Prasad Sarappadi Narasimha

Scopus Publications

Retraction Note: A Detailed Evolutionary Scrutiny of PEIS with GPS Fleet Tracker and AOMDV-SAPTV Based on Throughput, Delay, Accuracy, Error Rate, and Success Rate (Wireless Personal Communications, (2021), 121, 4, (2635-2651), 10.1007/s11277-021-08840-2)
M. Thurai Pandian, Sarappadi Narasimha Prasad, Manish Sharma
Wireless Personal Communications, 2026
Mitigating the Threat of DDoS Attacks: Emerging Strategies and Algorithms
Varsha C. Parihar, S. N. Prasad, Manjunath R. Kounte, K. B. Sai Prabhu
Lecture Notes in Electrical Engineering, 2026
Cost Function Reduction Using Stability-Informed Bayesian Optimization for the Model Predictive Control of a Semi-Active Suspension System
C. Raji, S. N. Prasad
Engineering Technology and Applied Science Research, 2025
Traditional Model Predictive Control (MPC) performance is very sensitive to the state-weighting matrix and the control-weighting , resulting in time-consuming and suboptimal results. This work introduces a smart control approach for semi-active suspension systems that combines MPC with Stability-informed Bayesian Optimization (SiBO). The Bayesian framework uses a neural network surrogate model to approximate the cost function, significantly reducing the number of iterations required for optimization. Using a quarter-car model with a Magnetorheological (MR) damper, the method tackles the challenge of non-linear damping by linearizing it and directly integrating it into the MPC cost function. This reduces the computational load and makes the control more suitable for real-time use. The proposed strategy optimizes the damping coefficient to balance ride comfort and stability. Compared to traditional methods, it delivers clear improvements. The system showed a 15% reduction in Root Mean Square (RMS) body acceleration and a 12% improvement in suspension travel over standard Proportional–Integral–Derivative (PID)-tuned damping. It also achieved faster convergence, reaching optimal performance in just 10 iterations. In contrast, Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) required more than 50 iterations to reach such results.
A Comparative Analysis of Advanced Deep Learning Techniques for Accurate Cardiac Arrhythmia Classification
Anniah Pratima, K. Gopalakrishna, Sarappadi Narasimha Prasad
Engineering Technology and Applied Science Research, 2025
The precise identification of cardiac arrhythmias facilitates accurate diagnosis and proper treatment, but the characterization process remains complex due to disturbances in ECG data signals along with skewed class frequencies and individual patient-specific variations. This study developed a deep learning framework, known as Penalty Regression Function-enhanced Deep Convolutional Neural Network (PRF-DCNN), as a comprehensive solution to cope with signal noise along with class imbalance and variations in patient data. The system starts by applying Correlation Factor-Based Extended Kalman Filtering (CF-EKF) for ECG signal denoising before allowing Ensemble Empirical Mode Decomposition (EEMD) to extract nonstationary features. The feature selection process along with the reduction of redundant characteristics uses the Frechet Fitness Rank Distribution-Anas Platyrhynchos Optimization (FFRD-APO method. The dataset is balanced by a Balanced Zero Noise GAN (BZNGAN) before Age-Weighted Average-Based Farthest First Clustering (AWA-FFC) refines the clustering process. The St. Petersburg INCART 12-lead ECG dataset was used to test the model, which obtained 99.53% accuracy, 99.10% sensitivity, and 99.67% specificity. The proposed system outperforms current models, showing its capacity for dependable time-critical arrhythmia detection in medical environments.
A robust penalty regression function-based deep convolutional neural network for accurate cardiac arrhythmia classification using electrocardiogram signals
Anniah Pratima, Gopalakrishna Kanathur, Sarappadi Narasimha Prasad
Iaes International Journal of Artificial Intelligence, 2025
Cardiac arrhythmias are a leading cause of morbidity and mortality worldwide, necessitating accurate, and timely diagnosis. This paper presents a novel approach for the classification of cardiac arrhythmias using a penalty regression function (PRF)-based deep convolutional neural network (DCNN). The proposed model integrates advanced preprocessing techniques, including frechet with fitness rank distribution-based anas platyrhynchos optimization (FFRD-APO) for feature selection and ensemble empirical mode decomposition (EEMD) for signal decomposition. Utilizing the St. Petersburg INCART 12-lead arrhythmia database, the PRF-DCNN model achieved superior performance metrics: an area under the curve-receiver operating characteristic (AUC-ROC) of 0.97, accuracy of 0.95, precision of 0.93, recall of 0.92, specificity of 0.97, and an F1 score of 0.93. The PRF effectively mitigated overfitting, ensuring robust and reliable classification across varied patient demographics. The model demonstrated significant improvements over traditional methods, offering an efficient solution for real-time cardiac monitoring and diagnosis. This study underscores the potential of PRF-DCNN in enhancing automated arrhythmia detection and lays the groundwork for future research to optimize and validate this approach in diverse clinical settings.
Revolutionizing Diabetes Prediction: A Machine Learning Approach to Early Detection and Intervention
Chaitra Nayak J, Rajanikanta Mohanty, S N Prasad
2025 2nd Asia Pacific Conference on Innovation in Technology Apcit 2025, 2025
Diabetes mellitus, a chronic metabolic disorder affecting over 422 million people globally, poses significant challenges to public health due to its increasing prevalence and severe complications. This research explores the application of machine learning (ML) techniques to revolutionize diabetes prediction, aiming to enhance early detection and intervention strategies. We implemented and compared various ML algorithms, including logistic regression, decision trees, support vector machines, random forests, k-nearest neighbors, XGBoost, and artificial neural networks, using a comprehensive dataset of diabetes-related features. Data preprocessing techniques, including normalization and addressing class imbalance, were applied to improve model performance. Our results demonstrate that ensemble methods and neural networks exhibited superior predictive performance. The logistic regression model, used as a baseline, achieved an overall accuracy of 92%.
Quantum-Resilient Cryptography: A Survey on Classical and Quantum Algorithms
H A Sharath, Jayavrinda Vrindavanam, Saswati Dana, S N Prasad
IEEE Access, 2025
The rapid advancement of quantum computing is poised to disrupt the foundations of classical cryptography, calling into question the long-term security of widely used algorithms. Classical cryptographic techniques, including symmetric key algorithms like DES, 3DES and AES, asymmetric schemes such as RSA and ECC, and essential primitives like hash functions and the One-Time Pad (OTP), have been the cornerstone of secure digital communication since their inception. However, the progress of quantum algorithms, spearheaded by Shor and Grover, poses serious threats to these schemes, necessitating a shift in cryptographic design. This paper presents an all-encompassing review of classical cryptographic techniques, along with emerging post-quantum cryptographic (PQC) algorithms that have been developed to provide quantum-resistant security while maintaining compatibility with classical infrastructure. Concurrently, the quantum key distribution (QKD) introduces a radical shift, leveraging quantum mechanics principles to attain unconditional security. This paper elaborates both Discrete Variable (DV-QKD) and Continuous Variable (CV-QKD) approaches, highlighting their operational principles and security models. By providing a relative analysis of classical cryptographic methods, PQC, and quantum cryptography, including QKD protocols and quantum-safe primitives, this work targets to provide a consolidated insight into modern cryptographic landscape and further research toward secure communication in the quantum era.
Use of Magnetometer and Machine Learning Algorithms for Real-Time Vehicle Classification
M. G. Ravi Kumar, Mrinal Sarvagya, G. P. Rashmi, Sunil Thimmaiah, B. K. Rashmi Priyadarshini, S. N. Prasad
Lecture Notes in Networks and Systems, 2025
Design of Compact MIMO Antennas for Ultra Wideband Applications
M. G. Ravi Kumar, Mrinal Sarvagya, Sunil Thimmaiah, Rashmi B. K. Priyadarshini, S. N. Prasad, B. S. Kavya
Lecture Notes in Networks and Systems, 2025
Design of novel convolution neural network model for lung cancer detection by using sensitivity maps
Sugandha Saxena, Sarappadi Narasimha Prasad
Iaes International Journal of Artificial Intelligence, 2024
<p>Despite the existence of numerous models for detecting lung cancer, there is still room for achieving higher levels of accuracy. In this paper, a maximum sensitivity neural network (MSNN) has been proposed. As the name suggests, the model aims to achieve high sensitivity and offers a viable remedy to minimize the number of false positive in oder to improve the overall accuracy for lung cancer detection. The MSNN model is a promising model since it can efficiently interpret grayscale lung computed tomography (CT) scan images as inputs and can be trained using just a few images also. This model has surpassed previous deep learning models by obtaining a remarkable sensitivity of 94.6% and an accuracy of 96.9%. A sensitivity map is created, offering important insights into the critical regions for finding malignant nodules. This innovative method has shown outstanding performance in identifying lung cancer with a low false positive rate which can increase the accuracy of medical diagnoses.</p>
Sqrt-Loglogish CNN and Markov model for 5G spectrum sensing application
Anupama Rajanna, Srimannarayana Kulkarni, Sarappadi Narasimha Prasad
Indonesian Journal of Electrical Engineering and Computer Science, 2024
Photo-realistic photo synthesis using improved conditional generative adversarial networks
Raghavendra Shetty Mandara Kirimanjeshwara, Sarappadi Narasimha Prasad
Iaes International Journal of Artificial Intelligence, 2024
An efficient in-memory carry select adder realization using resistive switching crossbar array with Ti-doped VO2 -based selector device
U. Dilna, S.N. Prasad
Materials Science in Semiconductor Processing, 2024
Deep Learning Methods on Chest X-Ray Radiography for Detection and Classification of Thoracic Disease: A Survey
Roshan Shetty, Prasad Narasimha Sarappadi
Aip Conference Proceedings, 2024
Adversarial sketch-photo transformation for enhanced face recognition accuracy: A systematic analysis and evaluation
Raghavendra Mandara Shetty Kirimanjeshwara, Sarappadi Narasimha Prasad
International Journal of Electrical and Computer Engineering, 2024
Wide-band spectrum sensing with convolution neural network using spectral correlation function
Anupama Rajanna, Srimannarayana Kulkarni, Sarappadi Narasimha Prasad
International Journal of Electrical and Computer Engineering, 2024
Modeling of 1S-1R crossbar array with Ti-doped VO2-based selector device
U. Dilna, S. N. Prasad
Ferroelectrics, 2024
Multiclass Classification of Chest X Ray Images Using Resnet-152 with Mix-Up Regularization Method
International Journal of Intelligent Engineering and Systems, 2024
Dysfluent Speech Classification Using Variational Mode Decomposition and Complete Ensemble Empirical Mode Decomposition Techniques With NGCU-Based RNN
N. A. Vinay, K. N. Vidyasagar, S. Rohith, S. Supreeth, S. N. Prasad, S. Pramod Kumar, S. H. Bharathi
IEEE Access, 2024
Blockchain-Enabled Detection of Neurological Disorders Using a Deep Learning Approach †
Kavya Bittasandra Sachidananda Murthy, Sarappadi Narasimha Prasad
Engineering Proceedings, 2023
An Accelerated Variable Stage Size Carry Skip Adder Realization Using 1S1R Resistive Memory
U. Dilna, S. N. Prasad
International Journal of Information Technology and Decision Making, 2023
Utilizing Deep Learning Techniques to Diagnose Nodules in Lung Computed Tomography (CT) Scan Images
Iaeng International Journal of Computer Science, 2023
Study and analysis on detection, classification, and prediction of cardiac arrhythmia using soft computing tool
A Pratima, K GopalaKrishna, S N Prasad
Journal of Physics Conference Series, 2023
A Review of AI-Based Diagnosis of Multiple Thoracic Diseases in Chest Radiography
Roshan Shetty, Prasad Narasimha Sarappadi, K. M. Sudarshan, Raveendra Gudodagi
Sustainable Computing Transforming Industry 4 0 to Society 5 0, 2023
A Hybrid Design for Low-Power Fault Tolerant One-Bit Full Adder for Neural Network Applications
C. Raji, S. N. Prasad
Lecture Notes in Electrical Engineering, 2022
Enhancing data security on a self-defensive Jacket Along with Microphone using IoT Technology and Cloud Computing
Malathi Acharya, Prasad S N
Proceedings of International Conference on Technological Advancements in Computational Sciences Ictacs 2022, 2022
Compressive Spectrum Sensing for Wideband Signals Using Improved Matching Pursuit Algorithms
R. Anupama, S. Y. Kulkarni, S. N. Prasad
Lecture Notes in Electrical Engineering, 2022
Assessment of Image Quality Metrics by Means of Various Preprocessing Filters for Lung CT Scan Images
Sugandha Saxena, S. N. Prasad, T. S. Deepthi Murthy
Lecture Notes in Electrical Engineering, 2022
Correction to: A Detailed Evolutionary Scrutiny of PEIS with GPS Fleet Tracker and AOMDV-SAPTV Based on Throughput, Delay, Accuracy, Error Rate, and Success Rate (Wireless Personal Communications, (2021), 121, 4, (2635-2651), 10.1007/s11277-021-08840-2)
M. Thurai Pandian, Sarappadi Narasimha Prasad, Manish Sharma
Wireless Personal Communications, 2021
A Detailed Evolutionary Scrutiny of PEIS with GPS Fleet Tracker and AOMDV-SAPTV Based on Throughput, Delay, Accuracy, Error Rate, and Success Rate
M. Thurai Pandian, Sarappadi Narasimha Prasad, Manish Sharma
Wireless Personal Communications, 2021
Self-Sequential Attention Layer Based DenseNet for Thoracic Diseases Detection
Roshan Shetty, , Prasad Sarappadi, and
International Journal of Intelligent Engineering and Systems, 2021
Comparative Study of Selector Device Design for Sneak Current in 3D Crosspoint ReRAM
U Dilna, S N Prasad
Mpcit 2020 Proceedings IEEE 3rd International Conference on Multimedia Processing Communication and Information Technology, 2020
Analogy of Algorithms for Automatic Epileptic Seizure Detection
Kavya B S, S N Prasad
Proceedings 5th IEEE International Conference on Recent Trends in Electronics Information and Communication Technology Rteict 2020, 2020
Managing inrush current using hot swap and oring fet
Prof. Raji. C*., , Mr. Mohan Kumar Velumani, Dr. S. N. Prasad, , and
International Journal of Innovative Technology and Exploring Engineering, 2019
Machine learning based sensitivity analysis for the applications in the prediction and detection of cancer disease
Sugandha Saxena, S. N Prasad
2019 IEEE International Conference on Distributed Computing VLSI Electrical Circuits and Robotics Discover 2019 Proceedings, 2019
Comparative study of Narrowband and Wideband Opportunistic Spectrum Access Techniques
Anupama R, S. Y. Kulkarni, S. N. Prasad
2019 IEEE International Conference on Distributed Computing VLSI Electrical Circuits and Robotics Discover 2019 Proceedings, 2019
CTF Controller for CT scan using beagle bone black
Vrashali Kathare, S N Prasad
2016 IEEE International Conference on Recent Trends in Electronics Information and Communication Technology Rteict 2016 Proceedings, 2017
The optimized data path ANN for low power and embedded applications
International Journal of Engineering and Technology, 2016
Low power datapath architecture for ANN
International Journal of Applied Engineering Research, 2015

Prasad Sarappadi Narasimha

EDUCATION

RESEARCH INTERESTS

Scopus Publications