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Articles

Intelligent garbage classification system based on improve MobileNetV3-Large

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Pages 1299-1321 | Received 23 Feb 2022, Accepted 12 Apr 2022, Published online: 26 Apr 2022

References

  • Ahuja Sanjay, K., Manoj Kumar, S., & Kiran Kumar, R. (2021). Optimized deep learning framework for detecting pitting corrosion based on image segmentation. International Journal of Performability Engineering, 17(7), 627–637. https://doi.org/10.23940/ijpe.21.07.p7.627637
  • Anggraini, N., Rahman, D. F., Wardhani, L. K., et al. (2020). Mobile based monitoring system for an automatic cat feeder using Raspberry Pi. 18(2), 10–38.
  • Chen, H., Dou, Q., Yu, L., Qin, J., & Heng, P.-A. (2018). Voxresnet: Deep voxelwise residual networks for brain segmentation from 3d mr images sciencedirect. NeuroImage, 170, 446–455. https://doi.org/10.1016/j.neuroimage.2017.04.041
  • Chusho, T., Ishigure, H., Konda, N., & Iwata, T. (2000). Component-based application development on architecture of a model, UI and components. Proceedings Seventh Asia-Pacific Software Engeering Conference, 349–353.
  • Demirtas, M., Calgan, H., Toufik, A., & Sedraoui, M. (2021). Small-signal modeling and robust multi-loop PID and H∞ controllers synthesis for a self-excited induction generator. ISA Transactions, 117(11), 234–250. https://doi.org/10.1016/j.isatra.2021.01.059
  • Hosny, K. M., Darwish, M. M., Li, K., Salah, A., Raja, G. (2021). COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi. PLOS ONE, 16(5), 1–18. https://doi.org/10.1371/journal.pone.0250688
  • Howard, A., Sandler, M., Chu, G., et al. (2019). Searching for mobilenetv3. Proceedings of the IEEE/CVF International Conference on Computer Vision, 1314–1324.
  • Hu, W. J., Xue, P. P., He, G. Y., & Tang, H. Y. (2021). Few shot object detection for headdresses and seats in Thangka Yidam based on ResNet and deformable convolution. Connection Science, 2021(1), 732–748.
  • Li, F., Tang, T., Tang, B., et al. (2020). Deep convolution domain-adversarial transfer learning for fault diagnosis of rolling bearings. Measurement, 169(5), 108339.
  • Li, J., Chen, J., Sheng, B., et al. (2021). Automatic detection and classification system of domestic waste via multi-model cascaded convolutional neural network. IEEE Transactions on Industrial Informatics, 5(1), 99–108.
  • Li, Y., & Chen, L. (2020). Improved LSTM data analysis system for IoT-based smart classroom. Journal of Intelligent and Fuzzy Systems, 39(4), 5141–5148. https://doi.org/10.3233/JIFS-179999
  • Liu, J., & Wang, X. (2020). Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model. Plant Methods, 16(1), 83–99. https://doi.org/10.1186/s13007-020-00624-2
  • Milanowska, K., Rother, M., Puton, T., Jeleniewicz, J., Rother, K., & Bujnicki, J. M. (2011). ModeRNA server: An online tool for modeling RNA 3D structures. Bioinformatics (oxford, England), 27(17), 2441–2442. https://doi.org/10.1093/bioinformatics/btr400
  • Razia Sulthana, A., Jovith, A., & Jaithunbi, A. (2021). LSTM and RNN to predict COVID cases: Lethality’s and tests in GCC nations and India. International Journal of Performability Engineering, 17(3), 299–306. https://doi.org/10.23940/ijpe.21.03.p5.299306
  • Shengting, W., Yuling, L., Ziran, Z., & Weng, T.-H. (2021). S_I_LSTM: Stock price prediction based on multiple data sources and sentiment analysis. Connection Science, 2021(1), 44–62.
  • Sherratt, F., Plummer, A., & Iravani, P. (2021). Understanding LSTM network behaviour of IMU-based locomotion mode recognition for applications in prostheses and wearables. Sensors, 21(4), 12–64. https://doi.org/10.3390/s21041264
  • Ubeda, J. (2019). Beginning robotics with Raspberry Pi and Arduino: Using python and OpenCV. Computing Reviews, 60(5), 188–188.
  • Vujovic, V., & Maksi movic, M. (2014). Raspberry Pi as a wireless sensor node: Performances and constraints, international convention on information & communication technology. Electronics & Microelectronics, 1013–1018.
  • Wen-Jie, L. V., Wei, X. H., Chen, Z. F., et al. (2020). The implementation of garbage classification software based on convolutional neural network. Computer Knowledge and Technology, 35(5), 30–34.
  • Wu, Y., Tao, Y., Deng, Z., Zhou, J., Xu, C., & Zhang, B. (2020). A fuzzy analysis framework for waste incineration power plant comprehensive benefit evaluation from refuse classification perspective. Journal of Cleaner Production, 258, 120734. https://doi.org/10.1016/j.jclepro.2020.120734
  • Zhang, J. M., Shen, S. Y., Song, T. Q., et al. (2004). Design and implementation of general purpose interface controller GPIO_WB IP core. Microelectronics & Computer, 21(6), 194–198.
  • Zhao, Y., He, K., & Qiao, Y. (2018a). ST-LDA: High quality similar words augmented LDA for service clustering. 18th International Conference, ICA3PP, 15–17.
  • Zhao, Y., Qiao, Y., & He, K. (2019). A novel tagging augmented LDA model for clustering. International Journal of Web Services Research, 16(3), 59–77. https://doi.org/10.4018/IJWSR.2019070104
  • Zhao, Y., Wang, C., Wang, J., & He, K. (2018b). Incorporating LDA with word embedding for web service clustering. International Journal of Web Services Research (IJWSR), 15(4), 29–44. https://doi.org/10.4018/IJWSR.2018100102
  • Zhao, Y., Xiaohong, P., Yan, W., Jingwei, S., & Xing, D. (2021). A prediction and discovery method of cloud API based on the multimodal compact LSTM model. Journal of Nonline and Convex Analysis, 22(10), 2267–2282.
  • Zhao, Y., & Zhen, C. (2021). Design method of intelligent classification garbage bin based on deep learning. Science Wind, 32(1), 1671–7341.