## 3 Proposed Methodology
### Classification of MANN-AM (Modified Artificial Neural Network with Attention Mechanism)  
ANN (Artificial Neural Network) is a term that is used to describe a biologically stimulated sub-area of AI (Artificial Intelligence) and is modelled after a succeeding brain. Usually, ANN is a computational network relying on biological networks that build the structure of the human brain. Identical to the human brain, it possesses neurons interconnected with one another. ANNs also comprise neurons that are associated with one another in several network layers. Such neurons are termed nodes. The main merits of utilizing ANN involve its parallel processing ability, storing data within the network and the ability to function with partial knowledge. It also possesses memory distribution. It is also fault tolerant. Concurrently, an attention mechanism exists with the idea of permitting a decoder to use relevant input sequences flexibly through weighted integration of all encoded inputs, with related vectors being credited with maximum weights. In ANNs, attention is meant for imitating cognitive attention. The effect improvises certain input data parts while alleviating other parts. The motivation for using MANN-AM prevails in the fact that the network has to devote a high focus to less, but significant, data parts. The overall model of MANN-AM is shown in Figure 2, wherein the model performs layer-wise learning. The proposed model is designed for predicting the absence or existence of UTI. This is undertaken by normalization (Norm), Euclidean distance (d), Nearest Neighbour (\(\mathrm{u_{i}(r)}\)) and Sigmoid activation-function (\(\mathrm{sigm(x)}\)). (i) _Normalization_  
Normalization ensures that both negative and positive values considered as input for the subsequent layer exist. This makes learning flexible, and the network considers all the input features to a certain extent while learning. It is given by Equation (7):  
\[\mathrm{Norm}=\mathrm{e}-\frac{\mathrm{E_{min}}}{\mathrm{E_{max}}}-\mathrm{E_{ min}} \tag{7}\]  
In Equation (7), \(\mathrm{e}\) indicates the data, \(\mathrm{E_{min}}\) represents the minimum values in each of the columns and \(\mathrm{E_{max}}\) represents the maximum values in each of the columns. (ii) _Euclidean distance_  
Figure 2: Modified Artificial Neural Network with Attention Mechanism Model. The Euclidean distance computes the distance amongst the vectors with two real values. It is given by Equation (8):  
\[\mathrm{d}=\sqrt{\sum_{i=1}^{v}(P_{1i}-P_{2i})^{2}} \tag{8}\]  
In Equation (8), \(\mathrm{d}\) represents the distance, \(v\) indicates the length and \(P_{1i}\) and \(P_{2i}\) denote the Euclidean vectors. * _Nearest-Neighbour_ The Nearest Neighbour is given by Equation (9): \[u_{i}(r)=\frac{\sum_{j=1}^{K}u_{ij}(1/||\left|d(r,r_{j})^{2}\right|)||}{\sum_{ j=1}^{K}\left(1/||\left|d(r,r_{j})^{2}\right)\right||}\] (9)  
In Equation (9), \(u_{i}\) indicates the membership-values in the \(i\)th class, \(r_{j}\) represents the neighbour residue of (r), \(r\) indicates the present residue, which is allocated to the membership values, \(K\) denotes the Nearest Neighbour and \(\mathrm{d}\) represents the distance amongst residues. Rather than considering all high and low weights, the above equation is utilized for picking only the least Nearest Neighbour that relies on the K value, which affords outcomes effectively in comparison to a conventional classifier. * _Sigmoid-Activation Function_ The sigmoid-activation function is given by Equation (10): \[\mathrm{sigm}(x)=\frac{1}{1(1+e^{-x})}\] (10)  
In Equation (10), \(e^{-x}\) indicates the exponential function that is utilized for computing the activation function comprising a small real value ranging from \(-1\) to \(1\). The algorithm corresponding to MANN-AM is shown in Algorithm 2.