Applications of Deep Learning Techniques
1. Introduction
2. The Present Issue
3. Future Directions
- The application of deep learning techniques for legal intelligence, such as legal text classification, argument mining, and judgment prediction;
- The application of deep learning techniques for natural language processing, such as information retrieval, text summarization, and sentiment analysis;
- The application of deep learning techniques for software engineering, including software development, software testing, and software maintenance;
- The application of deep learning techniques for healthcare and medical systems, such as precision medicine, drug discovery, molecular modeling, smart diagnostics, and medical imaging;
- The application of deep learning techniques for social media analysis, such as real-time violence detection, dis/misinformation, hate speech recognition, and country reputation monitoring;
- The application of deep learning techniques for academic data mining, such as information extraction from scientific text, innovation measurement, and citation analysis;
- The application of deep generative techniques for education, entertainment, finance, and materials science;
- The application of deep learning techniques in other special domains such as cybersecurity, business intelligence, Internet of Things, precious agriculture, and smart cities;
- Data quality evaluation, assurance, and improvement for deep learning in various applications;
- Responsibility, fairness, ethics, bias, trustworthiness, transparency, accountability, safety, and privacy in deep learning applications.
Acknowledgments
Conflicts of Interest
Abbreviations
DNN | Deep neural network |
CNN | Convolutional neural network |
GNN | Graph neural network |
DRL | Deep reinforcement learning |
LLM | Large language model |
List of Contributions
- Yang, J.; Kim, J.; Ryu, H.; Lee, J.; Park, C. Predicting Car Rental Prices: A Comparative Analysis of Machine Learning Models. Electronics 2024, 13, 2345.
- Kumar, Y.; Huang, K.; Lin, C.-C.; Watson, A.; Li, J.J.; Morreale, P.; Delgado, J. Applying Swin Architecture to Diverse Sign Language Datasets. Electronics 2024, 13, 1509.
- Tran, L.T.T.; Kim, H.-G.; La, H.M.; Van Pham, S. Automatic Speech Recognition of Vietnamese for a New Large-Scale Corpus. Electronics 2024, 13, 977. https://doi.org/10.3390/electronics13050977.
- Li, W.; Yang, X.; Yuan, G.; Xu, D. ATCNet: A Novel Approach for Predicting Highway Visibility Using Attention-Enhanced Transformer–Capsule Networks. Electronics 2024, 13, 920.
- Lu, Y.; Zheng, C.; Zheng, S.; Ma, J.; Wu, Z.; Wu, F.; Shen, Y. Multi-Spatio-Temporal Convolutional Neural Network for Short-Term Metro Passenger Flow Prediction. Electronics 2023, 13, 181.
- Lu, J.; Lee, S.-H.; Kim, I.-W.; Kim, W.-J.; Lee, M.-S. Small Foreign Object Detection in Automated Sugar Dispensing Processes Based on Lightweight Deep Learning Networks. Electronics 2023, 12, 4621. https://doi.org/10.3390/electronics12224621.
- Han, B.; Shi, L.; Wang, X.; Zhuang, L. Multi-Agent Multi-Target Pursuit with Dynamic Target Allocation and Actor Network Optimization. Electronics 2023, 12, 4613.
- Yang, Y.; Tan, C.; Cheng, Y.; Luo, X.; Qiu, X. Using a Deep Neural Network with Small Datasets to Predict the Initial Production of Tight Oil Horizontal Wells. Electronics 2023, 12, 4570.
- Zhao, Y.; Xie, J.; He, P. Deep Learning Neural Network-Based Detection of Wafer Marking Character Recognition in Complex Backgrounds. Electronics 2023, 12, 4293.
- Yahia, Y.; Lopes, J.C.; Lopes, R.P. Computer Vision Algorithms for 3D Object Recognition and Orientation: A Bibliometric Study. Electronics 2023, 12, 4218. https://doi.org/10.3390/electronics12204218.
- Kim, J.; Koo, S.; Lim, H. A Multi-Faceted Exploration Incorporating Question Difficulty in Knowledge Tracing for English Proficiency Assessment. Electronics 2023, 12, 4171.
- Li, W.; Hu, C.; Luo, Y. A Deep Learning Approach with Extensive Sentiment Analysis for Quantitative Investment. Electronics 2023, 12, 3960.
- Guo, Y.; Mao, Z. Long-Term Prediction Model for NOx Emission Based on LSTM–Transformer. Electronics 2023, 12, 3929. https://doi.org/10.3390/electronics12183929.
- Li, T.; Li, T.; Su, R.; Xin, J.; Han, D. Classification and Recognition of Goat Movement Behavior Based on SL-WOA-XGBoost. Electronics 2023, 12, 3506.
- Li, J.; Wang, Y.; Li, L.; Xiong, C.; Zhou, H. Deconvolutional Neural Network for Generating Spray Trajectory of Shoe Soles. Electronics 2023, 12, 3470.
References
- LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444. [Google Scholar] [CrossRef] [PubMed]
- Zhao, H.; Chen, H.; Ruggles, T.A.; Feng, Y.; Singh, D.; Yoon, H.-J. Improving Text Classification with Large Language Model-Based Data Augmentation. Electronics 2024, 13, 2535. [Google Scholar] [CrossRef]
- Pouyanfar, S.; Sadiq, S.; Yan, Y.; Tian, H.; Tao, Y.; Reyes, M.P.; Shyu, M.L.; Chen, S.C.; Iyengar, S.S. A survey on deep learning: Algorithms, techniques, and applications. ACM Comput. Surv. (CSUR) 2018, 51, 1–36. [Google Scholar] [CrossRef]
- Minaee, S.; Mikolov, T.; Nikzad, N.; Chenaghlu, M.; Socher, R.; Amatriain, X.; Gao, J. Large language models: A survey. arXiv 2024, arXiv:2402.06196. [Google Scholar]
- Chen, H.; Wu, L.; Chen, J.; Lu, W.; Ding, J. A comparative study of automated legal text classification using random forests and deep learning. Inf. Process. Manag. 2022, 59, 102798. [Google Scholar] [CrossRef]
- Gozalo-Brizuela, R.; Garrido-Merchán, E.C. A survey of Generative AI Applications. arXiv 2023, arXiv:2306.02781. [Google Scholar] [CrossRef]
- Silver, D.; Schrittwieser, J.; Simonyan, K.; Antonoglou, I.; Huang, A.; Guez, A.; Hubert, T.; Baker, L.; Lai, M.; Bolton, A.; et al. Mastering the game of go without human knowledge. Nature 2017, 550, 354–359. [Google Scholar] [CrossRef] [PubMed]
- Hoy, M.B. Alexa, Siri, Cortana, and more: An introduction to voice assistants. Med Ref. Serv. Q. 2018, 37, 81–88. [Google Scholar] [CrossRef] [PubMed]
- Abramson, J.; Adler, J.; Dunger, J.; Evans, R.; Green, T.; Pritzel, A.; Ronneberger, O.; Willmore, L.; Ballard, A.J.; Bambrick, J.; et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 2024, 630, 493–500. [Google Scholar] [CrossRef] [PubMed]
- Chen, H.; Pieptea, L.F.; Ding, J. Construction and evaluation of a high-quality corpus for legal intelligence using semiautomated approaches. IEEE Trans. Reliab. 2022, 71, 657–673. [Google Scholar] [CrossRef]
- Chen, H.; Chen, J.; Ding, J. Data evaluation and enhancement for quality improvement of machine learning. IEEE Trans. Reliab. 2021, 70, 831–847. [Google Scholar]
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Ding, J.; Chen, H.; Feng, Y.; Hossain, T. Applications of Deep Learning Techniques. Electronics 2024, 13, 3354. https://doi.org/10.3390/electronics13173354
Ding J, Chen H, Feng Y, Hossain T. Applications of Deep Learning Techniques. Electronics. 2024; 13(17):3354. https://doi.org/10.3390/electronics13173354
Chicago/Turabian StyleDing, Junhua, Haihua Chen, Yunhe Feng, and Tozammel Hossain. 2024. "Applications of Deep Learning Techniques" Electronics 13, no. 17: 3354. https://doi.org/10.3390/electronics13173354