Reyazur Rashid Irshad

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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.

The proliferation of sensing technology, coupled with the integration of cloud infrastructure in Internet of Medical Things (IoMT)-based healthcare frameworks, revolutionizes patient healthcare by enabling remote monitoring and leveraging the scalability, flexibility, and computational power of the cloud for efficient data processing, analysis, and decision-making. This transformative approach enhances patient care quality, mitigates risks, reduces waiting times and costs, and avoids the need for frequent visits to doctors. Albeit this development empowers seamless data processing across a sophisticated network of interconnected devices and servers, while concurrently posing noteworthy security risks that magnify the potential for vulnerabilities and breaches throughout the entire data processing pipeline. Conventional cryptographic algorithms, based on computational complexity, struggle to sufficiently secure sensitive healthcare data against the rapid advancements in computing power and the potential breakthroughs in algorithmic attacks. This underscores the necessity for powerful cryptographic algorithms that can effectively defend against advanced computational attacks, ensuring enhanced security and maintaining the confidentiality and integrity of information. To address these challenges, this paper presents a novel hybrid security model that combines the honeybee optimization algorithm with quantum cryptography. Quantum cryptography ensures the secure generation and distribution of encryption keys within healthcare systems, mitigating vulnerabilities. The integration of Honeybee optimization in the cloud server enhances data integrity and optimizes system performance. The honeybee fitness function accurately estimates the integrity of encrypted data by comparing it with the original hash value. Additionally, a user verification module is developed to authenticate authorized users and prevent unauthorized data access. The proposed framework is applied to a publicly available smart healthcare dataset, revealing a confidentiality rate of 98.00 %, a data integrity rate of 99.00 %, and a low error rate of 0.12 %. These results showcase the superior performance of the model compared to existing techniques. This research provides a robust solution to the security and privacy concerns in IoMT-based healthcare systems, fostering trust and ensuring the integrity of sensitive healthcare information.

In recent times, wireless sensing networks (WSNs) application usage is increased in a great way. Access to the communication channel is handled by the Medium Access Control (MAC) layer. At the MAC, a control channel is utilized to determine collision-free data transport pathways. As a result, with cognitive radio (CR) technology, the control channel architecture is critical to obtaining mandatory quality of Service (QoS). However, latency, network communication delay, and energy consumption are the main problem. In this article, a novel African Buffalo allied Jellfish Optimization (AFJO) is proposed for clustering and optimal Cluster Head (CH) selection. The hybrid intelligence method uses a unique probabilistic assessment rule purpose as a fitness role to find the best data transmission path while avoiding congestion, which is named as Fuzzy Interfaced Red Deer (FIRD). The proposed protocol’s performance is evaluated using Network Simulator (NS2), which takes into account parameters such as energy consumption, computational complexity, and Quality of Service (QoS) performance with radio frequency integrated parameters. The suggested technique decreases energy consumption, end-to-end latency, communication overhead, and maximizes network throughput when compared to state-of-the-art cross layer cognitive mac protocol for WSNs system approaches.

Remote health monitoring plays a pivotal role in tracking the health of patients outside traditional clinical settings. It facilitates early disease detection, preventive interventions, and cost-effective healthcare, relying on electronic sensors to collect essential data. The accuracy of medical data analysis is paramount for early disease identification, patient treatment, and optimizing social services, particularly as data utilization expands within the biomedical and healthcare sectors. However, the presence of incomplete or inconsistent data hampers the accuracy of analysis. This paper introduces a novel approach, employing Grey Wolf Optimization-based Convolutional Neural Networks (GW-CNN), to recover missing data and enhance cardiac disease identification. The proposed method combines data imputation techniques for identifying and predicting missing values in electronic sensor data, followed by feature extraction to capture relevant information. The CNN model leverages Grey Wolf Optimization to improve its predictive capabilities for cardiac disease. Comparative evaluation against existing models assesses the new model’s performance in terms of specificity, accuracy, precision, recall, and F1 score.

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.

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.

The Internet of Things (IoT) refers to a new paradigm in which everyday products and tools are networked and shared amongst people and other devices. As a result of the benefits it provides to homeowners in terms of convenience and efficiency, IoT technology is expected to become a critical component in the creation of smart homes. Integrity, authenticity, and confidentiality of the data sensed, gathered, and transferred by IoT items in smart homes provide additional security and privacy problems brought on by the application of the IoT model. Because of these issues, smart homes based on the Internet of Things are not secure and are susceptible to a wide variety of security threats. A comprehensive risk assessment should precede any security measures in order to identify and resolve any underlying concerns. Analytical representation based on assessing security issues shows that the suggested method reduces throughputs by 5–17%, latency by 0–0.08, and time by 1–3% compared to the FBC approach and the EESCFD method.

Healthcare data, encompassing vital signs such as temperature, heart rate, respiratory rate, and blood pressure along with results from the lab and needs to be kept secure and private as it is used to monitor the health of patients in real-time and make decisions on their healthcare. Unfortunately, healthcare data in IoT-based cloud systems are at risk of malicious attacks, including access control bypass, malware injection, and data privacy violations; this is due to the distributed nature of cloud computing, which requires the processing and storage of sensitive information among multiple servers, heightening the risk of data breaches. The current security algorithms are unable to understand the complexity of healthcare data for effective security; thus, robust security measures based on the inherent data features must be employed to strengthen encryption, access control, and two-factor authentication for guaranteeing the security of healthcare data in a cloud-powered IoT environment. In this paper, we have developed a novel Whale-fitness based Recurrent Neural network Scheme (WbRNS), that can understand data patterns and employ attributes-based Encryption (ABE) to transform plain text into ciphertext. The continual updating of whale fitness enables the neural network model to monitor for attacks and unauthorized network access.

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.