# Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression
## 1 Introduction
The logistic regression model effectively showcases the strength of the chosen features, leading to improved clustering compared to other traditional features. Moreover, because the model is based on possible depressive symptoms, it may provide a thorough justification for the treatment using machine learning techniques [4]. Machine learning is among the most widely used approaches for assessing a person's physiological state from various data sources. As machine learning models are increasingly used to make critical day-to-day predictions in critical contexts, various stakeholders in the AI industry are demanding greater transparency in these contexts. Selection and implementation of irrational AI decisions that do not account for the model's actions pose a high threat in this regard. As a result, the description of the model output is critical. Deep neural networks have recently made significant contributions to many research fields, particularly pattern recognition and artificial intelligence. It is more powerful than traditional neural networks, but has two major drawbacks. The first drawback is that in most cases, overfitting occurs. The latter takes a lot of time to model the underlying data. Convolutional neural networks (CNNs) are highly effective for classifying biological images, and features obtained from CNNs are often superior to those created by hand. Research has shown that using a combination of handcrafted and learned features can enhance the performance of CNNs in classifying biometric images. In the field of neural signal classification, a number of studies have employed various techniques to transform EEG signals into image representations, which have yielded better results compared to other methods. In addition to training with data, CNNs extract functions [5]. Recurrent neural networks (RNNs) are not typically used for analyzing time series data; however, they consist of 722 neural computations and applications, making them a more suitable choice than CNNs for this purpose. This is because RNNs are better suited for sequential data and pattern analysis. Long short-term memory (LSTM) is a form of recurrent neural network (RNN) that was developed to address the gradient decay problem that occurs while processing multivariate and time series data with long-term dependencies. The goal of this research is to employ LSTM-based RNNs to represent diverse emotional states in textual data. Machine learning is a common method for obtaining data about people's physical and mental states from a range of sources. Since that time, machine learning models are being used more frequently. Transparency becomes less important in such circumstances. Various AI industry participants make and implement AI decisions in this regard. It cannot be justified, and no explanation exists for how the model behaves. As a result, a description of the model's output is required. Precision medicine practitioners, for example, require more data from machine learning models [6]. In the study conducted by [PERSON] et al. [7], the issue of understanding global sentiment towards COVID-19 is addressed by using sentiment analysis and deep learning techniques to analyze cross-cultural polarity and emotion in COVID-19-related tweets. The researchers used a dataset of over 1 million tweets written in multiple languages and proposed a multi-layer LSTM assessment model for classifying both sentiment polarity and emotions expressed in the tweets. They found significant differences in sentiment polarity and emotion expressed in tweets across different cultures, with their proposed model achieving state-of-the-art accuracy on the Sentiment140 dataset for sentiment polarity classification. The findings provide valuable insights into how different cultures are reacting to the COVID-19 pandemic on social media, which can inform policy decisions and public health interventions.