A Comprehensive Crucial Review of Re-Purposing DNN-Based Systems: Significance, Challenges, and Future Directions

• Published in: International journal of advanced computer science & applications Vol. 15; no. 10
• Main Authors: Al-Hamzi, Yaser M, Sahibuddin, Shamsul Bin
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 01.01.2024
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Complexity; Computation; Computer science; Data analysis; Deep learning; Engineering; Engineers; Incompatibility; Industry 4.0; Interoperability; Machine learning; Massive data points; Neural networks; Paradigms; Pattern analysis; Reengineering; Software; Malaysia
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2024.0151059

The fourth industrial revolution is marked by the significance of artificial intelligence (AI), particularly the remarkable progress in deep neural networks (DNNs). These networks have become crucial in various areas of daily life because of their remarkable pattern-learning capabilities on massive datasets. However, the incompatibility of these systems makes reutilizing them for efficient data analysis and computation highly intricate and challenging due to their fragmentation, internal structure, and complexity. Training in DNNs, a vital essential activity in model development, is often time-consuming and costly intensive computation. More precisely, reusing the entire model during deployment when only a small portion of its required features will result in excessive overhead. On the other hand, reengineering the model without efficient code review could also pose security risks as the model would inherit its defects and weaknesses. This paper comprehensively reviews DNN-based systems, encompassing cutting-edge frameworks, algorithms, and models for complex data and existent limitations. The study, which results from a thorough examination, analysis, and synthesis of observations from 193 recent scholarly papers, provides a wealth of knowledge on the subject, identifying key issues and future research directions by offering novel guidelines to advance the DNN model’s repurposing and adaptation, especially in finance, healthcare, and autonomous applications. The demonstrated findings, specifically those related to failure and risk challenges of DNN converters, including factors (n=12), symptoms (n1=4, n2=3), and root causes (n1=4, n2=3), will enrich the ML-DNNs community and guide them toward desirable model development and deployment improvement, with significant practical implications for intelligent industries.