# Particle Swarm Optimisation for Emotion Recognition Systems: A Decade Review of the Literature
## 2 Related Works
### Systematic Literature Review  
SLR is a methodical and structured approach to identify, evaluate, and synthesise all relevant literature related to a specific research question or topic [31; 32]. It provides a comprehensive overview of the current state of knowledge on a particular subject, helps identify research gaps, and informs future research directions. SLR is a valuable tool for researchers as it helps them avoid bias and ensures that all relevant studies are considered, leading to more accurate and reliable research outcomes. A previous study that adopted SLR [33; 34; 35] has shown significant results in identifying recent related studies and findings that help the researcher investigate the depth of the chosen field.  
Specifically, this study aims to evaluate the utility of PSO in facilitating ERS development. To gain deeper insights into previous research, the methodology of SLR has been selected to determine the potential benefits of PSO for ERS practitioners. In a prior study conducted by [36], works on PSO were reviewed extensively using the SLR methodology. Specifically, the study utilised SLR focusing on PSO's applicability in medical disease detection. The review explored works highlighting the feasibility of PSO in the healthcare industry and demonstrated its potential for disease classification, including conditions, such as heart disease. Furthermore, additional findings from [36] obtained through the SLR indicate that PSO techniques can be employed for the detection of liver disease, cancer, brain disease, and diabetes.  
Other related works of PSO using SLR from [3] reviewed PSO and its potential applications based on the SLR method. Ref. [3] conducted an SLR on the potential applications of PSO algorithms. The review categorised PSO applications into healthcare, environmental, industrial, commercial, smart city, and general aspects. Technical characteristics of different PSO methods and applications were discussed in terms of accuracy and evaluation environment. The paper also proposed a case study to investigate the effectiveness of different PSO methods and applications. On the other hand, ref. [37] conducted an SLR of over two decades of PSO-related work. The study aimed to establish a general understanding of the trend of PSO and its related study to the applications, algorithms, and results from previous studies. The trend of publications and studies related to PSO for the twenty years was analysed in the paper.  
The previous SLR conducted by [3] will greatly contribute to this study by identifying relevant papers on PSO, including algorithms and related terms. The findings presented by [3] establish a solid foundation for understanding the potential applications of PSO across various industries and highlight its significance. In recent years, there has been increased attention on ERS, with various modalities being explored, including physiological modalities (e.g., EEG and ECG) and psychological modalities (e.g., facial expression and speech recognition) [26]. Previous research has also identified potential applications of ERS in industries, such as healthcare [17], education [19], and entertainment [16; 38]. PSOcan serve as a valuable tool for engineers, scientists, and researchers in developing and understanding the PSO methodology for creating the necessary modalities for emotion recognition. Therefore, employing SLR as the chosen methodology in this study will provide valuable insights into the utilisation of PSO in ERS, particularly over the last decade, and the recent developments.