# Deep Recurrent Neural Network Assisted Stress Detection System for Working Professionals
## 2 Background  
This section summarizes some of the research on various machine learning and deep learning algorithms used to predict the stress levels of working professionals. It also discusses the advantages and disadvantages of each technique in terms of its operational functions and characteristics. A previous study looked at the performance of employees concerning three key aspects, namely, workload, job security, and shift work, with a sample size of 100 participants [15]. Stressors and workplace performance were compared using a variety of statistical approaches, such as T-tests, chi-squared tests, correlations, and regression analyses. These three characteristics were found to be favorably linked and to have a significant impact on the performance of employees. Workload was determined to have the greatest effect on employees' performance out of all of the factors [16]. An improved deep learning approach was proposed to forecast human stress based on the finite element analysis. According to [17], Deep Neural Networks (DNNs) are effective at predicting stress components from given data. Researchers developed a multi-task learning mechanism to predict a person's mood, health, and stress levels. The automatic prediction process was also improved using the Domain Adaptation (DA) technique. The Gaussian Progression (GP) model was used to further investigate the non-linear relationship. According to this paper, the Deep Neural Network (DNN) technique produces accurate prediction results. However, it is constrained by the issue of increasing algorithm complexity, which reduces the system's overall performance. [PERSON] et al. [18] came up with a good way to deal with stress that can be used to measure how stressed female educators are [19]. We looked at the work of different authors who wrote about stress at work and put them into different groups based on certain factors. The authors put all of the sources and literature into four groups: journals; author profiles; research methods; and industries or types of research. After looking over the conceptual and descriptive research on work stress, they found that they could understand the basics and get a handle on it. The research also found that there were more empirical studies than exploratory or longitudinal studies and that most of the articles were written by academics rather than professionals. The US, the UK, and India were the top three countries that contributed to these research papers and other materials. They also found that, even though India has contributed to this research based on both practical and theoretical knowledge about stress and its effects, the scientific findings have not been used much to solve the real problem. To reduce stress, the right management techniques and tools are not being used; they are still only on paper. Research also shows that stress at work affects a person's physical and mental health, as well as how well they do their job and how productive they are at the company. Work stress can also cause diseases, such as coronary heart disease, high blood pressure, depression, anxiety, and nervousness. Ref. [20] wanted to find solutions for the monitoring of real-time stress levels using machine learning approaches. The approaches evaluated in this research included SVMs, LDA, NB, KNN, and Decision Trees (DTs). The training and testing difficulties of all of these strategies were also estimated based on their work stages and computations. These models have three stages: pre-processing, feature extraction, and classification. There were 240 participants in the case research conducted in China, and data were gathered through the use of a questionnaire with 24 questions. An analysis of the university faculty's work stress was conducted using ANOVA and multifactor line regression. The stress levels of the university faculty were found to be highly influenced by the faculty member's professional rank, age, and teaching experience.