## 5 Proposed Framework  
In this section, the proposed machine learning model will be developed using the workflow described in Figure 4: proposed research methodology. The work will start with the problem definition stage, which is studying the existing Metocean data system as well as the business needs/objectives. Each attribute of the data will be identified, and the Metocean data for the solution is prepared accordingly. The next stage will be gathering and preparation of data. This includes analysing and preparing for structuring and distributing of the data. The data will be organized and well prepared to be ready for the next level. Data conversion and aggregation will be conducted in this phase. The first training data set will be utilized in this phase. By using a Gaussian-means algorithm, the Metocean data will be classified into different distinct areas, while other data will then be chosen randomly, using Equation (5). Fundamentally, kernel density estimation (KDE) will be represented as \(K\), with the \(\sigma\) as bandwidth. Thus, \(x\) as the weighing of the observation from a particular point can be expressed as:  
\[p(x)=\frac{1}{N}\sum_{n=1}^{n}\left(\left|\left|\frac{x-x_{n}}{\sigma}\right| \right|^{2}\right),\ k(t)\propto e^{-t/2} \tag{5}\]  
In the architectural integration and model development stage, algorithm development and evaluation will be conducted. This consists of exploring and selecting the algorithm and reporting the interpretation of the results. Testing of the dataset occur, after which the training dataset will be run again, plus subsequent tests. Python programming language and Hitachi Infrastructure Analytics Advisor will be used for application development. To fully deploy a scalable system, cloud infrastructure is suggested to be developed in this work, as the data will be collected from sensors in real time and non-real time. Therefore, Software as a Service (SaaS) will act as a leveraging platform, such that we will exploit and launch a better Metocean data system with AI Solutions. The proof of concept includes the Metocean parameters (input and output) and component integration, while the decision tree is the data-driven technique. Thus, a new predictive model will then be developed. Conversely, training the data model in this work will rely on an unsupervised machine learning algorithm. The expectation-maximization (EM) algorithm will provide the fundamental concepts in graphical models and inference algorithms on graphs. It will simplify the iteration of parameters by optimizing the lower bound function. Estimating the ML parameters in this model can be one of the anticipated contributions of this research. Figure 4: Proposed research methodology. Therefore, for a model with latent/hidden variables \(x\) for the data points and \(y\) as the observed variables, the lower bound can be considered as follows:  
\[L(\theta)=\log p(y|\theta)=\log\int p(x,\ y|\theta)dx \tag{6}\]  
\[=\log\int q(x)\frac{p(x,y|\theta)}{q(x)}dx \tag{7}\]  
\[\geq\int q(x)\log\frac{p(x,y|\theta)}{q(x)}dx\stackrel{{ def}}{{=}}F(q,\theta) \tag{8}\]  
where \(q(x)\) is an arbitrary density function, while the lower bound \(F\) is the function of both the density \(q(x)\) and the model parameters. As most of the Metocean data are the time series data generated from offshore sensors, these data are unstructured. This research will model the time series data using the state-space model (SSM). Equation (9) describes the observation factors and hidden state's joint probability. The sequence of observed data \(y_{1}\), \(y_{2}\), \(y_{3}\),... can be generated from a sequence of hidden state variables \(x_{1}\), \(x_{2}\), \(x_{3}\).... and \(X_{1}\), \(X_{2}\),.... and \(X_{T}\) is represented by \(X_{1-T}\):  
\[p(x_{1:T}y_{1:T}|\theta)=\prod_{t=1}^{T}p(x_{t}|x_{t-1}\theta)p(y_{t}|x_{t}\theta) \tag{9}\]  
During the stage of experimentation, a test harness will be employed, where test model, test data, and evaluation of the new ML algorithm will begin. The performance will also be determined in this phase.