We investigated the fusion of the Intelligent Internet of Medical Things (IIoMT) with depression management, aiming to autonomously identify, monitor, and offer accurate advice without direct professional intervention. Addressing pivotal questions regarding IIoMT’s role in depression identification, its correlation with stress and anxiety, the impact of machine learning (ML) and deep learning (DL) on depressive disorders, and the challenges and potential prospects of integrating depression management with IIoMT, this research offers significant contributions. It integrates artificial intelligence (AI) and Internet of Things (IoT) paradigms to expand depression studies, highlighting data science modeling’s practical application for intelligent service delivery in real‐world settings, emphasizing the benefits of data science within IoT. Furthermore, it outlines an IIoMT architecture for gathering, analyzing, and preempting depressive disorders, employing advanced analytics to enhance application intelligence. The study also identifies current challenges, future research trajectories, and potential solutions within this domain, contributing to the scientific understanding and application of IIoMT in depression management. It evaluates 168 closely related articles from various databases, including Web of Science (WoS) and Google Scholar, after the rejection of repeated articles and books. The research shows that there is 48% growth in research articles, mainly focusing on symptoms, detection, and classification. Similarly, most research is being conducted in the United States of America, and the trend is increasing in other countries around the globe. These results suggest the essence of automated detection, monitoring, and suggestions for handling depression.

In the last 10 years, there has been a rise in the number of Arabic texts, which necessitates a more profound understanding of algorithms to efficiently understand and classify Arabic texts in many applications, like sentiment analysis. This paper presents a comprehensive review of recent developments in Arabic text classification (ATC) and Arabic text representation (ATR). We analyze the effectiveness of various models and techniques. Our review finds that while deep learning models, particularly transformer‐based architectures, are increasingly effective for ATC, challenges such as dialectal variations and insufficient labeled datasets remain key obstacles. However, developing suitable representation models and designing classification algorithms is still challenging for researchers, especially in Arabic. A basic introduction to ATC is provided in this survey, including preprocessing, representation, dimensionality reduction (DR), and classification with many evaluation metrics. In addition, the survey includes a qualitative and quantitative study of the ATC’s existing works. Finally, we conclude this work by exploring the limitations of the existing methods. We also mention the open challenges related to ATC, which help researchers identify new directions and challenges for ATC.

The Internet of Things (IoT) has become a transformative force across various sectors, including healthcare, offering new opportunities for automation and enhanced service delivery. The evolving architecture of the IoT presents significant challenges in establishing a comprehensive cyber‐physical framework. This paper reviews recent advancements in IoT‐driven healthcare automation, focussing on integrating technologies such as cloud computing, augmented reality and wearable devices. This work examines the IoT network architectures and platforms that support healthcare applications while addressing critical security and privacy issues, including specific threat models, attack classifications and security prerequisites relevant to the healthcare sector. This study highlights how emerging technologies like distributed intelligence, big data analytics and wearable devices are incorporated into healthcare to improve patient care and streamline medical operations. The findings reveal significant potential for IoT to transform healthcare practices, particularly in‐patient monitoring, and clinical decision‐making. However, security and privacy concerns continue to be a substantial barrier. The paper also explores the implications of global IoT and ehealth strategies and their influence on sustainable economic and community growth. It proposes an innovative cooperative security model to mitigate security risks in IoT‐enabled healthcare systems. Finally, it identifies key unresolved challenges and opportunities for future research in IoT‐based healthcare.