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.

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.

Background: Lumpy skin disease (LSD) has been a significant concern in veterinary medicine since its discovery. Despite decades of research, understanding the full spectrum of this disease remains a challenge. To address this gap, a comprehensive analysis of the existing body of knowledge on LSD is essential. Bibliometric analysis offers a systematic approach towards the mapping of research landscape, identifying key contributors, and uncovering emerging trends in LSD research. Objective: This study aims to conduct a thorough bibliometric analysis spanning from 1947 till the present date in order to map the knowledge domain of LSD. The objective is to gain insights into the global research trends, identify influential contributors, explore collaboration networks, and predict future outlook in LSD research. Method: Data extracted from the Scopus database was used to perform a bibliometric analysis. 341 relevant documents were selected for analysis. Bibliometric indicators, including publication numbers, citation counts, and the h-index, were utilized to assess the comprehensive contributions of nations, organizations, authors, and source titles. Additionally, cooperation networks between countries, organizations, and authors were visualized using the VOSviewer tool. Results: The analysis revealed a significant increase in research output on LSD, with a notable growth rate of 19.26%. Since its discovery in Zambia in 1929, LSD research has grown steadily, with an average annual growth rate of 5.21%. The University of Pretoria and the Federal Centre for Animal Health emerged as the most active institutions and organizations in LSD research. The Journal of Virology was identified as the most cited journal, reflecting its significant impact on the field, and a strong international collaboration was observed between the United Kingdom and South Africa. Conclusion: This study provides valuable insights into the global research landscape of LSD, highlighting key trends, contributors, and collaboration networks. By reviewing decades of research, this analysis enhances our understanding of LSD and serves as a foundation for future research endeavors. The findings of this study will aid researchers in navigating the vast literature on LSD, ultimately contributing to advancements in veterinary medicine and disease management strategies.

Wireless power transfer has become a trending research area for remotely transferring power. This paper presents the numerical simulation study of selective wireless power transfer to closely spaced wireless sensors embedded in reinforced concrete. A selective microwave wireless power transfer is achieved at a 10 mm separation between tightly-coupled monopole antennas (wireless sensor antennas). Both tightly-coupled wireless sensors operate at 2.45 GHz, hence beating the diffraction limit at λ/12 with the incorporation of additional scatterers in the reinforced concrete environment. The main objective is to realize selective wireless power transfer to wireless sensors with subwavelength separation (closely spaced) to which one makes the power request. Here, the presence of meta-structures creates some randomness serving as scatterers in the use of the electromagnetic time-reversal technique which enhances the spatial refocusing beyond the diffraction limit. This implies that the focal spot is less than half of the carrier wavelength at the operating frequency. At any time that one of the tightly-coupled sensor antennas sends a power request, power will be transferred to it alone. Cases of dry concrete with and without reinforced bars have been studied with electromagnetic time-reversal techniques for the closely spaced sensors embedded in concrete.