Research on Intelligent System of Multimodal Deep Learning in Image Recognition
DOI:
https://doi.org/10.54097/wau9262qKeywords:
Single Frame Image Denoising, Object Segmentation, Sparse Representation, Deep Neural NetworkAbstract
In this paper, a multi-scale image estimation method based on wavelet transform is proposed, which can effectively remove motion features from multiple videos. Then the autoencoder with sparsity limit is used to adjust the input signal to achieve effective compression. The effective features are extracted and the optimal unique vector is learned. The improved convolutional neural network is used to recognize weak moving objects. Experiments show that the algorithm can achieve high accuracy without large-scale learning samples, and the highest recognition rate is 99.36%. This algorithm has a great improvement over conventional algorithm.
References
Wang, X. S., Turner, J. D., & Mann, B. P. (2021). Constrained attractor selection using deep reinforcement learning. Journal of Vibration and Control, 27(5-6), 502-514.
Liu, Z., Yang, Y., Pan, Z., Sharma, A., Hasan, A., Ding, C., ... & Geng, T. (2023, July). Ising-cf: A pathbreaking collaborative filtering method through efficient ising machine learning. In 2023 60th ACM/IEEE Design Automation Conference (DAC) (pp. 1-6). IEEE.
Zi, Y., Wang, Q., Gao, Z., Cheng, X., & Mei, T. (2024). Research on the Application of Deep Learning in Medical Image Segmentation and 3D Reconstruction. Academic Journal of Science and Technology, 10(2), 8-12.
Yan, C., Qiu, Y., Zhu, Y. (2021). Predict Oil Production with LSTM Neural Network. In: Liu, Q., Liu, X., Li, L., Zhou, H., Zhao, HH. (eds) Proceedings of the 9th International Conference on Computer Engineering and Networks . Advances in Intelligent Systems and Computing, vol 1143. Springer, Singapore. https://doi.org/10.1007/978-981-15-3753-0_34.
Xin Chen , Yuxiang Hu, Ting Xu, Haowei Yang, Tong Wu. (2024). Advancements in AI for Oncology: Developing an Enhanced YOLOv5-based Cancer Cell Detection System. International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 12(2),75-80, doi:10.55524/ijircst.2024.12.2.13.
Yan, X., Wang, W., Xiao, M., Li, Y., & Gao, M. (2024). Survival prediction across diverse cancer types using neural networks. doi:10.48550/ARXIV.2404.08713
Li, S., Kou, P., Ma, M., Yang, H., Huang, S., & Yang, Z. (2024). Application of Semi-supervised Learning in Image Classification: Research on Fusion of Labeled and Unlabeled Data. IEEE Access.
Yao, J., Wu, T., & Zhang, X. (2023). Improving depth gradient continuity in transformers: A comparative study on monocular depth estimation with cnn. arXiv preprint arXiv:2308.08333.
Yulu Gong , Haoxin Zhang, Ruilin Xu, Zhou Yu, Jingbo Zhang. (2024). Innovative Deep Learning Methods for Precancerous Lesion Detection. International Journal of Innovative Research in Computer Science and Technology (IJIRCST), 12(2),81-86, doi:10.55524/ijircst.2024.12.2.14.
Xiao, M., Li, Y., Yan, X., Gao, M., & Wang, W. (2024). Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example. doi:10.48550/ARXIV.2404.08279
Guo, A., Hao, Y., Wu, C., Haghi, P., Pan, Z., Si, M., ... & Geng, T. (2023, June). Software-hardware co-design of heterogeneous SmartNIC system for recommendation models inference and training. In Proceedings of the 37th International Conference on Supercomputing (pp. 336-347).
Hu, Z., Li, J., Pan, Z., Zhou, S., Yang, L., Ding, C., ... & Jiang, W. (2022, October). On the design of quantum graph convolutional neural network in the nisq-era and beyond. In 2022 IEEE 40th International Conference on Computer Design (ICCD) (pp. 290-297). IEEE.
Wang, X. S., & Mann, B. P. (2020). Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learning. arXiv preprint arXiv:2010.01255.
Dai, W., Tao, J., Yan, X., Feng, Z., & Chen, J. (2023, November). Addressing Unintended Bias in Toxicity Detection: An LSTM and Attention-Based Approach. In 2023 5th International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 375-379). IEEE.
Liu, Y., Yang, H., & Wu, C. (2023). Unveiling patterns: A study on semi-supervised classification of strip surface defects. IEEE Access, 11, 119933-119946.
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