The increasing development of gadgets to evaluate stress, wellness, and anxiety has garnered significant attention in recent years. These technological advancements aim to expedite the identification and subsequent treatment of these prevalent conditions. This study endeavors to critically examine the latest smart gadgets and portable techniques utilized for diagnosing depression, stress, and emotional trauma while also exploring the underlying biochemical processes associated with their identification. Integrating various detectors within smartphones and smart bands enables continuous monitoring and recording of user activities. Given their widespread use, smartphones, smartwatches, and smart wristbands have become indispensable in our daily lives, prompting the exploration of their potential in stress detection and prevention. When individuals experience stress, their nervous system responds by releasing stress hormones, which can be easily identified and quantified by smartphones and smart bands. The study in this paper focused on the examination of anxiety and stress and consistently employed “heart rate variability” (HRV) characteristics for diagnostic purposes, with superior outcomes observed when HRV was combined with “electroencephalogram” (EEG) analysis. Recent research indicates that electrodermal activity (EDA) demonstrates remarkable precision in identifying anxiety. Comparisons with HRV, EDA, and breathing rate reveal that the mean heart rate employed by several commercial wearable products is less accurate in identifying anxiety and stress. This comprehensive review article provides an evidence-based evaluation of intelligent gadgets and wearable sensors, highlighting their potential to accurately assess stress, wellness, and anxiety. It also identifies areas for further research and development.

Social media platforms such as Telegram, Twitter, WhatsApp, and Facebook have grown indispensable for information exchange and delivery in recent years. Humans have a tendency to trust any piece of content that circulates on social media without questioning its authenticity or origin. Fascinated people sometimes spread fake news on social media because of its convenience for information sharing. Additionally, it is used for personal advantages, such as amending government policies and defaming eminent personalities. Hence, to avoid the fatal outcome of spreading fake news, different methods are developed for its identification that achieves high accuracy. This paper reviews conventional artificial news detection methods to solve the mentioned issue. The Systematic Literature Review, often known as SLR, is a procedure that assesses review papers connected to detecting fake news. A technique known as unsupervised learning was used for this article review, along with ensemble learning, semi-supervised learning, reinforcement learning, and supervised learning. This methodology's effectiveness is evaluated regarding the precision of the measurements taken. In conclusion, the deep learning model generates practical outputs and outperforms classical machine learning models.