## 2 Background
### Applications of ANN-Based Approaches  
Artificial Neural Networks (ANNs) have become one of the most widely used techniques for modeling complex systems in a wide range of application domains. The versatility and effectiveness of ANNs make them suitable for solving problems in various fields, such as image processing, speech recognition, natural language processing, control systems, finance, medicine, and many more. In image processing, ANNs have been used for tasks such as image classification, object recognition, and image segmentation. For example, in ([PERSON] et al., 2015) [21], a deep convolutional neural network was proposed for handwritten digit recognition, achieving state-of-the-art performance. In speech recognition, ANNs have been applied to model speech signals and perform tasks such as speaker identification and speech-to-text conversion. For example, ([PERSON] et al., 2012) [24] introduced a deep neural network architecture for large vocabulary continuous speech recognition, achieving significant improvements over traditional techniques. In natural language processing, ANNs have been used for tasks such as text classification, sentiment analysis, and machine translation. For example, ([PERSON] et al., 2018) [25] proposed a deep neural network model for machine translation, demonstrating the effectiveness of the approach. In control systems, ANNs have been applied to model dynamic systems and perform control tasks such as system identification, control design, and optimization. For example, ([PERSON] and [PERSON], 1990) [26] introduced the backpropagation algorithm for training multi-layer feedforward neural networks for system identification and control. In finance, ANNs have been used for tasks such as stock market prediction and credit scoring. For example, ([PERSON], 2010) [27] provides an overview of the use of ANNs in finance and discusses various applications and challenges. In medicine, ANNs have been applied to tasks such as diagnosis, prognosis, and drug discovery. For example, ([PERSON] and [PERSON], 2015) [28] reviewed the applications of ANNs in medical diagnosis and discussed various issues and challenges. The wide range of application domains and the versatility of ANNs make them a valuable tool for solving complex problems and improving our understanding of the world. ANNs have become a popular and powerful tool for solving a wide range of problems in various fields, such as image processing [29; 30; 31], speech recognition, natural language processing, control systems [32; 33], finance, and medicine [34; 35; 36]. The versatility and effectiveness of ANNs make them suitable for modeling complex systems and improving our understanding of the world. Despite their many benefits, ANNs also have certain limitations and disadvantages that must be considered when selecting a machine-learning approach. In this article, we will present a summary of the advantages, disadvantages, and limitations of the ANN-basedapproach as shown in Table 1. One way to do this is through empirical studies, where the ANN models are trained and tested on real-world datasets. The evaluation metrics used in these studies may include accuracy, precision, recall, F1-score, and others, depending on the specific application and problem being addressed. Additionally, computational efficiency can be measured in terms of the amount of time required to train the model and make predictions, as well as the amount of computational resources required. Other factors that can impact the effectiveness and efficiency of the ANN methods include the choice of architecture, the quality and size of the training dataset, the choice of hyperparameters, and the choice of optimization algorithms. [PERSON] [27] provides an overview of the use of ANNs in finance and discusses various evaluation metrics and factors that impact performance. [PERSON] and [PERSON] [28] reviewed the applications of ANNs in medical diagnosis and discussed various issues and challenges in evaluating the performance of ANN models.