Optimization of Neural Network Architectures for Image Recognition

Purpose: To aim of the study was to analyze the optimization of neural network architectures for image recognition. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Recent studies on neural network architectures have revealed significant improvements in image recognition by focusing on optimizing network designs. Key findings highlight the effectiveness of using deeper and more complex architectures, such as convolutional neural networks (CNNs) and residual networks (ResNets), which enhance recognition accuracy by capturing intricate features in images. Tuning hyper parameters, such as learning rates, batch sizes, and activation functions, is crucial for optimizing performance. Techniques like dropout, batch normalization, and data augmentation help reduce overfitting and improve the generalization of models. Unique Contribution to Theory, Practice and Policy: Information bottleneck theory, transfer learning theory & bayesian optimization theory may be used to anchor future studies on optimization of neural network architectures for image recognition. Promote the practical implementation of efficient scaling methods such as Efficient Net across various applications and domains. Advocate for the development of standards and regulations that guide the ethical deployment of AI technologies in image recognition.