DesPatNet25: Data encryption standard cipher model for accurate automated construction site monitoring with sound signals
Article
Article Title | DesPatNet25: Data encryption standard cipher model for accurate automated construction site monitoring with sound signals |
---|---|
ERA Journal ID | 17852 |
Article Category | Article |
Authors | Akbal, Erhan, Barua, Prabal Datta, Dogan, Sengul, Tuncer, Turker and Acharya, U. Rajendra |
Journal Title | Expert Systems with Applications |
Journal Citation | 193, pp. 1-10 |
Article Number | 116447 |
Number of Pages | 10 |
Year | 2022 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0957-4174 |
1873-6793 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.eswa.2021.116447 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0957417421017322 |
Abstract | Nowadays, various construction site monitoring (CSM) models have been presented using sound signals. Many researchers have used deep learning (DL) networks to develop an accurate automated CSM model. These DL-based models require huge dataset to train the model and also such networks are complex. Hence, in this work, a novel hand-modeled automated system is developed using a public CSM sound dataset. The proposed model uses the first S-Box of the data encryption standard (DES) cipher as a feature generator by using two binary kernels. Using tent average pooling, sub-bands (compressed) sound signals are generated and the presented multiple kernelled DES pattern generates features from each signal. The proposed hand-modeled automated system extracts 25 feature vectors, hence it is named as DesPatNet25. The developed DesPatNet25 consists of: (i) feature vectors creation, (ii) feature selection using iterative neighborhood component analysis (INCA), and (iii) classification. Our proposed model attained accuracies of 96.77% and 97.05% using k-nearest neighbor (kNN) classifier with 10-fold cross-validation and hold-out validation (80:20 split ratio) techniques, respectively. These high classification accuracies clearly demonstrate the success of the DesPatNet25 model with sound signal classification for automated CSM tasks. |
Keywords | Construction site monitoring; DesPatNet25; ESC; Huge sound dataset; Vehicle identification using sound |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Ngee Ann Polytechnic, Singapore |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan | |
Firat University, Turkey | |
School of Business | |
University of Technology Sydney | |
Cogninet Australia, Australia |
https://research.usq.edu.au/item/yyw98/despatnet25-data-encryption-standard-cipher-model-for-accurate-automated-construction-site-monitoring-with-sound-signals
79
total views1
total downloads0
views this month0
downloads this month