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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Tasnim, Nusrat
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Computer Science & IT Engineering

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Rooftops detection with YOLOv8 from aerial imagery and a brief review on rooftop photovoltaic potential assessment Ahmed, Md. Sabbir; Arman, Md. Shohel; Tasnim, Nusrat; Imran, Md Hafizul; Smmak, Musabbir; Bhuiyan, Touhid
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2282-2290

Abstract

Recent years have seen significant advancements in the switch from fossil fuel-based energy systems to renewable energy. Decentralized solar photovoltaic (PV) is one of the most promising energy sources since there is a lot of rooftop space, it is easy to install, and the cost of the PV panels is low. The determination of rooftop locations for PV installation is crucial for energy planning. With this context, this study aimed to detect the suitable rooftops of different shapes. The dataset of 5,076 building roofs used in this study was gathered by us utilizing a drone. This study identified ten distinct roof shapes accurately, including triangle, square, penta, hexa, hepta, octa, nona, deca, gabled roof, and hipped roof, using the most recent version of you only live once (YOLO), known as YOLOv8. Recent research revealed, YOLOv8 is more accurate than earlier YOLO models which is the reason of utilizing YOLOv8. Accuracy of this work of rooftops detection is 93.6%. Also, the precision, recall, and F1-score confidence curve showed good performances too. Finally, a brief review of the most recent studies on the evaluation of rooftop PV potential was conducted to provide insight into the use of solar energy.
BonoNet: a deep convolutional neural network for recognizing bangla compound characters Ahmed, Kazi Rifat; Jahan, Nusrat; Masud, Adiba; Tasnim, Nusrat; Sharmin, Sazia; Mim, Nusrat Jahan; Mahmud, Imran
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4171-4180

Abstract

The bangla alphabet includes vowels, consonants, and compound symbols. The compound nature of bangla is a product of combining two or more root bangla characters into one graph. They are difficult to differentiate because they have a sophisticated geometric shape and an immense variety of scripts used by different places and individuals. This is one of the greatest challenges in creating effective optical character recognition (OCR) systems for bangla. In this paper, a deep convolutional neural network (DCNN)-based system is presented to identify bangla compound characters with high precision. The model was trained using the AIBangla dataset. It has about 171 classes of bangla compound characters. A DCNN system, BonoNet, was designed to classify compound characters. BonoNet outperformed all other state-of-the-art architecture on the test set and improved over current state-of-the-art architecture methods. BonoNet will greatly improve the automation and analysis of the bangla language by accurately identifying these compound complex characters.
Co-Authors Ahmed, Kazi Rifat Ahmed, Md. Sabbir Arman, Md. Shohel Bhuiyan, Touhid Imran, Md Hafizul Jahan, Nusrat Mahmud, Imran Masud, Adiba Mim, Nusrat Jahan Sharmin, Sazia Smmak, Musabbir