Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions

Mohammad Mustafa Taye

doi:10.3390/computers12050091

journal article Open Access Apr 25, 2023

Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions

Mohammad Mustafa Taye

Computers Vol. 12 No. 5 pp. 91 · MDPI AG

View at Publisher Save 10.3390/computers12050091

Abstract

In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance. Deep learning technology, which grew out of artificial neural networks (ANN), has become a big deal in computing because it can learn from data. The ability to learn enormous volumes of data is one of the benefits of deep learning. In the past few years, the field of deep learning has grown quickly, and it has been used successfully in a wide range of traditional fields. In numerous disciplines, including cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, deep learning has outperformed well-known machine learning approaches. In order to provide a more ideal starting point from which to create a comprehensive understanding of deep learning, also, this article aims to provide a more detailed overview of the most significant facets of deep learning, including the most current developments in the field. Moreover, this paper discusses the significance of deep learning and the various deep learning techniques and networks. Additionally, it provides an overview of real-world application areas where deep learning techniques can be utilised. We conclude by identifying possible characteristics for future generations of deep learning modelling and providing research suggestions. On the same hand, this article intends to provide a comprehensive overview of deep learning modelling that can serve as a resource for academics and industry people alike. Lastly, we provide additional issues and recommended solutions to assist researchers in comprehending the existing research gaps. Various approaches, deep learning architectures, strategies, and applications are discussed in this work.

Topics

No keywords indexed for this article. Browse by subject →

References

87

[1]

Arel "Deep machine learning—A new frontier in artificial intelligence research [research frontier]" IEEE Comput. Intell. Mag. (2010) 10.1109/mci.2010.938364

[2]

Benos, L., Tagarakis, A.C., Dolias, G., Berruto, R., Kateris, D., and Bochtis, D. (2021). Machine Learning in Agriculture: A Comprehensive Updated Review. Sensors, 21. 10.3390/s21113758

[3]

Huang "Deep learning in finance and banking: A literature review and classification" Front. Bus. Res. China (2020) 10.1186/s11782-020-00082-6

[4]

Gambella "Optimization problems for machine learning: A survey" Eur. J. Oper. Res. (2021) 10.1016/j.ejor.2020.08.045

[5]

Vuong, Q. (2023, April 11). Machine Learning for Robotic Manipulation. Available online: https://arxiv.org/abs/2101.00755v1.

[6]

Yuan "Machine learning for structural health monitoring: Challenges and opportunities" Sens. Smart Struct. Technol. Civ. Mech. Aerosp. Syst. (2020)

[7]

Kubat, M. (2017). An Introduction to Machine Learning, Springer International Publishing. 10.1007/978-3-319-63913-0

[8]

A Fast Learning Algorithm for Deep Belief Nets

Geoffrey E. Hinton, Simon Osindero, Yee-Whye Teh

Neural Computation 2006 10.1162/neco.2006.18.7.1527

[9]

Deng "Deep Learning: Methods and Applications" Found. Trends Signal Process. (2013) 10.1561/2000000039

[10]

Karhunen, J., Raiko, T., and Cho, K. (2015). Advances in Independent Component Analysis and Learning Machines, Academic Press.

[11]

Du, K.L., and Swamy, M.N. (2019). Neural Networks and Statistical Learning, Springer Science & Business Media. [2nd ed.]. 10.1007/978-1-4471-7452-3

[12]

Han, J., Kamber, M., and Pei, J. (2012). Data Mining: Concepts and Techniques, Morgan Kaufmann.

[13]

Haykin, S. (2008). Neural Networks and Learning Machines, Pearson Education USA.

[14]

Ahmad, J., Farman, H., and Jan, Z. (2019). Deep Learning: Convergence to Big Data Analytics, Springer. SpringerBriefs in Computer Science.

[15]

(2023, March 11). Deep Learning Techniques: An Overview|SpringerLink. Available online: https://link.springer.com/chapter/10.1007/978-981-15-3383-9_54.

[16]

Srinivas, M., Sucharitha, G., and Matta, A. (2021). Machine Learning Algorithms and Applications, Wiley. 10.1002/9781119769262

[17]

Janiesch "Machine learning and deep learning" Electron. Mark. (2021) 10.1007/s12525-021-00475-2

[18]

Sarker "Machine Learning: Algorithms, Real-World Applications and Research Directions" SN Comput. Sci. (2021) 10.1007/s42979-021-00592-x

[19]

Hassanien, A.E., Chang, K.C., and Mincong, T. (2021). Advanced Machine Learning Technologies and Applications, Springer Nature. 10.1007/978-3-030-69717-4

[20]

Deep learning in neural networks: An overview

Jürgen Schmidhuber

Neural Networks 2015 10.1016/j.neunet.2014.09.003

[21]

Yosinski "How transferable are features in deep neural networks?" Adv. Neural Inf. Process. Syst. (2014)

[22]

Cireşan, D.C., Meier, U., Masci, J., Gambardella, L.M., and Schmidhuber, J. (2011). High-Performance Neural Networks for Visual Object Classification. arXiv.

[23]

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Adv. Neural Inf. Process. Syst.

[24]

Zhang "Deep Learning on Graphs: A Survey" IEEE Trans. Knowl. Data Eng. (2020) 10.1109/tkde.2020.2981333

[25]

Deep learning

Yann LeCun, Yoshua Bengio, Geoffrey Hinton

Nature 2015 10.1038/nature14539

[26]

Shrestha "Review of Deep Learning Algorithms and Architectures" IEEE Access (2019) 10.1109/access.2019.2912200

[27]

Learning Deep Architectures for AI

Y. Bengio

Foundations and Trends® in Machine Learning 2009 10.1561/2200000006

[28]

Mathew "Deep learning techniques: An overview" Adv. Intell. Syst. Comput. (2021) 10.1007/978-981-15-3383-9_54

[29]

Deng "A tutorial survey of architectures, algorithms, and applications for deep learning" APSIPA Trans. Signal Inf. Process. (2014)

[30]

Osisanwo "Supervised Machine Learning Algorithms: Classification and Comparison" Int. J. Comput. Trends Technol. (2017) 10.14445/22312803/ijctt-v48p126

[31]

Nasteski "An overview of the supervised machine learning methods" Horizons. B (2017) 10.20544/horizons.b.04.1.17.p05

[32]

Panigrahi, A., Chen, Y., and Kuo, C.C.J. (2018). Analysis on Gradient Propagation in Batch Normalized Residual Networks. arXiv.

[33]

Kumari "Linear regression analysis study" J. Pr. Cardiovasc. Sci. (2018) 10.4103/jpcs.jpcs_8_18

[34]

Du, K.-L., and Swamy, M.N.S. (2019). Neural Networks and Statistical Learning, Springer. 10.1007/978-1-4471-7452-3

[35]

Swapna "CNN Architectures: Alex Net, Le Net, VGG, Google Net, Res Net" Int. J. Recent Technol. Eng. (2020)

[36]

Goodfellow "Generative Adversarial Networks" Commun. ACM (2014) 10.1145/3422622

[37]

Xu, W., Sun, H., Deng, C., and Tan, Y. (2017, January 4–9). Variational Autoencoders for Semi-supervised Text Classification. Proceedings of the 31st AAAI Conference on Artificial Intelligence, San Francisco, CA, USA. 10.1609/aaai.v31i1.10966

[38]

Kameoka "Supervised determined source separation with multichannel variational autoencoder" Neural Comput. (2019) 10.1162/neco_a_01217

[39]

Li, Y. (2017). Deep Reinforcement Learning: An Overview. arXiv.

[40]

Paliwal "Deep Reinforcement Learning" Smart Innov. Syst. Technol. (2022) 10.1007/978-3-030-92905-3_16

[41]

Arulkumaran "A Brief Survey of Deep Reinforcement Learning" IEEE Signal Process. Mag. (2017) 10.1109/msp.2017.2743240

[42]

Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K. (2016, January 19–24). Asynchronous Methods for Deep Reinforcement Learning. Proceedings of the 33rd International Conference on Machine Learning, ICML 2016, New York, NY, USA.

[43]

Van Hasselt, H., Guez, A., and Silver, D. (2016, January 12–17). Deep Reinforcement Learning with Double Q-learning. Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA. 10.1609/aaai.v30i1.10295

[44]

Mastering the game of Go with deep neural networks and tree search

David Silver, Aja Huang, Chris J. Maddison et al.

Nature 2016 10.1038/nature16961

[45]

Naeem "A reinforcement learning and deep learning based intelligent system for the support of impaired patients in home treatment" Expert Syst. Appl. (2021) 10.1016/j.eswa.2020.114285

[46]

Reynolds "Gaussian Mixture Models" Encycl. Biom. (2009)

[47]

Gradient-based learning applied to document recognition

Y. Lecun, L. Bottou, Y. Bengio et al.

Proceedings of the IEEE 1998 10.1109/5.726791

[48]

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Hoo-Chang Shin, Holger R. Roth, Mingchen Gao et al.

IEEE Transactions on Medical Imaging 2016 10.1109/tmi.2016.2528162

[49]

ImageNet classification with deep convolutional neural networks

Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

Communications of the ACM 2017 10.1145/3065386

[50]

Hinton "Deep belief networks" Scholarpedia (2009) 10.4249/scholarpedia.5947

Showing 50 of 87 references

Cited By

801

Machine learning for renewable energy advancements: Prospects and emerging techniques

Safiullah Khan, Juhi Jannat Mim · 2026

Energy Reports

Role of deep learning in battery management system (BMS) for electric vehicles – A review

Rasel Ahmed, Md. Shaharia Hossen · 2026

Energy Reports

Improving the response surface methodology optimization with metaheuristics: A practical approach

Jorge Ramos-Frutos, Javier Cruz-Salgado · 2026

Expert Systems with Applications

Deep Convolutional Neural Networks in Medical Image Analysis: A Review

Ibomoiye Domor Mienye, Theo G. Swart · 2025

Information

Machine learning in ocular oncology and oculoplasty: Transforming diagnosis and treatment

Dipali Mane, Khuspe Pankaj Ramdas · 2025

IP International Journal of Ocular...

A machine learning-based feature extraction method for image classification using ResNet architecture

Jing Liao, Linpei Guo · 2025

Digital Signal Processing

Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives

Yosri A. Fahim, Ibrahim W. Hasani · 2025

European Journal of Medical Researc...

Exploring the Unseen: A Survey of Multi-Sensor Fusion and the Role of Explainable AI (XAI) in Autonomous Vehicles

De Jong Yeong, Krishna Panduru · 2025

Sensors

Graph neural networks and transfer entropy enhance forecasting of mesozooplankton community dynamics

Minhyuk Jeung, Min-Chul Jang · 2025

Environmental Science and Ecotechno...

Machine Learning and Deep Learning Paradigms: From Techniques to Practical Applications and Research Frontiers

Kamran Razzaq, Mahmood Shah · 2025

Computers

The Role of Artificial Intelligence in Advancing Biosensor Technology: Past, Present, and Future Perspectives

Tuğba Akkaş, Mahshid Reshadsedghi · 2025

Advanced Materials

Hyperspectral Imaging and Deep Learning for Quality and Safety Inspection of Fruits and Vegetables: A Review

Chen Yang, Zhiming Guo · 2025

Journal of Agricultural and Food Ch...

Neuroimmune Dysregulation and AI-Driven Therapeutic Strategies in Alzheimer’s Disease

Shampa Ghosh, Rakesh Bhaskar · 2025

Cellular and Molecular Neurobiology

Analysis and Prediction of Traffic Conditions Using Machine Learning Models on Ikorodu Road in Lagos State, Nigeria

Udeme Udo Imoh, Majid Movahedi Rad · 2025

Infrastructures

Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning

Tasfiq E. Alam, Md Manjurul Ahsan · 2025

Machine Learning with Applications

Intelligent Fault Classification Exploration Inspired by Suprathreshold Stochastic Resonance

Ziheng Xu, Siliang Lu · 2025

IEEE Transactions on Instrumentatio...

Applications of artificial intelligence in regenerative dentistry: promoting stem cell therapy and the scaffold development

Elham Saberian, Andrej Jenča · 2024

Frontiers in Cell and Developmental...

Comparison of the Accuracy of Ground Reaction Force Component Estimation between Supervised Machine Learning and Deep Learning Methods Using Pressure Insoles

Amal Kammoun, Philippe Ravier · 2024

Sensors

Harnessing the Power of Mobile Phone Technology: Screening and Identifying Autism Spectrum Disorder With Smartphone Apps

Kavita Reddy, Amar Taksande · 2024

Cureus

Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications

Ibomoiye Domor Mienye, Theo G. Swart · 2024

Information

Metrics

801

Citations

87

References

Details

Published: Apr 25, 2023
Vol/Issue: 12(5)
Pages: 91
License: View

Authors

M

Mohammad Mustafa Taye

Data Science and Artificial Intelligence, Philadelphia University, Amman 19392, Jordan

Cite This Article

Mohammad Mustafa Taye (2023). Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers, 12(5), 91. https://doi.org/10.3390/computers12050091

Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions

You May Also Like