Version 1
: Received: 17 September 2023 / Approved: 18 September 2023 / Online: 19 September 2023 (03:53:36 CEST)
Version 2
: Received: 8 November 2023 / Approved: 9 November 2023 / Online: 9 November 2023 (13:37:31 CET)
How to cite:
Yousif, J.H.; Yousif, M.J. Critical Review of Neural Network Generations and Models Design. Preprints2023, 2023091149. https://doi.org/10.20944/preprints202309.1149.v2
Yousif, J.H.; Yousif, M.J. Critical Review of Neural Network Generations and Models Design. Preprints 2023, 2023091149. https://doi.org/10.20944/preprints202309.1149.v2
Yousif, J.H.; Yousif, M.J. Critical Review of Neural Network Generations and Models Design. Preprints2023, 2023091149. https://doi.org/10.20944/preprints202309.1149.v2
APA Style
Yousif, J.H., & Yousif, M.J. (2023). Critical Review of Neural Network Generations and Models Design. Preprints. https://doi.org/10.20944/preprints202309.1149.v2
Chicago/Turabian Style
Yousif, J.H. and Mohammed J. Yousif. 2023 "Critical Review of Neural Network Generations and Models Design" Preprints. https://doi.org/10.20944/preprints202309.1149.v2
Abstract
In recent years, Neural networks are increasingly deployed in various fields to learn complex patterns and make accurate predictions. However, designing an effective neural network model is a challenging task that requires careful consideration of various factors, including architecture, optimization method, and regularization technique. This paper aims to comprehensively overview the state-of-the-art artificial neural network (ANN) generation and highlight key challenges and opportunities in machine learning applications. It provides a critical analysis of current neural network model design methodologies, focusing on the strengths and weaknesses of different approaches. Also, it explores the use of different learning approaches, including convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN) in image recognition, natural language processing, and time series analysis. Besides, it discusses the benefits of choosing the ideal values for the different components of ANN, such as the number of Input/output layers, hidden layers number, activation function type, epochs number, and model type selection, which help improve the model performance and generalization. Furthermore, it identifies some common pitfalls and limitations of existing design methodologies, such as overfitting, lack of interpretability, and computational complexity. Finally, it proposes some directions for future research, such as developing more efficient and interpretable neural network architectures, improving the scalability of training algorithms, and exploring the potential of new paradigms, such as Spiking Neural Networks, quantum neural networks, and neuromorphic computing.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
9 November 2023
Commenter:
Jabar H. Yousif
Commenter's Conflict of Interests:
Author
Comment:
Removed one repeated Table (4). Shifted down the numbers of Tables after removing Table 4. Changed some Figure's titles. Enhanced the format of Tables.
Commenter: Jabar H. Yousif
Commenter's Conflict of Interests: Author
Shifted down the numbers of Tables after removing Table 4.
Changed some Figure's titles.
Enhanced the format of Tables.