eISSN: 3087-4262 editor@aimjournals.com
All Journals
Submit your article

International Journal of Intelligent Data and Machine Learning

Open Access Peer Review International
Open Access

ADVANCING ARTIFICIAL INTELLIGENCE: AN IN-DEPTH LOOK AT MACHINE LEARNING AND DEEP LEARNING ARCHITECTURES, METHODOLOGIES, APPLICATIONS, AND FUTURE TRENDS

DOI https://doi.org/10.55640/ijidml-v02i01-02
pdf
Dr. Tanay Deshpande 1 , Dr. Kavita Sharma 1 ,
4 Department of Computer Science, National Institute of Technology Karnataka, Surathkal, India
4 Department of Computer Applications, Jawaharlal Nehru University, New Delhi, India

Abstract

Artificial Intelligence (AI) has emerged as a transformative force across various domains, with Machine Learning (ML) and its subset, Deep Learning (DL), at its core. This article provides a comprehensive exploration of ML and DL, delving into their fundamental concepts, diverse architectural paradigms, typical workflow, and wide-ranging applications. We discuss the evolution from traditional ML algorithms to complex deep neural networks, highlighting key methodologies like supervised, unsupervised, and reinforcement learning. The article outlines the practical workflow involved in developing ML and DL solutions, from data acquisition to deployment. Furthermore, it showcases the profound impact of these technologies across sectors such as computer vision, natural language processing, healthcare, finance, agriculture, and robotics. Finally, we explore emerging trends and future directions, including the growing importance of Explainable AI (XAI), ethical considerations, federated learning, and quantum machine learning, underscoring the continuous evolution and societal implications of this rapidly advancing field.

References

Arel, I.; Rose, D.C.; Karnowski, T.P. Deep machine learning—A new frontier in artificial intelligence research [research frontier]. IEEE Comput. Intell. Mag. 2010, 5, 13–18.
Benos, L.; Tagarakis, A.C.; Dolias, G.; Berruto, R.; Kateris, D.; Bochtis, D. Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors 2021, 21, 3758.
Huang, J.; Chai, J.; Cho, S. Deep learning in finance and banking: A literature review and classification. Front. Bus. Res. China 2020, 14, 13.
Gambella, C.; Ghaddar, B.; Naoum-Sawaya, J. Optimization problems for machine learning: A survey. Eur. J. Oper. Res. 2021, 290, 807–828.
Vuong, Q. Machine Learning for Robotic Manipulation. 2021. Available online: https://arxiv.org/abs/2101.00755v1 (accessed on 11 April 2023).
Yuan, F.-G.; Zargar, S.A.; Chen, Q.; Wang, S. Machine learning for structural health monitoring: Challenges and opportunities. Sens. Smart Struct. Technol. Civ. Mech. Aerosp. Syst. 2020, 11379, 1137903.
Kubat, M. An Introduction to Machine Learning. In An Introduction to Machine Learning; Springer International Publishing: Cham, Switzerland, 2017; pp. 321–329.
Hinton, G.E.; Osindero, S.; Teh, Y.-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006, 18, 1527–1554.
Deng, L. Deep Learning: Methods and Applications. Found. Trends Signal Process. 2013, 7, 197–387.
Karhunen, J.; Raiko, T.; Cho, K. Unsupervised deep learning: A short review. In Advances in Independent Component Analysis and Learning Machines; Academic Press: Cambridge, MA, USA, 2015; pp. 125–142.
Du, K.L.; Swamy, M.N. Neural Networks and Statistical Learning, 2nd ed.; Springer Science & Business Media: London, UK, 2019; pp. 1–988.
Han, J.; Kamber, M.; Pei, J. Data Mining: Concepts and Techniques; Morgan Kaufmann: Waltham, MA, USA, 2012.
Haykin, S. Neural Networks and Learning Machines; Pearson Education USA: Upper Saddle River, NJ, USA, 2008.
Ahmad, J.; Farman, H.; Jan, Z. Deep learning methods and applications. In Deep Learning: Convergence to Big Data Analytics; SpringerBriefs in Computer Science; Springer: Singapore, 2019; pp. 31–42.
Deep Learning Techniques: An Overview|SpringerLink. Available online: https://link.springer.com/chapter/10.1007/978-981-15-3383-9_54 (accessed on 11 March 2023).
Srinivas, M.; Sucharitha, G.; Matta, A. Machine Learning Algorithms and Applications; Wiley: Hoboken, NJ, USA, 2021.
Janiesch, C.; Zschech, P.; Heinrich, K. Machine learning and deep learning. Electron. Mark. 2021, 31, 685–695.
Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Comput. Sci. 2021, 2, 160.
Hassanien, A.E.; Chang, K.C.; Mincong, T. (Eds.) Advanced Machine Learning Technologies and Applications; Springer Nature: Singapore, 2021; Volume 1141.
Schmidhuber, J. Deep Learning in Neural Networks: An Overview. Neural Netw. 2015, 61, 85–117.
Yosinski, J.; Clune, J.; Bengio, Y.; Lipson, H. How transferable are features in deep neural networks? Adv. Neural Inf. Process. Syst. 2014, 4, 3320–3328.
Cireşan, D.C.; Meier, U.; Masci, J.; Gambardella, L.M.; Schmidhuber, J. High-Performance Neural Networks for Visual Object Classification. arXiv 2011, arXiv:1102.0183.
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. Adv. Neural Inf. Process. Syst. 2019.
Zhang, Z.; Cui, P.; Zhu, W. Deep Learning on Graphs: A Survey. IEEE Trans. Knowl. Data Eng. 2020, 34, 249–270.
LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444.
Shrestha, A.; Mahmood, A. Review of Deep Learning Algorithms and Architectures. IEEE Access 2019, 7, 53040–53065.
Bengio, Y. Learning Deep Architectures for AI. Found. Trends Mach. Learn. 2009, 2, 1–127.
Mathew, A.; Amudha, P.; Sivakumari, S. Deep learning techniques: An overview. Adv. Intell. Syst. Comput. 2021, 1141, 599–608.
Deng, L. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans. Signal Inf. Process. 2014, 3, e2.

Most read articles by the same author(s)

1 2 3 4 5 6 7 > >>