Background: Curcumin has been widely used to treat a variety of diseases and disorders since ancient times, most notably for the purpose of healing wounds. Despite the large number of available reviews on this topic, a bibliometric tool-based meta-analysis is missing in the literature. Scope and approach: To evaluate the influence and significance of the countries, journals, organizations and authors that have contributed the most to this topic, the popular bibliometric markers, including article count, citation count, and Hirsch index (H-index), are taken into account. Their collaborative networks and keyword co-occurrence along with the trend analysis are also sketched out using the VOSviewer software. To the best of our knowledge, this is the first bibliometric review on the topic and hence it is envisaged that it will attract researchers to explore future research dimensions in the related field. Key findings and conclusions: India provided the most articles, making up more than 27.49 percent of the entire corpus. The International Journal of Biological Macromolecules published the most articles (44), and it also received the most citations (2012). The Journal of Ethnopharmacology (28 articles) and Current Pharmaceutical Design (20 articles) were the next most prolific journals with 1231 and 812 citations, respectively. The results indicate a significant increase in both research and publications on the wound-healing properties of curcumin. Recent studies have concentrated on creating novel medicine-delivery systems that use nano-curcumin to boost the effect of the curcumin molecule in therapeutic targeting. It has also been observed that genetic engineering and biotechnology have recently been employed to address the commercial implications of curcumin.

Lung cancer is a high-risk disease that causes mortality worldwide; nevertheless, lung nodules are the main manifestation that can help to diagnose lung cancer at an early stage, lowering the workload of radiologists and boosting the rate of diagnosis. Artificial intelligence-based neural networks are promising technologies for automatically detecting lung nodules employing patient monitoring data acquired from sensor technology through an Internet-of-Things (IoT)-based patient monitoring system. However, the standard neural networks rely on manually acquired features, which reduces the effectiveness of detection. In this paper, we provide a novel IoT-enabled healthcare monitoring platform and an improved grey-wolf optimization (IGWO)-based deep convulution neural network (DCNN) model for lung cancer detection. The Tasmanian Devil Optimization (TDO) algorithm is utilized to select the most pertinent features for diagnosing lung nodules, and the convergence rate of the standard grey wolf optimization (GWO) algorithm is modified, resulting in an improved GWO algorithm. Consequently, an IGWO-based DCNN is trained on the optimal features obtained from the IoT platform, and the findings are saved in the cloud for the doctor’s judgment. The model is built on an Android platform with DCNN-enabled Python libraries, and the findings are evaluated against cutting-edge lung cancer detection models.

This work proposes a robust convergence-based approach that integrates blockchain and edge computing, supporting a proactive and adaptive secure-optimized framework to fortify security measures and mitigate unauthorized access in IoT systems. In the developed framework the blockchain technology enforces proactive security by ensuring data integrity, decentralized authentication, and resilient threat prevention. While the fusion of an Artificial Neural Network (ANN) and Aquila Optimizer (AO), enables the framework to enhance the accuracy of threat detection by fine-tuning parameters and optimizing the network’s architecture. The ANN adeptly captures intricate patterns and features from input data, while the AO intelligently adjusts critical hyperparameters to foster swift convergence and achieve peak network performance. This integration empowers a robust and accurate security detection system, enabling effective identification of complex and multifaceted security threats. Through extensive experimental analyses, we validate the effectiveness of the proposed ANN-AO framework, demonstrating a reduction in latency by 587 ms, 1187 ms, 1079 ms, and 460 ms, while improving security detection by 17.00%, 41.00%, 11.00%, and 8.00%, when compared to cutting-edge security methods. Moreover, the results are further verified and validated through statistical and computational analyses.

IoT-enabled healthcare apps are providing significant value to society by offering cost-effective patient monitoring solutions in IoT-enabled buildings. However, with a large number of users and sensitive personal information readily available in today’s fast-paced, internet, and cloud-based environment, the security of these healthcare systems must be a top priority. The idea of safely storing a patient’s health data in an electronic format raises issues regarding patient data privacy and security. Furthermore, with traditional classifiers, processing large amounts of data is a difficult challenge. Several computational intelligence approaches are useful for effectively categorizing massive quantities of data for this goal. For many of these reasons, a novel healthcare monitoring system that tracks disease processes and forecasts diseases based on the available data obtained from patients in distant communities is proposed in this study. The proposed framework consists of three major stages, namely data collection, secured storage, and disease detection. The data are collected using IoT sensor devices. After that, the homomorphic encryption (HE) model is used for secured data storage. Finally, the disease detection framework is designed with the help of Centered Convolutional Restricted Boltzmann Machines-based whale optimization (CCRBM-WO) algorithm. The experiment is conducted on a Python-based cloud tool. The proposed system outperforms current e-healthcare solutions, according to the findings of the experiments. The accuracy, precision, F1-measure, and recall of our suggested technique are 96.87%, 97.45%, 97.78%, and 98.57%, respectively, according to the proposed method.

Cloud computing provides a ubiquitous data storage and access mechanism for organizations, industries, and smart grids to facilitate their operations. However, the concern in cloud storage systems is to secure data access control toward authentication for sensitive data, such as the electric vehicles (EVs) requesting information for attending a charging service. Consequently, denying an authentic user’s request will result in delaying the requested service, thereby leading to service inefficiency. The role-based access control (RBAC) plays a crucial role in securing and authenticating such time-sensitive data. The design mechanism of roles is based on skills, authority, and responsibilities for organizations. For EVs, the roles are based on the type of membership, such as permanent, occasional, or one-time. In this paper, we propose a new RBAC access control and privacy-preserving information access method toward the coarse-grained measure control and privacy protection in the cloud storage system for EVs. The data can be encrypted and decrypted based on the types of users who possess appropriate access permission toward authorized and unauthorized users according to their roles specified by role-based access control policies. The proposed approach has been simulated with various role-based scenarios, and the efficiency was evaluated against state-of-the-art role-based access-control techniques.

The IoT’s millions of sensor-equipped gadgets upload data to computers for management and use in smart grids, intelligent transportation networks, healthcare networks, and smart cities. Sensor data needs a safe server-sensor connection. Studying altered data could have catastrophic consequences. Internet-of-Things sensors must authenticate with the reader and base station before sending data. IoT sensors can securely and effectively communicate data. Correction evidence in the suggested technique reveals that the required data is created at the receiver using the normal Euclidean parameters of IoT sensors. The proposed approach is compatible with most assaults, making it a good security option. A framework for edge IoT devices can optimize latency, security, and other performance characteristics. Constraints Application Protocol (COAP) and The Stream Control Transmission Protocol (AESCQTTP) combine cooperative resource management, machine learning, context-aware computing, and flexible infrastructure to handle communication and computing difficulties. To reduce end-to-end latency by (7.13–7.35)%, raise security by 98.99%, and increase efficient pocket distribution to 98%. This study examined existing research issues and edge-computing technologies and proposed a novel strategy for optimizing edge-IoT system performance metrics. This arrangement could let future networks communicate securely.