Article section

Article title
Contributor
Abstract
About article
DOI
Citation style
Copyright
Open access
References
Research Article

Evaluating the Trade-offs Between Machine Learning and Deep Learning: A Multi-Dimensional Analysis

Authors

Abstract

The proliferation of artificial intelligence applications necessitates a clear understanding of the fundamental distinctions between Machine Learning (ML) and Deep Learning (DL) approaches. This study presents a systematic comparative analysis through a multi-dimensional evaluation framework. We analyzed 150 implementations across three domains (computer vision, natural language processing, and structured data analysis), evaluating performance metrics, resource utilization, and architectural complexities. Our findings reveal that while DL architectures achieve superior accuracy in complex pattern recognition tasks (mean improvement: 27.3%, p < 0.001), they require substantially higher computational resources (GPU utilization: 89.2% vs. 23.7% for ML). Traditional ML demonstrates notable advantages in scenarios with limited datasets (<10,000 samples), exhibiting 3.8x faster training times and a 72% lower memory footprint. To guide implementation decisions, we developed a quantitative decision matrix based on five critical parameters: data volume, computational constraints, problem complexity, interpretability requirements, and time sensitivity. The matrix achieved 91.4% accuracy in predicting the optimal approach across 50 independent test cases. This research provides empirical evidence for the trade-offs between ML and DL, offering practitioners a structured framework for algorithm selection while considering resource constraints and performance requirements.

Keywords:

Algorithm Selection Framework Deep Learning Computational Complexity Feature Engineering Machine Learning Neural Network Architectures

Article information

Journal

Journal of Computer, Software, and Program

Volume (Issue)

2(1), (2025)

Pages

10-18

Published

06-03-2025

How to Cite

Elmobark, N. (2025). Evaluating the Trade-offs Between Machine Learning and Deep Learning: A Multi-Dimensional Analysis. Journal of Computer, Software, and Program, 2(1), 10-18. https://doi.org/10.69739/jcsp.v2i1.254

References

Abbasi, M., Shahraki, A., & Taherkordi, A. (2021). Deep learning for Network Traffic Monitoring and Analysis (NTMA): A survey. Computer Communications, 170(3), 19-41. https://doi.org/10.1016/j.comcom.2021.01.021

Ahmed, A., Hasan, M. K., Aman, A. H. M., ..., & Leila, R. (2024). Review on hybrid deep learning models for enhancing encryption techniques against side channel attacks. IEEE Access, 12, 188435-188453. https://doi.org/10.1109/ACCESS.2024.3431218

Awad, M., & Khanna, R. (2015). Machine learning and knowledge discovery. In Efficient learning machines (pp. 5-23). https://doi.org/10.1007/978-1-4302-5990-9_2

Barbudo, R., Ventura, S., & Romero, J. R. (2023). Eight years of AutoML: Categorisation, review and trends. Knowledge and Information Systems, 65, 5097-5149. https://doi.org/10.1007/s10115-023-01935-1

Bharadiya, J. (2023). A review of Bayesian machine learning principles, methods, and applications. International Journal of Innovative Research in Science Engineering and Technology, 8(5), 2033. https://doi.org/10.5281/zenodo.8002438

Chen, C., Zhang, P., Zhang, H., ..., & Zhang, Y. (2020). Deep learning on computational-resource-limited platforms: A survey. Mobile Information Systems, 2020(4), 1-19. https://doi.org/10.1155/2020/8454327

Coroama, L., & Groza, A. (2022, September). Evaluation metrics in explainable artificial intelligence (XAI). In International conference on advanced research in technologies, information, innovation and sustainability (pp. 401-413). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-20319-0_30

Costa, A., Araújo, S. O., Peres, R. S., & Barata, J. (2024). Machine learning applications in manufacturing - Challenges, trends, and future directions. IEEE Open Journal of the Industrial Electronics Society, 5, 1085-1103. https://doi.org/10.1109/OJIES.2024.3431240

Degadwala, S., & Vyas, D. (2024). Systematic analysis of deep learning models vs. machine learning. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 10(4), 60-70. https://doi.org/10.32628/CSEIT24104108

Dolz, M. F., Barrachina Mir, S., Martínez Pérez, H., ..., & Tomás, A. E. (2023). Performance–energy trade-offs of deep learning convolution algorithms on ARM processors. The Journal of Supercomputing, 79(5), 6241-6266. https://doi.org/10.1007/s11227-023-05050-4

Frikha, M., Taouil, K., Fakhfakh, A., & Derbel, F. (2024). Predicting power consumption using deep learning with stationary wavelet. Forecasting, 6(3), 864-884. https://doi.org/10.3390/forecast6030043

Google Cloud for Education. (2023). High performance computing infrastructure guide. Google Cloud Documentation.

Grebovic, M., Filipovic, L., Katnic, I., ..., & Popovic, T. (2023). Machine learning models for statistical analysis. The International Arab Journal of Information Technology, 20(3A), 456-467. https://doi.org/10.34028/iajit/20/3A/8

Gropp, W., Banerjee, S., & Foster, I. (2020). Infrastructure for artificial intelligence, quantum and high performance computing. arXiv. https://doi.org/10.48550/arXiv.2012.09303

Haffner, O., Kučera, E., & Rosinova, D. (2024). Applications of machine learning and computer vision in Industry 4.0. Applied Sciences, 14(6), 2431. https://doi.org/10.3390/app14062431

Hagendorff, T., & Meding, K. (2021). Ethical considerations and statistical analysis of industry involvement in machine learning research. AI & Society, 38(1). https://doi.org/10.1007/s00146-021-01284-z

He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770-778). https://doi.org/10.1109/CVPR.2016.90

Joloudari, J. H., Mojrian, S., Saadatfar, H., ..., & Acharya, U. R. (2024). Resource allocation problem and artificial intelligence: The state-of-the-art review (2009–2023) and open research challenges. Multimedia Tools and Applications, 83(26), 1-44. https://doi.org/10.1007/s11042-024-18123-0

Jordan, M., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415

Khong, W. H., Soon, L.-K., & Goh, H. N. (2015). A comparative study of statistical and natural language processing techniques for sentiment analysis. Jurnal Teknologi, 77(18), 1-13. https://doi.org/10.11113/jt.v77.6502

Kotei, E., & Thirunavukarasu, R. (2023). A systematic review of transformer-based pre-trained language models through self-supervised learning. Information, 14(3), 187. https://doi.org/10.3390/info14030187

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097-1105. https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539

Lee, K., Thompson, S., & Wilson, R. (2023). Computational efficiency in modern machine learning systems. Journal of Machine Learning Research, 24, 1-28. https://www.jmlr.org/papers/volume24/22-1208/22-1208.pdf

Mishra, A. (2024). A comprehensive review of artificial intelligence and machine learning: Concepts, trends, and applications. International Journal of Scientific Research in Science and Technology, 11(5), 126-142. https://doi.org/10.32628/IJSRST2411587

Nanduri, V. S. (2024). AI hardware resource monitoring in the data center environment. International Journal of Scientific Research in Engineering and Management, 8(7), 1-11. https://doi.org/10.55041/IJSREM36782

Ndung’u, R. N. (2022). Data preparation for machine learning modelling. International Journal of Computer Applications Technology and Research, 11(6), 231-235. https://doi.org/10.7753/IJCATR1106.1008

Oreski, D., Oreski, S., & Klicek, B. (2016). Effects of dataset characteristics on the performance of feature selection techniques. Applied Soft Computing, 52, 109-119. https://doi.org/10.1016/j.asoc.2016.12.023

Rane, N., Mallick, S. K., Kaya, Ö., & Rane, J. (2024). Techniques and optimization algorithms in deep learning: A review. In Applied machine learning and deep learning: Architectures and techniques (Chapter 3). Deep Science Publishing. https://doi.org/10.70593/978-81-981271-4-3_3

Romero-Hall, E. (2020). Research methods in learning design and technology: A historical perspective of the last 40 years. In E. Romero-Hall (Ed.), Research methods in learning design and technology. https://doi.org/10.4324/9780429260919-1

Sarker, I. H. (2021). Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science, 2(6), Article 420. https://doi.org/10.1007/s42979-021-00815-1

Sarraf, A., Azhdari, M., & Sarraf, S. (2021). A comprehensive review of deep learning architectures for computer vision applications. American Scientific Research Journal for Engineering, Technology, and Sciences, 77(1), 1-29.

Serey, J., Alfaro, M., Fuertes, G., ..., & Sabattin, J. (2023). Pattern recognition and deep learning technologies, enablers of Industry 4.0, and their role in engineering research. Symmetry, 15(2), 535. https://doi.org/10.3390/sym15020535

Sweet, L. B., Müller, C., Anand, M., & Zscheischler, J. (2023). Cross-validation strategy impacts the performance and interpretation of machine learning models. Artificial Intelligence for the Earth Systems, 2(4), 1-35. https://doi.org/10.1175/AIES-D-23-0026.1

Yao, J., & Yuan, B. (2024). Optimization strategies for deep learning models in natural language processing. Journal of Theory and Practice of Engineering Science, 4(05), 80-87. https://doi.org/10.53469/jtpes.2024.04(05).11

Zhao, G., Song, S., Lin, H., & Jiang, W. (2023). Bayesian optimization machine learning models for true and fake news classification. In 2023 IEEE 6th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) (pp. 1-6). IEEE. https://doi.org/10.1109/ITNEC56291.2023.10082424

Downloads

Views

26

Downloads

15