[1]  Y. Yang, Z. Chen, Y. Yan, M. Li, and T. Gegen, "A new method of image recognition based on deep learning generates adversarial networks and integrates traditional algorithms," Journal of Computing and Electronic Information Management, vol. 13, no. 1, pp. 57-61, 2024.

[2]  Y. Yan, S. He, Z. Yu, J. Yuan, Z. Liu, and Y. Chen, "Investigation of Customized Medical Decision Algorithms Utilizing Graph Neural Networks," arXiv preprint arXiv:2405.17460, 2024.

[3]  X. Yan, W. Wang, M. Xiao, Y. Li, and M. Gao, "Survival Prediction Across Diverse Cancer Types Using Neural Networks," arXiv preprint arXiv:2404.08713, 2024.

[4]  H. Zhang, S. Diao, Y. Yang, J. Zhong, and Y. Yan, "Multi-scale image recognition strategy based on convolutional neural network," Journal of Computing and Electronic Information Management, vol. 12, no. 3, pp. 107-113, 2024.

[5]  Z. Gao, Q. Wang, T. Mei, X. Cheng, Y. Zi, and H. Yang, "An Enhanced Encoder-Decoder Network Architecture for Reducing Information Loss in Image Semantic Segmentation," arXiv preprint arXiv:2406.01605, 2024.

[6]  T. Mei, Y. Zi, X. Cheng, Z. Gao, Q. Wang, and H. Yang, "Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks," arXiv preprint arXiv:2405.11704, 2024.

[7]  I. Lee and Y. J. Shin, "Machine learning for enterprises: Applications, algorithm selection, and challenges," Business Horizons, vol. 63, no. 2, pp. 157-170, 2020.

[8]  T. Kristiansen, "Pricing of Contracts for Difference in the Nordic market," Energy Policy, vol. 32, no. 9, pp. 1075-1085, 2004.

[9]  A. Kim, Y. Yang, S. Lessmann, T. Ma, M. C. Sung, and J. E. Johnson, "Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting," European Journal of Operational Research, vol. 283, no. 1, pp. 217-234, 2020.

[10] J. Yuan, L. Wu, Y. Gong, Z. Yu, Z. Liu, and S. He, "Research on Intelligent Aided Diagnosis System of Medical Image Based on Computer Deep Learning," arXiv preprint arXiv:2404.18419, 2024.

[11] S. Lu, Z. Liu, T. Liu, and W. Zhou, "Scaling-up medical vision-and-language representation learning with federated learning," Engineering Applications of Artificial Intelligence, vol. 126, p. 107037, 2023.

[12] Z. Yao, F. Lin, S. Chai, W. He, L. Dai, and X. Fei, "Integrating Medical Imaging and Clinical Reports Using Multimodal Deep Learning for Advanced Disease Analysis," arXiv preprint arXiv:2405.17459, 2024.

[13] L. Xiao, M. Li, Y. Feng, M. Wang, Z. Zhu, and Z. Chen, "Exploration of Attention Mechanism-Enhanced Deep Learning Models in the Mining of Medical Textual Data," arXiv preprint arXiv:2406.00016, 2024.

[14] Z. Liu and J. Song, "Comparison of Tree-based Feature Selection Algorithms on Biological Omics Dataset," in Proceedings of the 5th International Conference on Advances in Artificial Intelligence, pp. 165-169, November 2021.

[15] S. Chai, X. Fei, Y. Wang, L. Dai, and M. Sui, "Deep Learning-Based Lung Medical Image Recognition," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 3, pp. 100-105, 2024.

[16] Z. Gao, Q. Wang, T. Mei, X. Cheng, Y. Zi, and H. Yang, "An Enhanced Encoder-Decoder Network Architecture for Reducing Information Loss in Image Semantic Segmentation," arXiv preprint arXiv:2406.01605, 2024.

[17] J. Yao, T. Wu, and X. Zhang, "Improving depth gradient continuity in transformers: A comparative study on monocular depth estimation with cnn," arXiv preprint arXiv:2308.08333, 2023.

[18] W. Dai, J. Tao, X. Yan, Z. Feng, and J. Chen, "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, November 2023, IEEE.

[19] Q. Zhan, Y. Ma, E. Gao, D. Sun, and H. Yang, "Innovations in Time Related Expression Recognition Using LSTM Networks," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 3, pp. 120-125, 2024.

[20] S. Wang, Z. Liu, and B. Peng, "A Self-training Framework for Automated Medical Report Generation," in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 16443-16449, December 2023.

[21] A. W. Lo and A. Remorov, "Stop-loss strategies with serial correlation, regime switching, and transaction costs," Journal of Financial Markets, vol. 34, pp. 1-15, 2017.

[22] R. Salakhutdinov and I. Murray, "On the quantitative analysis of deep belief networks," in Proceedings of the 25th international conference on Machine learning, pp. 872-879, July 2008.

[23] P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P. A. Manzagol, and L. Bottou, "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion," Journal of Machine Learning Research, vol. 11, no. 12, 2010.

[24] Y. Li, X. Yan, M. Xiao, W. Wang, and F. Zhang, "Investigation of Creating Accessibility Linked Data Based on Publicly Available Accessibility Datasets," in Proceedings of the 2023 13th International Conference on Communication and Network Security, pp. 77–81, 2024, Association for Computing Machinery.

[25] M. S. Pepe, "Receiver operating characteristic methodology," Journal of the American Statistical Association, vol. 95, no. 449, pp. 308-311, 2000.

[26] J. Davis and M. Goadrich, "The relationship between Precision-Recall and ROC curves," in Proceedings of the 23rd international conference on Machine learning, pp. 233-240, June 2006. 

[27] J. M. Lobo, A. Jiménez-Valverde, and R. Real, "AUC: a misleading measure of the performance of predictive distribution models," Global Ecology and Biogeography, vol. 17, no. 2, pp. 145-151, 2008.

Columbia University, New York, USA