Open Access

Systematic Analysis of Deep Learning Models for Performance Assessment

https://doi.org/10.55640/ijnget-v03i08-05

PDF

Dr. Aleksandar Iliev ¹ , Dr. Elena Stojanovsk ¹ ,

⁴ Department of Artificial Intelligence and Intelligent Computing Balkan Institute of Digital Research Skopje, North Macedonia

⁴ Center for Machine Learning and Data Analytics Macedonian Institute of Emerging Technologies Bitola, North Macedonia

Abstract

Deep learning has evolved into a major computational paradigm for solving complex problems involving image understanding, remote sensing, human activity recognition, graph-based learning, and other data-intensive applications. However, the increasing diversity of deep learning architectures has made model selection and performance assessment more difficult because predictive effectiveness must be evaluated alongside computational complexity, robustness, scalability, and deployment requirements. This article presents a systematic analysis of deep learning models by synthesizing existing research on fundamental deep learning techniques, neural network architectures, application-specific models, robustness, and hardware acceleration. The review comparatively examines conventional deep learning approaches, convolutional neural networks, graph convolutional neural networks, robust deep learning models, and application-oriented architectures. Particular attention is given to the relationship between model architecture and performance dimensions such as accuracy, generalization, computational efficiency, scalability, and practical deployability. The analysis indicates that no single deep learning architecture is universally optimal; rather, performance depends strongly on data characteristics, task requirements, architectural complexity, and computational resources. CNN-oriented models demonstrate strong suitability for spatial and visual information, graph-based approaches provide advantages for relational data but introduce substantial acceleration challenges, and robust learning approaches address reliability concerns that conventional accuracy-oriented evaluation may overlook. The study establishes a multidimensional framework for assessing deep learning performance and identifies the need for evaluation strategies that jointly consider predictive capability, robustness, computational cost, and deployment constraints.

Keywords

Deep Learning, Model Performance, Neural Network Architectures, CNN

References

L. Alzubaidi et al., “Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions,” J. Big Data, vol. 8, pp. 1–74, 2021.

W. Han et al., “A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities,” ISPRS J. Photo gramm. Remote. Sens., vol. 202, pp. 87–113, 2023.

C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electron. Mark., vol. 31, no. 3, pp. 685–695, 2021.

S. Li, Y. Tao, E. Tang, T. Xie, and R. Chen, “A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: Challenges and opportunities,” PeerJ Comput. Sci., vol. 8, no. 9, 2022, Art. no. e1166.

J. Liu and Y. Jin, “A comprehensive survey of robust deep learning in computer vision,” J. Autom. Intell., vol. 2, no. 4, pp. 175–195, 2023.

A. Mathew, P. Amudha, and S. Sivakumari, “Deep learning techniques: An overview,” in Adv. Mach. Learn. Technol. App.: AMLTA 2020, 2021, pp. 599–608.

P. P. Shinde and S. Shah, “A review of machine learning and deep learning applications,” in 4th Int. Conf. Comput. Commun. Ctrl. Autom. (ICCUBEA), Pune, India, IEEE, Aug. 16–18, 2018, pp. 1–6.

A. Shrestha and A. Mahmood, “Review of deep learning algorithms and architectures,” IEEE Access, vol. 7, pp. 53040–53065, 2019.

M. A. Wani, F. A. Bhat, S. Afzal, and A. I. Khan, Advances in Deep Learning. Singapore: Springer, 2020.

S. Zhang et al., “Deep learning in human activity recognition with wearable sensors: A review on advances,” Sensors, vol. 22, no. 4, Feb. 14, 2022.

K. Ramamurthy, R. K. Konduru and N. Amanmadov, "Evo Graph Coder: An Evolutionary Graph-Reasoning Framework for Self-Adaptive Software Engineering," in IEEE Access, vol. 14, pp. 63063-63076, 2026, doi: 10.1109/ACCESS.2026.3686019.

Geo Philip, Paulson, Integrated Intelligent Building Energy Management: A Multi-Layer Framework for Renewable Energy, HVAC Optimization, and Smart Electrical Network Coordination. Available at SSRN: https://ssrn.com/abstract=6993209 or http://dx.doi.org/10.2139/ssrn.6993209

M. R. Marri, S. B. Kurada, S. Gupta, S. Dey, S. Kumar and R. Chauhan, "Graph-Based Deep Learning Model for Identifying Cyber Threats in Cloud Platforms," 2025 International Conference on Computational Intelligence, Security, and Artificial Intelligence (IntelliSecAI), Al-Khobar, Saudi Arabia, 2025, pp. 1-7, doi: 10.1109/IntelliSecAI66368.2025.11472831.

Most read articles by the same author(s)

<< < 6 7 8 9 10 11 12 13 > >>

International Journal of Next-Generation Engineering and Technology

Systematic Analysis of Deep Learning Models for Performance Assessment

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles