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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Applied Power Engineering (IJAPE) International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science
Bodapati Venkata Rajanna
MLR Institute of Technology
Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Long range based effective field monitoring system Popuri Rajani Kumari; Chalasani Suneetha; Vadlamudi Sri Lakshmi; Nakka Rama Priya; Bodapati Venkata Rajanna; Ambarapu Sudhakar
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 2: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i2.pp847-853

Abstract

Adoption of the internet of things (IoT) is moving forward quickly because of the developments in communication protocols and technology involving sensors. The IoT is promoting real-time agricultural field monitoring from any distant place. For the IoT to be implemented effectively there are a number of agricultural issues related to less power usage and long-distance transfer of data are to be addressed. By using LoRa, which is a wireless communication system for IoT applications, these difficulties can be avoided when sending information from fields of crops to a web server. Acustomized sensor node and LoRa are used in this work to transmit continuously updated information to a remote server. Monitoring the quality of water, and reducing wasteful use of water are the main goals.
Smart IoT-based temperature-controlled cooling system for solar panels using Arduino Mula Sreenivasa Reddy; Kondragunta Rama Krishnaiah; Anjali Devi Gorla; Anantha Sravanthi Peddinti; Sanam Nagendram; Mohammad Najumunnisa; Bodapati Venkata Rajanna; Shaik Hasane Ahammad; Gongati Pandu Ranga Reddy
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i2.pp781-792

Abstract

The efficiency of solar photovoltaic panels declines significantly as their surface temperature increases beyond optimal levels. This paper presents a smart, temperature-controlled cooling system based on an Arduino UNO microcontroller to enhance solar panel performance by mitigating overheating. The system integrates a DS18B20 temperature sensor and a moisture sensor to monitor real-time environmental conditions. When the temperature exceeds a defined threshold, the Arduino activates a CPU fan and water pump to dissipate heat effectively. Experimental testing demonstrated an efficiency improvement of approximately 10% to 12% during peak solar conditions. A hysteresis logic-based system with autonomous control is used to control the amount of energy and water utilized, by only cooling when required. An LCD screen displays real-time information locally on-site, while an ESP8266 WiFi module sends information to a "cloud" so that remote monitoring can occur through the ThingSpeak cloud service. The entire system operates entirely from solar energy and is capable of being operated off-the-grid as well as being environmentally friendly. Due to its low cost, modularity, and energy efficiency, this smart cooling solution provides a viable solution for rural areas or areas with limited resources to enhance the performance of photovoltaic systems.
Analysis of different converter topologies for EV applications Bodapati Venkata Rajanna; Kondragunta Rama Krishnaiah; Sakimalla Prabhakar Girija; Shaik Hasane Ahammad; Mohammad Najumunnisa; Syed Inthiyaz; Gouthami Eragamreddy; Giriprasad Ambati; Nitalaksheswara Rao Kolukula
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.
Artificial intelligence framework for multi-stage lung disease detection with audio signals Bandreddi Venkata Seshukumari; Jyothirmayi Tayi; Rajeshkhanna Bhuthkuri; Bhavani Madireddy; Jhansi Yellapu; Bodapati Venkata Rajanna; Nitalaksheswara Rao Kolukula; Siva Sairam Prasad Kodali; Jayasree Pinajala; James Stephen Meka; Chilakala Rami Reddy
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.
Machine learning-driven prognostics for lithium-ion batteries: enhancing RUL prediction and performance in smart energy storage systems Bodapati Venkata Rajanna; Aaluri Seenu; Kondragunta Rama Krishnaiah; Anantha Sravanthi Peddinti; Nelaturi Nanda Prakash; Bandreddi Venkata Seshukumari; Giriprasad Ambati; Shaik Hasane Ahammad; Chakrapani Srivardhan Kumar; Allamraju Shubhangi Rao
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i1.pp257-274

Abstract

In the evolving landscape of energy systems, batteries play a critical role in enabling hybrid and stand-alone renewable energy storage solutions. Precisely estimating battery life and remaining useful operational life will go a long way in enhancing the efficiency of the system with assured reliability in smart power storage devices. This report comprehensively surveys advanced approaches in the management of batteries through state-of-the-art artificial intelligence tools-support vector machines, relevance vector machines (RVM), long short-term memory (LSTM) models, and bayesian filters-that are being used with a view to enhancing remaining useful life (RUL) estimates and making real-time system health monitoring capabilities possible. Modeling approaches surveyed include state estimation, capacity, and thermal management, while discussing their applicability to lithium-ion batteries. The review also explores publicly available battery datasets, feature engineering strategies, and hybrid diagnostic frameworks. A technoeconomic perspective is provided to assess system performance in renewable-integrated power grids. This paper aims to consolidate current knowledge, provide comparative insights into the strengths and limitations of different approaches, and highlight open research challenges to guide future developments in smart AI-enabled battery systems that support sustainable and resilient energy infrastructure.
An ensemble-based approach for breast cancer identification using mammography Naveen Ananda Kumar Joseph Annaiah; Nakka Thirupathi Rao; Balakesava Reddy Parvathala; Banana Omkar Lakshmi Jagan; Bodapati Venkata Rajanna
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp133-141

Abstract

Breast cancer is among the most common cancers in women worldwide; timely detection is vitally important for improving chances of survival. The present study examines an innovative machine learning technique for the diagnosis of breast cancer using the breast cancer Wisconsin (diagnostic) dataset from Kaggle. The dataset includes 569 instances, and each instance has 30 attributes derived from digitized fine needle aspiration (FNA) images of masses found in the breast. We will present an ensemble deep learning (DL) model fusing a convolutional neural network (CNN) and LRAlexNet architectures to increase the accuracy and robustness of this type of cancer diagnosis. CNN models are well-known for their power to capture spatial hierarchies in image data, and LRAlexNet is a specialized deep CNN that excels at image classification due to its depth and parameter optimization. In this work, we use the ability to extract features of CNNs along with the superior classification performance of LRAlexNet to distinguish between benign and malignant cancers. The model will be trained and validated on the curated breast imaging subset of the digital database for screening mammography (CBIS-DDSM) dataset, and performance will be evaluated using sensitivity, accuracy, specificity, and the area under the curve (AUC) for the receiver operating characteristic. The results show that the ensemble CNN-LRAlexNet model achieved superior accuracy for breast cancer prediction when compared to traditional machine learning methods.
Co-Authors Aaluri Seenu Allamraju Shubhangi Rao Ambarapu Sudhakar Anantha Sravanthi Peddinti Anjali Devi Gorla Balakesava Reddy Parvathala Banana Omkar Lakshmi Jagan Bandreddi Venkata Seshukumari Bhavani Madireddy Chakrapani Srivardhan Kumar Chalasani Suneetha Chilakala Rami Reddy Giriprasad Ambati Gongati Pandu Ranga Reddy Gouthami Eragamreddy James Stephen Meka Jayasree Pinajala Jhansi Yellapu Jyothirmayi Tayi Kondragunta Rama Krishnaiah Mohammad Najumunnisa Mula Sreenivasa Reddy Nakka Rama Priya Nakka Thirupathi Rao Naveen Ananda Kumar Joseph Annaiah Nelaturi Nanda Prakash Nitalaksheswara Rao Kolukula Popuri Rajani Kumari Rajeshkhanna Bhuthkuri Sakimalla Prabhakar Girija Sanam Nagendram Shaik Hasane Ahammad Siva Sairam Prasad Kodali Syed Inthiyaz Vadlamudi Sri Lakshmi