Authors : Nurudeen Yemi Hussain

Volume/Issue : Volume 9 - 2024, Issue 10 - October

Google Scholar : https://tinyurl.com/bux5ksns

Scribd : https://tinyurl.com/25t2ranj

DOI : https://doi.org/10.38124/ijisrt/IJISRT24OCT1521

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Analyzing complex data from domains such as computer vision, natural language processing, and time- series data presents numerous challenges due to the high- dimensional and abstract nature of these datasets. Traditional machine learning approaches often require extensive feature engineering to extract meaningful representations. Deep learning architectures have emerged as powerful tools for automatically learning rich hierarchies of features and representations directly from raw data in an end-to-end manner. This paper reviews several widely used deep learning models and their application to feature extraction and representation learning for complex dataset analysis. Convolutional neural networks (CNNs) are effective for visual feature extraction tasks. CNNs leverage convolutional and pooling layers to learn hierarchies of local patterns, transforming raw pixel values into high-level abstract visual concepts. Recurrent neural networks (RNNs) such as LSTMs and GRUs are well-suited for modeling sequential data through their ability to maintain long- term temporal dependencies. They have achieved state- of-the-art performance on tasks involving audio, text, and time-series data. Autoencoders provide an unsupervised framework for learning compressed representations of data through reconstruction. Generative adversarial networks (GANs) have shown success in learning the underlying distributions of datasets to synthesize new samples. These deep learning architectures are applied to problems across domains using standard preprocessing, training procedures, and evaluation metrics. CNN- extracted image features outperform handcrafted counterparts on image classification benchmarks. RNN- learned word embedding capture semantic and syntactic relationships compared to bag-of-words methods. Visualizations of intermediate CNN and RNN layers reveal their discovery of progressively higher-level patterns. Auto encoders learn disentangled latent spaces separating essential factors of variation in data. Deep models provide performance gains over traditional pipelines through their automatic extraction of layered, abstract representations optimized directly for predictive tasks. Their learned features also enhance human interpretability and dataset insights. While deep learning has revolutionized representation learning, open challenges remain around model interpretability, training data efficiency, and scalability to massive, heterogeneous datasets. Therefore, deep architectures represent a transformative development in automated feature engineering for analyzing complex data.

Keywords : Deep Learning, Convolutional Neural Networks, Recurrent Neural Networks, Auto Encoders, Feature Extraction, Representation Learning, Computer Vision, Natural Language Processing.

References :

1. Abukmeil, M., Ferrari, S., Genovese, A., Piuri, V., & Scotti, F. (2021). A survey of unsupervised generative models for exploratory data analysis and representation learning. Acm computing surveys (csur), 54(5), 1-40.
2. Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., ... & Olah, C. (2016). Deep speech 2: End-to-end speech recognition in english and mandarin. In International conference on machine learning (pp. 173-182). PMLR.
3. Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016). Deep learning for computational biology. Molecular systems biology, 12(7), 878.
4. Bhatt, C. A., & Kankanhalli, M. S. (2011). Multimedia data mining: state of the art and challenges. Multimedia Tools and Applications, 51, 35-76.
5. Bisong, E., & Bisong, E. (2019). Recurrent Neural Networks (RNNs). Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners, 443-473.
6. Bouarara, H. A. (2021). Recurrent neural network (RNN) to analyse mental behaviour in social media. International Journal of Software Science and Computational Intelligence (IJSSCI), 13(3), 1-11.
7. Chauhan, N. K., & Singh, K. (2018, September). A review on conventional machine learning vs deep learning. In 2018 International conference on computing, power and communication technologies (GUCON) (pp. 347-352). IEEE.
8. Chauhan, R., Ghanshala, K. K., & Joshi, R. C. (2018, December). Convolutional neural network (CNN) for image detection and recognition. In 2018 first international conference on secure cyber computing and communication (ICSCCC) (pp. 278-282). IEEE.
9. Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2016). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.
10. Dertat, A. (2017, October 8). Applied deep learning - Part 3: Autoencoders. Medium. https://towardsdatascience.com/applied-deep-learning-part-3-autoencoders-1c083af4d798
11. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
12. Gonzalez, R. C. (2018). Deep convolutional neural networks [lecture notes]. IEEE Signal Processing Magazine, 35(6), 79-87.
13. Grave, E., Joulin, A., & Usunier, N. (2016). Improving neural language models with a continuous cache. arXiv preprint arXiv:1612.04426.
14. Grossberg, S. (2013). Recurrent neural networks. Scholarpedia, 8(2), 1888.
15. Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions. International Journal of Forecasting, 37(1), 388-427.
16. Hosseini, M. P., Lu, S., Kamaraj, K., Slowikowski, A., & Venkatesh, H. C. (2020). Deep learning architectures. Deep learning: concepts and architectures, 1-24.
17. Ketkar, N., Moolayil, J., Ketkar, N., & Moolayil, J. (2021). Convolutional neural networks. Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyTorch, 197-242.
18. Khamparia, A., & Singh, K. M. (2019). A systematic review on deep learning architectures and applications. Expert Systems, 36(3), e12400.
19. Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial intelligence review, 53, 5455-5516.
20. Kong, Q., Xia, Y., Fuhl, W., & Li, H. (2020). Cross-modal distillation for robust sound event recognition. In proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 14136-14145).
21. Li, Y., Yu, F., Shahbazi, A., Li, X., & Li, G. (2020). Clinical time-series analysis via Bayesian deep learning. Scientific reports, 10(1), 1-11.
22. Maas, A. L., Daly, R. E., Pham, P. T., Huang, D., Ng, A. Y., & Potts, C. (2011). Learning word vectors for sentiment analysis. In Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies (pp. 142-150).
23. Metzger, A., & Toscani, M. (2022). Unsupervised learning of haptic material properties. Elife, 11, e64876.
24. Miao, Y., & Blunsom, P. (2016, June). Language as a latent variable: Discrete generative models for sentence compression. In Empirical Methods in Natural Language Processing.
25. Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of big data, 2, 1-21.
26. Navidan, H., Moshiri, P. F., Nabati, M., Shahbazian, R., Ghorashi, S. A., Shah-Mansouri, V., & Windridge, D. (2021). Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation. Computer Networks, 194, 108149.
27. O'shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.
28. Pan, Z., Yu, W., Wang, B., Xie, H., Sheng, V. S., Lei, J., & Kwong, S. (2020). Loss functions of generative adversarial networks (GANs): Opportunities and challenges. IEEE Transactions on Emerging Topics in Computational Intelligence, 4(4), 500-522.
29. Recurrent neural network. (2023, July 19). Free Chatbot maker | Chatbot for Website, WhatsApp | BotPenguin. https://botpenguin.com/glossary/recurrent-neural-network
30. Salehi, P., Chalechale, A., & Taghizadeh, M. (2020). Generative adversarial networks (GANs): An overview of theoretical model, evaluation metrics, and recent developments. arXiv preprint arXiv:2005.13178.
31. Saxena, D., & Cao, J. (2021). Generative adversarial networks (GANs) challenges, solutions, and future directions. ACM Computing Surveys (CSUR), 54(3), 1-42.
32. Shah, S. (2022, March 15). Convolutional neural network: An overview. Analytics Vidhya. https://www.analyticsvidhya.com/blog/2022/01/convolutional-neural-network-an-overview/
33. Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306.
34. Wani, M. A., Bhat, F. A., Afzal, S., Khan, A. I., Wani, M. A., Bhat, F. A., ... & Khan, A. I. (2020). Introduction to deep learning. Advances in deep learning, 1-11.
35. Zhong, X., Gallagher, B., Liu, S., Kailkhura, B., Hiszpanski, A., & Han, T. Y. J. (2022). Explainable machine learning in materials science. npj computational materials, 8(1), 204.