# Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression
## 3 Methods Used
### Proposed Method
Specifically, we employed NLTK's word tokenization, which involves splitting text into individual words, as well as stemming, which involves reducing words to their base form. These techniques helped us extract meaningful information from the raw data and convert them into a structured format that could be easily analyzed. #### 4.2.2 Data Cleaning

We carried out a data cleaning procedure, which entailed eliminating punctuation and special characters from the text to prepare the dataset for modeling. Some characters contain symbols such as (!,/?) could make it difficult to represent the data accurately and may not add to the context of the text. In order to increase the precision of our model, we cleaned and normalized the data. \begin{table}
\begin{tabular}{l c} \hline \hline
**Specifications** & **Parameters** \\ \hline Processor & Intel(R) Core TM i9- 12900 k (5.20 GHz) \\ \hline Random Access Memory (RAM) & 64 GB \\ \hline Graphics Processing Unit (GPU) & Nvidia RTX Quadro A5000 \\ \hline IDE & VSCode (Python) \\ \hline Operating System & Ubuntu 20.04.5 LTS (Windows WSL) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameter setting. #### 4.2.3 Stemming

Stemming is a method for obtaining a word's basic form by eliminating its affixes. Having the ability to retain only the stems rather than the whole words allows us to decrease the size of the index and dataset while simultaneously improving retrieval precision. We are able to keep the meaning and usage of the stems while removing extraneous information from the words by using stemming methods. We are able to better evaluate and comprehend the data thanks to this procedure. The example is given below. Working = Work + ing (Stemmed word is work)

Crying = Cry + ing (Stemmed word is cry)

Depression = Depress + ion (Stemmed word is depress)

We eliminate affixes from the data using the Porter stemmer stemming method. Using this method, affixes are systematically removed to reveal the word's base form. The input array held the affixes in the same sequential sequence as the initial input data. We were able to better process and evaluate the data thanks to this procedure. #### 4.2.4 Embedding Normalization

Word embeddings are a method for representing textual characters numerically in the form of vectors. The many facets of the word in relation to the whole text are intended to be captured by these vectors. GloVe embedding, an unsupervised learning approach that creates word embeddings by combining global word co-occurrence matrices from a given corpus of data, was used for our textual data. The associations between words are mostly derived by the GloVe algorithm using statistics. It makes use of a regression model that is trained on a huge database of word co-occurrence counts in order to determine the statistical significance of words and the degree of relationship between two words. By using this method, we can more easily express words as numbers and analyze them. The Figure 1 depicts the preprocessing of the dataset along with the method of normalization and how the dataset was converted into a numeric word-based dataset with the images which were the input for the proposed methodology. ### Proposed Deep Learning Architecture

A convolutional neural network (CNN)-based deep learning architecture with multiple layers designed for image classification as shown in Table 2 as well as Figure 3 depicts a clear visualization for the same. The input is a grayscale image of size 32 \(\times\) 32 \(\times\) 1, where the last dimension corresponds to the color channel. The network consists of two convolutional layers with 128 and 48 filters, respectively, each followed by a 2 \(\times\) 2 average pooling layer to reduce the size of the feature maps. The convolutional layers utilize the rectified linear unit (ReLU) activation function. After the second pooling layer, the output feature maps are flattened into a one-dimensional vector of length 256.