# Novel Cuckoo Search-Based Metaheuristic Approach for Deep Learning Prediction of Depression
## 2 Literature Review
For example, in smart healthcare, a deep learning-based predictive evaluation system has been proposed to enable interactive multimedia-enabled smart healthcare [PERSON] et al. (2018). In the field of education, a computer-aided instruction system has been developed for college music majors that uses convolutional neural networks [PERSON] et al. (2018). Another study proposed a content-aware fusion method for RGB-D saliency prediction [PERSON] et al. (2018). In 3-D point cloud classification, a dual-graph attention convolution network has been proposed [PERSON] et al. (2018). Additionally, a traffic burst-sensitive model has been developed for short-term prediction under special events [PERSON] et al. (2018). In the field of robotics, various deep learning models gain attention for solving different real life problem [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018). The studies highlight the potential benefit of using machine learning and deep learning to identify subgroups of patients who respond differently to different types of therapy, which could inform treatment decisions. However, it is crucial to highlight that the study has several limitations, such as the sample size and the retrospective methodology, and that other studies with bigger sample sizes and various demographics are needed to corroborate the findings. Various studies emphasize the hybrid method for utilizing deep learning to identify patient subgroups that respond differently to different forms of therapy, which might improve treatment decisions [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018). The architecture of deep learning, the optimization methods used, and the calibration of hyper parameters to identify various patterns in data are significantly responsible for its efficiency. Finding an optimization technique that can considerably enhance deep learning results is the goal of this research. The propensity to become stuck in local minima and poor convergence rates are the main difficulties that learning algorithms encounter [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018). The majority of traditional optimization methods are deterministic and referred to as \"gradient-based approaches\" since they utilize gradient information. Stochastic optimization algorithms can be categorized into heuristic and metaheuristic approaches, which are usually effective but not always reliable. To move from local search to global search, randomization is a useful strategy, and therefore, most metaheuristic algorithms aim to achieve global optimization by creating various solutions on a global scale through exploration and by focusing on limited search areas through exploitation. Each nature-inspired metaheuristic algorithm combines exploration and exploitation with its own characteristics to find the best solutions, and some of them have obtained solutions close to optimal. Choosing the best solution guarantees optimal convergence, while randomization through exploitation prevents the solutions from being trapped in local optima and increases solution diversity. A good balance between these two key components typically ensures global optimality [41; 42]. Some popular examples of nature-inspired algorithms include GA, ABC, PSO, and CS algorithms, with the cuckoo search algorithm being a recent popular swarm intelligence algorithm inspired by the egg-laying behavior of cuckoo birds [43; 44]. In this paper, we overcome the shortcoming of CS by introducing a PS algorithm in the exploration phase of the CS approach. After that, a new optimization algorithm named PSCS is applied with deep learning to enhance performance of deep learning for depression detection. The training model utilizes sentiment analysis to distinguish between positive expressions of depression and other psychiatric disorders as a means of detecting depression.