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.

Accurate lung cancer detection is vital for timely diagnosis and treatment. This study evaluates the performance of six convolutional neural network (CNN) architectures, ResNet‐50, VGG‐16, ResNet‐101, VGG‐19, DenseNet‐201, and EfficientNet‐B4, using the LIDC‐IDRI dataset. Models were assessed both in their base forms and with transfer learning. The dataset consisted of 460 × 460 × 3 pixel images categorized into squamous cell carcinoma (SCC), normal benign, large cell carcinoma (LCC), and adenocarcinoma (ADC). Performance metrics were computed, including accuracy (99.47% for the custom CNN), precision (99.50%), recall (98.37%), AUC (99.98%), and F1‐score (98.98%) during training. However, overfitting was observed in the validation phases. Transfer learning models showed better generalization, with DenseNet‐201 achieving a top validation accuracy of 96.88% and EfficientNet‐B4 of 96.53%. Hyperparameter tuning improved the models’ generalization capabilities, maintaining high accuracy while reducing overfitting. This study highlights the effectiveness of transfer learning, particularly DenseNet‐201, in enhancing automated lung cancer detection systems. Future work will focus on expanding datasets and exploring additional augmentation techniques to further refine model performance in clinical settings.

Lung cancer (LC) remains one of the most challenging malignancies to diagnose accurately due to the complexity of its pathology and the limitations of traditional diagnostic protocols. This study presents a novel deep learning–based ensemble technique aimed at enhancing the automatic detection of LC, integrating machine learning (ML) methodologies to improve diagnostic accuracy and reliability significantly. The publicly available Kaggle LC survey dataset, comprising 309 instances with 16 clinical attributes, including age, smoking history, and symptoms were used. The synthetic minority oversampling technique (SMOTE) for data balancing, complemented by fivefold cross‐validation strategy to assess the performance of various ML models, was also employed. This approach utilizes convolutional neural networks (CNNs) integrated with ensemble learning to develop two novel models, ConvXGB and ConvCatBoost , which synergistically combine CNN‐based feature extraction with the powerful classification capabilities of XGBoost and CatBoost , respectively, for binary classification in LC detection. ConvXGB achieved improved results, including an accuracy of 98.26%, precision of 98.72%, recall of 98.72%, and F 1‐score of 98.72%. Such performance metrics underscore ConvXGB’s potential to minimize false positives and negatives, enhancing outcomes in LC diagnostics. Additionally, the complexities surrounding LC diagnosis through a comprehensive comparative analysis were explored, establishing benchmarks for evaluating diagnostic precision, accuracy, recall, and F 1‐score. Our findings highlight the transformative impact of ML in LC research, paving the way for diagnostic frameworks that promise significant advancements in oncological practices.

Mental fatigue is a complex condition arising from various neurological processes and influenced by external factors such as stress and cognitive demands. This comprehensive review elucidates the primary neurological mechanisms underlying mental fatigue, particularly emphasizing how it was elevated or otherwise affected during the COVID-19 pandemic. We explore the intricate relationship between prolonged cognitive tasks, chronic stress, and the development of mental fatigue, emphasizing the impacts that mental fatigue has on mental health across diverse populations. Utilizing advanced artificial intelligence techniques, including machine learning and deep learning, this study identifies and quantifies the patterns of mental fatigue. The innovative approach deployed in this study enhances our understanding of the complex interplay between mental fatigue and psychological disorders, uncovering potential predisposing factors and underlying mechanisms. A thorough bibliometric analysis highlights global research trends, key contributors, and emerging interdisciplinary methods in mental fatigue research. This paper identifies gaps in knowledge and methodological challenges. It proposes promising avenues for future investigations that emphasize multidisciplinary approaches and the development of novel diagnostic and treatment tools tailored to address mental fatigue. By integrating insights from neurological studies with the psychological implications of mental fatigue, this study aims to inform better interventions to improve mental health outcomes. Our findings have significant implications for healthcare professionals, researchers, and policymakers working to mitigate the impact of mental fatigue in various contexts.