## 3 Proposed Method
### Review Rating Prediction
#### 3.5.2 Training Process of HAN Using Developed Jaya-TDO Approach  
The training procedure of HAN was completed by Jaya-TDO technique, which was newly developed by combining Jaya optimization [28] and TDO approach [29]. TDO model is bio-inspired metaheuristic model, and it is designed based on the characteristics of Tasmanian devil. This model efficiently resolves real word issues, even though it did not resolve optimization issues; thus, Jaya optimization was united with TDO. The algorithmic procedure of Jaya-TDO technique is defined below. _(a) Initialization:_ The initial population of Tasmanian devils is randomly produced depending on problem constraints. Here, population members of TDO recommended candidate rates for problem variables according to their search space location. Thus, every fellow of the population is a vector, along with amount of elements similar to the quantity of problem variables mathematically. The TDO members are expressed by the following matrix:  
\[D=\left[ \begin{array}{c}D_{1}\\ \vdots\\ D_{t}\\ \vdots\\ D_{A}\end{array} \right]_{A\times p}=\left[ \begin{array}{ccccc}d_{1,1}& \cdots&d_{1,y}&\cdots&d_{1,v}\\ \vdots&\ddots&\vdots&\vdots&\vdots\\ d_{j,1}&\cdots&d_{j,y}&\cdots&d_{j,v}\\ \vdots&\ddots&\vdots&\ddots&\vdots\\ d_{A,1}&\cdots&d_{A,y}&\cdots&d_{A,v}\end{array} \right]_{A\times p} \tag{42}\]  
where \(D\) represents the population of Tasmanian devils, \(D_{i}\) implies \(i\)th candidate solution, \(d_{j,y}\) refers to candidate rate for \(y\)th variable, \(A\) indicates number of penetrating Tasmanian devils and \(v\) specifies quantity of variables for definite problems. _(b) Fitness computation:_ Afterwards, the fitness rate was calculated for every solution, in which, the fitness with lower value was specified as optimal solution and the fitness rate was evaluated by  
\[\rho_{h}=\frac{1}{m}\sum_{h=1}^{m}(R_{t}^{*}-R_{t})^{2} \tag{43}\]  
where \(R_{t}^{*}\) indicates target output, \(m\) denotes whole samples and \(R_{t}\) signifies classified output from HAN. _(c) Exploration phase:_ The Tasmanian devil needs to feed on flesh rather than the hunting process. The behavior of the Tasmanian devil in the territory-scanning portion when identifying the meat is similar to the algorithm search process in solving space. Tasmanian devil identifies TDO exploration power, scanning different portions of search space to find real optimum portion. In search space, the position of other population members is considered as a carrion location for Tasmanian devil. Here, accidental selection of these situations was simulated by following equation in which \(u\)th population member is selected as target carrion for \(p\)th Tasmanian devil. Therefore, \(u\) must be selected randomly from \(1\) to \(T\), whereas the reverse is \(p\). \[B_{u}=L_{p};u=1,2,\ldots,T;p\in\{1,2,\ldots,T\mid p\
eq u\} \tag{44}\]  
where \(B_{u}\) indicates selected carrion through \(p\)th Tasmanian devil. The new position was estimated for Tasmanian devil in search space based on selected carrion. The Tasmanian devil shifts to carrion when objective function is improved; otherwise, it transfers away from carrion. Additionally, Tasmanian devil motion technique is expressed by  
\[d_{s,y}^{\text{new, }R1}=\left\{ \begin{array}{l}d_{s,y}+z\cdot\left(v_{s,y}-C \cdot d_{s,y}\right);O_{B_{u}}<O_{u}\\ d_{s,y}+z\cdot\left(d_{s,y}-v_{s,y}\right);\text{ Otherwise}\end{array} \right. \tag{45}\]  
The position is recognized if objective function rate is enhanced in new location; otherwise, preceding position is chosen, which is illustrated as  
\[L_{u}=\left\{ \begin{array}{l}L_{u}^{new,R1};O_{u}^{new,R1}<O_{u}\\ L_{u};\text{ otherwise}\end{array} \right.