Combating mental illnesses such as depression and anxiety has become a global concern. As a result of the necessity for finding effective ways to battle these problems, machine learning approaches have been included in healthcare systems for the diagnosis and probable prediction of the treatment outcomes of mental health conditions. With the growing interest in machine and deep learning methods, analysis of existing work to guide future research directions is necessary. In this study, 33 articles on the diagnosis of schizophrenia, depression, anxiety, bipolar disorder, post-traumatic stress disorder (PTSD), anorexia nervosa, and attention deficit hyperactivity disorder (ADHD) were retrieved from various search databases using the preferred reporting items for systematic reviews and meta-analysis (PRISMA) review methodology. These publications were chosen based on their use of machine learning and deep learning technologies, individually assessed, and their recommended methodologies were then classified into the various disorders included in this study. In addition, the difficulties encountered by the researchers are discussed, and a list of some public datasets is provided.

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In the field of stress recognition, the majority of research has conducted experiments on datasets collected from controlled environments with limited stressors. As these datasets cannot represent real-world scenarios, stress identification and analysis are difficult. There is a dire need for reliable, large datasets that are specifically acquired for stress emotion with varying degrees of expression for this task. In this paper, we introduced a dataset for Stress Analysis with Dimensions of Valence and Arousal of Korean Movie in Wild (SADVAW), which includes video clips with diversity in facial expressions from different Korean movies. The SADVAW dataset contains continuous dimensions of valence and arousal. We presented a detailed statistical analysis of the dataset. We also analyzed the correlation between stress and continuous dimensions. Moreover, using the SADVAW dataset, we trained a deep learning-based model for stress recognition.

I. INTRODUCTION Easy to use test bench for analysis of CAN communication is essential for performing experiments without the use of actual vehicles. This study focuses on developing a cost-effective test bench for studying CAN communication using an easily available card-sized minicomputer. In the systems where the specific data sources continuously sense the environment and transmit data to other components of the system. The CAN bus is one of such communication solutions. CAN is a widely-used communication protocol, originally developed for use in the automotive industry. However, its use is not only limited to use in automobiles. It provides high-speed serial communication in various domains such as manufacturing industry, medicine, and agriculture. The CAN protocol is a popular choice for such systems due to its safety, real-time capabilities, and the automatic control of access to the data bus. The CAN is a peer-to-peer network where there is no master node and the individual nodes can send and receive data through a can bus. With the highest speed of 1MB, CAN bus is a message-oriented protocol designed to operate at speeds from 20kb/s to 1Mb/s. CAN has built-in support for error detection and re-transmission of data. This paper is organized as follows: Section II briefly describes the CAN standard and the Raspberry Pi and PiCAN2 modules used in this study for CAN communication. Section III presents the overall design of the proposed test bench for analyzing response time. Section IV summarizes the experiments and observations. Finally, Section V concludes the paper. II. RELATED WORK Different techniques and hardware have been used to study CAN data. Meseguer et al. designed a platform for assisting drivers to ensure safe and economical driving style [1]. They developed an Android application to communicate with the CAN data scanner, ELM327, for accessing speed, acceleration and throttle position