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All Journal International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics
Venkata Rajanna, Bodapati
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Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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A high-efficiency transformerless buck-boost inverter with fuzzy logic control for grid-connected solar PV systems Venkata Rajanna, Bodapati; Rama Krishnaiah, Kondragunta; Ramaiah, Veerlapati; Ahammad, Shaik Hasane; Najumunnisa, Mohammad; Inthiyaz, Syed; Rao Kolukula, Nitalaksheswara; Sudhakar, Ambarapu
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10752

Abstract

Transformerless inverters are increasingly favored in grid-connected photovoltaic (PV) systems due to their higher efficiency, reduced size, and lower cost. This paper presents a novel transformerless inverter topology that integrates buck boost conversion with an advanced fuzzy logic controller (FLC) to enhance energy extraction and power quality under dynamically changing solar conditions. The proposed system employs a sine triangle pulse width modulation (PWM) scheme in conjunction with the FLC to improve waveform quality and system responsiveness. By dynamically adapting to variations in irradiance and load, the control strategy reduces the total harmonic distortion (THD) from 36.51% to 1.51%, significantly enhancing compliance with international grid standards. Additionally, a novel grounding technique is implemented to mitigate common mode leakage currents, a typical issue in transformerless systems, without the need for galvanic isolation. Comprehensive MATLAB/Simulink simulations validate the inverter’s performance, demonstrating superior dynamic behavior, harmonic suppression, and overall reliability. The proposed architecture offers a compact, cost effective, and high performance solution for next generation grid integrated solar PV systems.
Li-Fi technology for automated transport Kumari, Popuri Rajani; Suneetha, Chalasani; Anil Kumar, Maddali; Mrudula, Tangirala; Venkatachalam, Anbumani; Venkata Rajanna, Bodapati; Ambati, Giriprasad
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.pp1129-1136

Abstract

India is now one of the countries that is growing quickly worldwide. Today, practically for everything, a vehicle is necessary. Vehicle production is growing rapidly. One of the downsides of this enormous increase is the ineffective management of traffic. The well-planned expansion of transport organizations has resulted in a variety of challenges with travel. It is detrimental to both mankind and the economy when emergency vehicles like ambulances and fire engines are late in arriving. Smart transport is the most effective strategy to lower vehicle accidents and communicate with other cars to open a way for emergency vehicles. Here, the preliminary ideas and findings of a small-scale model of an automated transport system are presented using an innovative discovery known as Li-Fi, also known as light-fidelity. Full duplex communication is accomplished with Li-Fi, in which light is modified at speeds that are too rapid for the eye to follow. Li Fi may be used to create intelligent transportation systems since it offers various advantages over other communication protocols.
An ensemble-based approach for breast cancer identification using mammography Joseph Annaiah, Naveen Ananda Kumar; Thirupathi Rao, Nakka; Reddy Parvathala, Balakesava; Lakshmi Jagan, Banana Omkar; Venkata Rajanna, Bodapati
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 Ahammad, Shaik Hasane Ambati, Giriprasad Anil Kumar, Maddali Inthiyaz, Syed Joseph Annaiah, Naveen Ananda Kumar Kumari, Popuri Rajani Lakshmi Jagan, Banana Omkar Mrudula, Tangirala Najumunnisa, Mohammad Rama Krishnaiah, Kondragunta Ramaiah, Veerlapati Rao Kolukula, Nitalaksheswara Reddy Parvathala, Balakesava Sudhakar, Ambarapu Suneetha, Chalasani Thirupathi Rao, Nakka Venkatachalam, Anbumani