## 2 Background  
### Artificial Neural Network  
The basic theory of Artificial Neural Networks (ANNs) is based on the idea that they can simulate the way the human brain works. They consist of a large number of interconnected artificial neurons, which are simple processing units that can take inputs, perform computations on them, and produce an output. The connections between these neurons are represented by weights, which can be adjusted during the training process to learn a specific task [13; 14]. The field of Artificial Neural Networks (ANNs) is a rapidly evolving field, and new architectures, algorithms, and applications are being developed all the time. Here are some current state-of-the-art ANN architectures and major research directions. The field of Artificial Neural Networks (ANNs) has seen significant advancements in recent years, leading to the development of new architectures and algorithms that have achieved state-of-the-art performance on a wide range of tasks. One of the main directions in ANN research is the use of convolutional neural networks (CNNs) for image and video data analysis. CNNs have been shown to achieve state-of-the-art results on tasks such as image classification, object detection, and semantic segmentation [13]. Another major direction is the use of Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) for stock price forecasting [15], sequential data analysis, such as speech and text, with notable applications in natural language processing tasks, such as machine translation, speech recognition, and language modeling [16]. Generative Adversarial Networks (GANs) are a direction of research that focuses on the generation of new data, such as images, videos, and speech, and they have been widely applied in tasks such as image and video synthesis [17]. Another important direction is the field of Transfer Learning, in which the knowledge acquired from a pre-trained model is used to fine-tune it and apply it to new domains with a limited amount of data. Additionally, recent attention is paid to the field of Explainable AI, which tries to understand and make transparent the decision-making process of the ANNs, especially in critical tasks such as healthcare, finance, and self-driving cars. Finally, adversarial training is an approach that improves the robustness of ANN by training it to resist adversarial examples, which are inputs that are carefully crafted to fool the network. One of the main research methods used in the field is mathematical modeling [18; 19; 20], which involves developing and analyzing mathematical models of ANNs to understand their properties and behaviors. Researchers use techniques from optimization, control theory, and information theory to study the properties of ANNs and develop new algorithms for training and inference [21]. Big Data provides a wealth of opportunities for industries, such as e-commerce, industrial control, and medical informatics, but it also poses significant challenges for data mining and information processing due to its large volume, variety, velocity, and veracity. To address these challenges, researchers have looked to findings in neuroscience for inspiration. This has led to the development of Artificial Neural Networks (ANNs), which mimic the structure and function of the human brain and allow for the transmission of information across complex networks [1; 2; 3; 4; 22]. ANNs have the ability to learn and adapt through continual signal stimulation, making them well-suited for processing and extracting insights from large and complex datasets. This cerebral cortex mechanism for deep learning aided in the development of deep learning [1]. The connections between the neurons in a Recurrent Neural Network (RNN) create a directed loop, allowing the model to demonstrate dynamic temporal activity. Natural language processing, voice recognition, and other applications benefit from Recurrent Neural Networks because of their capacity to employ their internal memory to interpret arbitrary input sequences [23].