Deep Learning for Cultural Heritage: A Mobile App for Monument Recognition Using Convolutional Neural Networks

被引：0

作者：

Hasan, Md. Samaun ^{[1
,2
,3
,4
]}

Uddin, Md. Salah ^{[1
]}

Hasan, Kazi Jahid ^{[1
]}

Rahman, Mizanur ^{[1
]}

Ali, Mohammad ^{[2
]}

机构：

[1] Daffodil International University, Dhaka

[2] Rajshahi University, Rajshahi

[3] Jahangirnagar University, Savar

[4] Visva-Bharati University, West Bengal, Santiniketan

来源：

International Journal of Interactive Mobile Technologies | 2025年 / 19卷 / 03期

关键词：

architectural heritage; convolutional neural network (CNN); deep learning; mobile apps;

D O I：

10.3991/ijim.v19i03.50935

中图分类号：

学科分类号：

摘要：

Convolutional neural networks (CNN) has multi-dimensional features that are inextricably interrelated to the extraordinary identification of visionary objects. Humans have a thirst of eagerness to know about ancient monuments. CNN can be a suitable tool for this purpose, in addition to some image processing approaches. This study has developed a mobile application for monument identification in two classes. The prominent contributions have been performed in the data preprocessing and feature extraction requirement using the Canny approach. In addition, CNN has been applied for the utmost level of recognition of the inputted visionary image to the mobile application using CNN. The proposed mechanism has obtained 90.68% accuracy in the testing period as its utmost accuracy result. The coagulation of CNN and other cutting-edge technology has enhanced the performance of the developed application. Moreover, these features have introduced this implementation as a comparatively desired and required app in daily utilization. © 2025 by the authors of this article.

引用

页码：22 / 40

页数：18

共 26 条

[21]

Xia L., Mi S., Zhang J., Luo J., Shen Z., Cheng Y., Dual-stream feature extraction network based on CNN and transformer for building extraction, Remote Sensing, 15, 10, (2023)

[22]

Wahi M., Et al., A deep learning classification approach using high spatial satellite images for detection of built-up areas in rural zones: Case study of Souss-Massa region-Morocco, Remote Sensing Applications: Society and Environment, 29, (2023)

[23]

Rawas S., Samala A. D., Revolutionizing brain tumor analysis: A fusion of ChatGPT and multi-modal CNN for unprecedented precision, Int. J. Onl. Eng. (iJOE), 20, 8, pp. 37-48, (2024)

[24]

Parmar K., Varma I. K., A comprehensive approach to enhancing doctor-patient interaction: Bridging the gap for better healthcare, International Journal of Online and Biomedical Engineering (iJOE), 20, 13, pp. 4-23, (2024)

[25]

Fernandez B., Javier M., Michael Hansmeyer’s algorithmic architecture: The transformative impact of 3D printing on architectural design and education, International Journal of Emerging Technologies in Learning (iJET), 19, 7, pp. 44-58, (2024)

[26]

Saliha E., Arzu O. Y., New building design in historical environment: Production process with optimization-based algorithm, International Journal of Architectural Computing, (2024)

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