Article Per Year (5 Year)
p-Index From 2021 - 2026
0.983
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences
Kolukula, Nitalaksheswara Rao
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
An efficient object detection by autonomous vehicle using deep learning Kolukula, Nitalaksheswara Rao; Kalapala, Rajendra Prasad; Ivaturi, Sundara Siva Rao; Tammineni, Ravi Kumar; Annavarapu, Mahalakshmi; Pyla, Uma
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4287-4295

Abstract

The automation industries have been developing since the first demonstration in the period 1980 to 2000 it is mainly used on automated driving vehicle. Now a day’s automotive companies, technology companies, government bodies, research institutions and academia, investors and venture capitalists are interested in autonomous vehicles. In this work, object detection on road is proposed, which uses deep learning (DL) algorithms. You only look once (YOLO V3, V4, V5). In this system object detection on the road data set is taken as input and the objects are mainly on-road vehicles, traffic signals, cars, trucks and buses. These inputs are given to the models to predict and detect the objects. The Performance of the proposed system is compared with performance of deep learning algorithms convolution neural network (CNN). The proposed system accuracy greater than 76.5% to 93.3%, mean average precision (Map) and frame per second (FPS) are 0.895 and 43.95%.
Bidirectional power converter for electrical vehicle with battery charging and smart battery management system Rajanna, Bodapati Venkata; Krishnaiah, Kondragunta Rama; Reddy, Ganta Raghotham; Ahammad, Shaik Hasane; Najumunnisa, Mohammad; Inthiyaz, Syed; Eragamreddy, Gouthami; Sudhakar, Ambarapu; Kolukula, Nitalaksheswara Rao
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i4.pp2592-2604

Abstract

In electric vehicles (EVs), efficient energy management is critical for reliable power transfer between the battery and motor. This paper presents the design and implementation of a bidirectional DC-DC converter equipped with a smart battery management system (BMS). The system supports bidirectional power flow, operating in boost mode during acceleration and buck mode during regenerative braking, thereby enhancing overall energy efficiency and vehicle performance. A PIC microcontroller governs the system, performing real-time monitoring of key battery parameters such as state of charge (SOC), state of health (SOH), voltage, and temperature. Safety features include automatic cooling fan activation when the temperature exceeds 45 °C and generator startup when battery voltage falls below 23 V. Real-time data is displayed via an LCD interface to improve user interaction and system transparency. The proposed system achieved a conversion efficiency of 90-93% during experimental testing, with stable switching, reliable automation, and effective thermal protection. The embedded energy management system optimizes charging and discharging cycles while preventing overcharging, deep discharge, and thermal stress. This intelligent, automated power converter enhances battery life, improves EV reliability, and contributes to sustainable transportation by enabling features like vehicle-to-grid (V2G) energy transfer. The proposed architecture is well-suited for integration into modern EV infrastructure. Although the system architecture supports future V2G integration, V2G functionality was not implemented or tested in the present experimental setup.
Artificial intelligence-based multi-key security for protected and transparent medical cloud storage Bagadi, Ravi Kiran; Koraganji, Neelima Santoshi; Venkata Seshukumari, Bandreddi; Karuturi, Kavya Ramya Sree; Abotula, Sireesha; Rajanna, Bodapati Venkata; Annavarapu, Mahalakshmi; Kolukula, Nitalaksheswara Rao; Pinajala, Jayasree; Meka, James Stephen
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1241-1250

Abstract

Ensuring the security and privacy for the patient medical records and medical reports data is a crucial challenge as cloud-based healthcare technologies become more prevalent. For cloud-hosted medical data, internet of things (IoT) and artificial intelligence (AI) technologies shows best solutions for the challenges in the medical domain. This study suggests a Secure and Transparent Multi-Key Authentication Framework that makes use of AI. Using Z-score normalization, the framework first preprocesses the data before clustering to create a multi-level multi-key security structure. The physics-informed triangulation aggregation neural network (PITANN) model in the study reduces computation costs by minimizing overhead, ensuring secure handling of location-based and medical data for enhanced data classification and encryption effectiveness. A multi-key derivation of an elliptic curve, the ElGamal cryptography scheme is presented, which allows for safe multi-key encryption with little increase in the length of the ciphertext. This method guarantees safe, confidential access to cloud-hosted encrypted health information. An envisioned amalgamation improves flexibility by enhancing performance metrics such as speed of computation while safeguarding patient information through enhanced security measures and ensuring precise medical record integrity within virtual healthcare systems.
Artificial intelligence framework for multi-stage lung disease detection with audio signals Venkata Seshukumari, Bandreddi; Tayi, Jyothirmayi; Bhuthkuri, Rajeshkhanna; Madireddy, Bhavani; Yellapu, Jhansi; Rajanna, Bodapati Venkata; Kolukula, Nitalaksheswara Rao; Kodali, Siva Sairam Prasad; Pinajala, Jayasree; Meka, James Stephen; Rami Reddy, Chilakala
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp106-115

Abstract

Automated diagnostic systems are increasingly pivotal in advancing the accuracy and efficiency of medical diagnostics. Due to abnormal changes in human life and pollution, lung disease and cancer cases increasing in huge number. Identification and prediction of lung diseases may help to increase the human life span. This study introduces a robust framework for automatic lung disease detection using respiratory sound signals. The methodology brings together a series of activities like preprocessing, feature extraction, selection, and classification to improve diagnostic accuracy. The adaptive empirical stockwell-transform (AEST) is used to enhance the quality of the signal, whereby extracting and refining features, mainly Mel-frequency cepstral coefficients (MFCC), and Mel-spectrograms, are used. The scalable convolutional geyser network (SCGN) helps to mitigate challenges posed by imbalanced datasets, redundant features, and overfitting, ensuring reliable classification of the features. The model is validated when using the International Conference on Biomedical and Health Informatics (ICBHI) dataset, which validates the performance indicators of the model (F1-score 0.94, accuracy 0.95, precision 0.93, recall 0.94). This is shown superior performance compared to other existing models and demonstrates the framework's ability to diagnose a serviceable and reliable medical diagnosis; which indicates the strengths of combining advances in signal processing and scalable deep learning (DL) in healthcare applications.
Analysis of different converter topologies for EV applications Rajanna, Bodapati Venkata; Krishnaiah, Kondragunta Rama; Girija, Sakimalla Prabhakar; Ahammad, Shaik Hasane; Najumunnisa, Mohammad; Inthiyaz, Syed; Eragamreddy, Gouthami; Ambati, Giriprasad; Kolukula, Nitalaksheswara Rao
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp518-532

Abstract

Electric vehicles (EVs) are gaining global prominence due to their high efficiency, low noise, and minimal carbon emissions. A critical aspect of EV performance lies in the interaction between energy storage systems (ESS) and power converters. Nonetheless, power delivery from storage units tends to be unreliable and needs strong converter units for effective and stable energy transmission. Several forms of direct current-to-direct current conversion systems used in electric vehicles are thoroughly examined in the paper, including both isolated and non-isolated designs such as those with the cuk, flyback, and push-pull architectures. The paper looks at converter categorization, control methods such as proportional-integral and artificial neural networks, as well as the method of modulation using unipolar and bipolar sinusoidal pulse-width modulation (PWM). Additionally, the role of optimization algorithms in improving converter performance is explored. Simulations were conducted using MATLAB/Simulink to evaluate each topology under varying load and input voltage conditions. The results demonstrate that the Push-Pull converter has the best efficiency for high-power applications, while the Cuk and Flyback converters are best for applications requiring continuous current and low-power, compact designs, respectively. This research offers insights for choosing optimal converter structures to improve energy efficiency and reliability of systems in electric vehicles.
Co-Authors Abotula, Sireesha Ahammad, Shaik Hasane Ambati, Giriprasad Annavarapu, Mahalakshmi Bagadi, Ravi Kiran Bhuthkuri, Rajeshkhanna Eragamreddy, Gouthami Girija, Sakimalla Prabhakar Inthiyaz, Syed Ivaturi, Sundara Siva Rao Kalapala, Rajendra Prasad Karuturi, Kavya Ramya Sree Kodali, Siva Sairam Prasad Koraganji, Neelima Santoshi Krishnaiah, Kondragunta Rama Madireddy, Bhavani Meka, James Stephen Najumunnisa, Mohammad Pinajala, Jayasree Pyla, Uma Rajanna, Bodapati Venkata Rami Reddy, Chilakala Reddy, Ganta Raghotham Sudhakar, Ambarapu Tammineni, Ravi Kumar Tayi, Jyothirmayi Venkata Seshukumari, Bandreddi Yellapu, Jhansi