## 2 Methodology
### Artificial Neural-Network Model (ANN)  
An ANN is a computational model that is entirely based on the shape and capabilities of biological neural networks. ANNs are non-linear statistical information modeling tools that are used to model complex relationships between inputs and outputs or to determine patterns. ANNs are made up of layers of interconnected nodes, or neurons, where each neuron processes information and exchanges messages with neurons in adjacent layers [46]. The three layers of neurons are the input layer, the hidden layers, and the output layer. Data are received by the input layer and is then passed to the hidden layers. In the hidden layers, the supplied data are transformed through a number of processes so that relevant features may be extracted [46]. The output layer uses the information that has been processed to create the final output. The strength of a connection between neurons is determined by the weight assigned to each connection [47]. Prior to the activation function being applied, biases are added to the weighted sum. Activation functions enable the model to learn complex patterns. They introduce non-linearity into the model and common examples are ReLU (rectified linear unit), tanh, sigmoid, and softmax (for multi-class classification). A total of 70% of the data is used for the training set, while 30% is used as a testing set [48]. Training was repeated for 80% and 90% of the data. The neural-network data-processing algorithm involves the different steps to prepare the data for training and prediction. One is data cleaning, where inconsistent data, outliers, and missing values are dealt with. Features are normalized to ensure that the scale of all input variables are similar. Another step is feature selection, where the most predictive features for the target variable are selected. Next is data splitting, where the dataset is split into training, validation, and test sets. This aids in assessing how well the model performs with unseen data. One other important step is data encoding. At this stage, input data are transformed into suitable format for neural-network training by scaling or standardization. Categorical variables are converted to numerical representations using one-hot encoding or label encoding. To ensure effective model training, there is a need to organize the data into batches the data. The order of the batches is randomized during training so as to lower the chance of overfitting. Data augmentation is applied to image or text data to artificially increase the diversity of the training data. Also, a data pipeline can be constructed for loading and preprocessing data during model training and evaluation. Convolutional neural networks (CNNs): These networks are frequently employed in image processing, which includes normalization, augmentation, and resizing of images. Recurrent neural networks (RNNs): When used with sequential data, such as text or time series, these require temporal alignment for time series data and tokenization, embedding, and padding for text data. Feed-forward neural network (FNNs): This is a classification algorithm inspired by biology. It is made up of a number of simple neuron-like processing units that are arranged in layers. Every unit in a layer is related to every unit in the layer before it. It is the most basic type of neural network. Feature scaling, categorical variable encoding, and data partitioning into training, validation, and test sets are common preprocessing steps \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attributes** & **Network A** & **Network B** & **Network C** & **Network D** \\ \hline Call rate & 99c & 79c & 79c & 99c \\ \hline Data speed & 21.63 & 22.22 & 22.22 & 21.63 \\ \hline Customer service & Not very good & Very good & Very good & Not very good \\ \hline Premiums/incentives & Low & Low & High & High \\ \hline Network coverage & Vast & Not vast & Vast & Not vast \\ \hline I would choose & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A sample choice set presented to participants.