharshan, BN Aravind Sustainable Computing: Transforming Industry 4.0 to Society 5.0, 187 , 2023 2023
Fuzzy-Based Hierarchical Routing Protocol for Wireless Sensor Networks GH Raghunandan, N Keerthi Kumar, K Neeraj, RU Kashyap, SV Vishal, ... Sustainable Computing: Transforming Industry 4.0 to Society 5.0, 229-240 , 2023 2023 Citations: 2
Improvement of Flat Plate Collector Performance Using Nano-additives N Keerthi Kumar, GH Raghunandan, R Gudodagi Sustainable Computing: Transforming Industry 4.0 to Society 5.0, 307-317 , 2023 2023
A Novel Algorithm for Reconfigurable Architecture for Software-Defined Radio Receiver on Baseband Processor for Demodulation HD Nataraj Urs, R Venkata Siva Reddy, R Gudodagi, KM Sudharshan, ... Sustainable Computing: Transforming Industry 4.0 to Society 5.0, 187-206 , 2023 2023
A Review of AI-Based Diagnosis of Multiple Thoracic Diseases in Chest Radiography R Shetty, PN Sarappadi, KM Sudarshan, R Gudodagi Sustainable Computing: Transforming Industry 4.0 to Society 5.0, 241-252 , 2023 2023 Citations: 1
Two-Wheeler Location Tracking and Tilt Monitoring System K Samvrudhi, R Gudodagi, MR Ahmed, SV Vinay 2022 6th International Conference On Computing, Communication, Control And … , 2022 2022 Citations: 3
Encryption and decryption of secure data for diverse genomes R Gudodagi, RVS Reddy International Conference on Artificial Intelligence and Sustainable … , 2022 2022 Citations: 3
Deep Learning Algorithm for Procedure and Network Inference for Genomic Data R Gudodagi, RVS Reddy, MR Ahmed International Conference on Artificial Intelligence and Sustainable … , 2022 2022 Citations: 2
Design and development of learning model for compression and processing of deoxyribonucleic acid genome sequence R Gudodagi, RVS Reddy, MR Ahmed International Journal of Electrical and Computer Engineering 12 (2), 1786 , 2022 2022
Investigations and Compression of Genomic Data R Gudodagi, RVS Reddy, MR Ahmed 2020 Third International Conference on Advances in Electronics, Computers … , 2020 2020 Citations: 5
Customized Computational Environment for Investigations and Compression of Genomic Data RVSR RAVEENDRA GUDODAGI, MOHAMMED RIYAZ AHMED International Journal of Pharmaceutical Research 12 (Supplementary Issue 2 … , 2020 2020 Citations: 3
Multilingual Voice Assistant to Dumb People Based On Hand Gestures RG Amulya G.S, Anubhav Reddy S, Anurag Dangi, Arvind Ajawan International Journal of Advanced Science and Technology 29 (No. 10s (2020 … , 2020 2020
Smart Farming Using Arduino & GSM RG Chilukuri Akanksha, Guduru Karthik, Genikani Harish Kumar, K Indrakaran Reddy International Journal of Advanced Science and Technology 29 (No. 10s (2020 … , 2020 2020
MOST CITED SCHOLAR PUBLICATIONS
Predicting thyroid dysfunction using machine learning techniques KT Akash, FM Usman, TN Kumar, MR Ahmed, R Gudodagi 2023 12th International Conference on Advanced Computing (ICoAC), 1-8 , 2023 2023 Citations: 11
Investigations and Compression of Genomic Data R Gudodagi, RVS Reddy, MR Ahmed 2020 Third International Conference on Advances in Electronics, Computers … , 2020 2020 Citations: 5
Segmentation of acl in mri images R Gudodagi, K Vidyasagar, H Aravind International Journal of Engineering and Computer Science 2 , 2013 2013 Citations: 5
Exploiting the joint potential of instance segmentation and semantic segmentation in autonomous driving M Usman, MR Ahmed, R Gudodagi, N Kumar 2023 International Conference for Advancement in Technology (ICONAT), 1-7 , 2023 2023 Citations: 4
Two-Wheeler Location Tracking and Tilt Monitoring System K Samvrudhi, R Gudodagi, MR Ahmed, SV Vinay 2022 6th International Conference On Computing, Communication, Control And … , 2022 2022 Citations: 3
Encryption and decryption of secure data for diverse genomes R Gudodagi, RVS Reddy International Conference on Artificial Intelligence and Sustainable … , 2022 2022 Citations: 3
Customized Computational Environment for Investigations and Compression of Genomic Data RVSR RAVEENDRA GUDODAGI, MOHAMMED RIYAZ AHMED International Journal of Pharmaceutical Research 12 (Supplementary Issue 2 … , 2020 2020 Citations: 3
Enhancing Early Detection of Brain Aneurysms: A CNN-Driven, Real-Time Approach with Angiography Imaging R Gudodagi, HT Madan, DD Kavitha, HM Manjunatha 2024 International Conference on IoT Based Control Networks and Intelligent … , 2024 2024 Citations: 2
Fuzzy-Based Hierarchical Routing Protocol for Wireless Sensor Networks GH Raghunandan, N Keerthi Kumar, K Neeraj, RU Kashyap, SV Vishal, ... Sustainable Computing: Transforming Industry 4.0 to Society 5.0, 229-240 , 2023 2023 Citations: 2
Deep Learning Algorithm for Procedure and Network Inference for Genomic Data R Gudodagi, RVS Reddy, MR Ahmed International Conference on Artificial Intelligence and Sustainable … , 2022 2022 Citations: 2
Contention Based MAC Collision Avoidance Technique for Mobile Adhoc Networks K Pavithra, R Gudodagi 2023 3rd International Conference on Intelligent Technologies (CONIT), 1-6 , 2023 2023 Citations: 1
Deep learning algorithms for secure and efficient compression of genomic sequence data R Gudodagi, KT Akash, MR Ahmed 2023 IEEE 3rd International Conference on Technology, Engineering … , 2023 2023 Citations: 1
A Review of AI-Based Diagnosis of Multiple Thoracic Diseases in Chest Radiography R Shetty, PN Sarappadi, KM Sudarshan, R Gudodagi Sustainable Computing: Transforming Industry 4.0 to Society 5.0, 241-252 , 2023 2023 Citations: 1
Depth extraction from video: A survey A Chavan, RS Gudodagi Int. J. Innov. Res. Adv. Eng. 2 (2), 199-204 , 2015 2015 Citations: 1
Quantum-Resistant Cryptography for Military Communications U Sachin, R Gudodagi, HT Madan 2025 IEEE Space, Aerospace and Defence Conference (SPACE), 1-6 , 2025 2025
Metaverse - A Forthcoming Lifestyle RG S Naresh, Shreyas A Hegde, R Siddu, Shreyas N Kokhale 2023 IEEE 3rd International Conference on Technology, Engineering … , 2023 2023
A Novel Algorithm for Reconfigurable HDN Urs, RVS Reddy, R Gudodagi, KM Sudharshan, BN Aravind Sustainable Computing: Transforming Industry 4.0 to Society 5.0, 187 , 2023 2023
Improvement of Flat Plate Collector Performance Using Nano-additives N Keerthi Kumar, GH Raghunandan, R Gudodagi Sustainable Computing: Transforming Industry 4.0 to Society 5.0, 307-317 , 2023 2023
A Novel Algorithm for Reconfigurable Architecture for Software-Defined Radio Receiver on Baseband Processor for Demodulation HD Nataraj Urs, R Venkata Siva Reddy, R Gudodagi, KM Sudharshan, ... Sustainable Computing: Transforming Industry 4.0 to Society 5.0, 187-206 , 2023 2023
Design and development of learning model for compression and processing of deoxyribonucleic acid genome sequence R Gudodagi, RVS Reddy, MR Ahmed International Journal of Electrical and Computer Engineering 12 (2), 1786 , 2022 2022
