Users can interact with one another through social networks (SNs) by exchanging information, delivering comments, finding new information, and engaging in discussions that result in the production of vast volumes of data daily. These data, available in various forms, such as images, text, and videos, may be interpreted to reflect the user’s activities, including their mental state regarding depression. For example, depression is a chronic disease from which the vast majority of users suffer, and it has emerged as a significant issue relating to mental health on a global scale. However, because these data are scant, unfinished, and sometimes given inaccurately, it is challenging to make an accurate automated diagnosis from them. Even though several procedures have been utilized over the past few decades to diagnose depression, machine learning (ML) and deep learning (DL) techniques supply superior insights. Thus, in this study, we review several state-of-the-art ML and DL techniques in terms of the systematic literature review (SLR) approach for depression detection. We also highlight some critical challenges from the existing literature that may help to explore for future study. Finally, we believe this survey will help readers and researchers in ML and DL to understand critical solutions in diagnosing depression.

Within the ever-growing healthcare industry, dental informatics is a burgeoning field of study. One of the major obstacles to the health care system’s transformation is obtaining knowledge and insightful data from complex, high-dimensional, and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, heterogeneous, poorly documented, and generally unstructured electronic health records, imaging, sensor data, and text. There were still certain restrictions even after many current techniques were used to extract more robust and useful elements from the data for analysis. New effective paradigms for building end-to-end learning models from complex data are provided by the most recent deep learning technology breakthroughs. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for dental informatics problems and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to some drawbacks and the need for better technique development and provide new perspectives about this exciting new development in the field.

Communication, neuro-prosthetics, and environmental control are just a few applications for disabled persons who use robots and manipulators that use brain-computer interface (BCI) systems. The brain’s motor imagery (MI) signal is an essential input for a brain-related task in BCI applications. Due to their noninvasive, portability, and cost-effectiveness, electroencephalography (EEG) signals are the most widely used input in BCI systems. The EEG data are often collected from more than 100 different locations in the brain; channel selection techniques are critical for selecting the optimum channels for a given application. However, when analyzing EEG data, the principal purpose of channel selection is to reduce computational complexity, improve classification accuracy by avoiding overfitting, and reduce setup time. Several channel selection assessment algorithms, both with and without classification-based methods, extracted appropriate channel subsets using defined criteria. Therefore, based on the exhaustive analysis of the EEG channel selection, this manuscript analyses several existing studies to reduce the number of noisy channels and improve system performance. We review several existing works to find the most promising MI-based EEG channel selection algorithms and associated classification methodologies on various datasets. Moreover, we focus on channel selection methods that choose fewer channels with great precision. Finally, our main finding is that a smaller channel set, typically 10–30% of total channels, provided excellent performance compared to other existing studies.