Title:Evolutionary Perspectives on Neural Network Generations: A Critical Examination of Models and Design Strategies
Volume: 3
Author(s): Jabar H. Yousif*Mohammed J. Yousif
Affiliation:
- Faculty of Computing and Information Technology, Sohar University, PO Box 44, Sohar, PCI 311, Oman
Keywords:
Neural network generations, machine learning, convolutional neural networks, deep neural networks, model performance, quantum neural networks.
Abstract: In the last few years, Neural Networks have become more common in different areas
due to their ability to learn intricate patterns and provide precise predictions. Nonetheless, creating
an efficient neural network model is a difficult task that demands careful thought of multiple
factors, such as 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 deep neural networks
(DNN) in image recognition, natural language processing, and time series analysis. In addition,
the text explores the advantages of selecting optimal values for various components of an Artificial
Neural Network (ANN). These components include the number of input/output layers, the
number of hidden layers, the type of activation function used, the number of epochs, and the
model type selection. Setting these components to their ideal values can help enhance the model's
overall 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.
