Cloud computing has revolutionized organizational operations by providing convenient, on-demand access to resources. The emergence of the Internet of Things (IoT) has introduced a new paradigm for collaborative computing, leveraging sensors and devices that generate and process vast amounts of data, thereby resulting in challenges related to scalability and security, making the significance of conventional security methods even more pronounced. Consequently, in this paper, we propose a novel Scalable and Secure Cloud Architecture (SSCA) that integrates IoT and cryptographic techniques, aiming to develop scalable and trustworthy cloud systems, thus enabling multi-user systems and facilitating simultaneous access to cloud resources by multiple users. The design adopts a decentralized approach, utilizing multiple cloud nodes to handle user requests efficiently and incorporates Multicast and Broadcast Rekeying Algorithm (MBRA) to ensure the privacy and confidentiality of user information, utilizing a hybrid cryptosystem that combines MBRA, Post Quantum Cryptography (PQC) and blockchain technology. Leveraging IoT devices, the architecture gathers data from distributed sensing resources and ensures the security of collected information through robust MBRA-PQC encryption algorithms, while the blockchain ensures that the confidential data is stored in distributed and immutable records. The proposed approach is applied to several datasets and the effectiveness is validated through various performance metrics, including response time, throughput, scalability, security, and reliability. The results highlight the effectiveness of the proposed SSCA, showcasing a notable reduction in response time by 1.67 seconds and 0.97 seconds for 250 and 1000 devices, respectively, in comparison to the MHE-IS-CPMT. Likewise, SSCA demonstrated significant improvements in the AUC values, exhibiting enhancements of 6.30%, 6.90%, 7.60%, and 7.30% at the 25-user level, and impressive gains of 5.20%, 9.30%, 11.50%, and 15.40% at the 50-user level when compared to the MHE-IS-CPMT, EAM, SCSS, and SHCEF models, respectively.

The Internet of Things (IoT) based Smart city applications are the latest technology-driven solutions designed to collect and analyze data to enhance the quality of life for urban residents by creating more sustainable, efficient, and connected communities. Communication nodes are networked independently to monitor the circumstances, where they require being more energy-efficient and securing, to improve the performance of sustainable cities. Because, the enormous number of networked devices makes the smart cities application vulnerable to many security breaches, all of which have serious ramifications for a city and its residents’ safety, well-being, and economic development. Low-powered sensors have limitations in terms of battery life, short transmission range, and security considerations, despite the fact that the combination of edge computing and Green IoT considerably enhances network performance in terms of processing and data storage. Consequently, it is necessary to implement an advanced approach to provide energy resources with secure data transmission for sustainable cities. Therefore, this research proposes an Intelligent Buffalo-based Secure Edge-enabled Computing (IB-SEC) framework for smart cities by IoT platform, which aims to enhance communication efficiency, and reliability, with minimizing latency in terms of energy consumption and data security for data transmission. The developed IB-SEC platform utilizes a combination of the African Buffalo Optimization (ABO) algorithm and a Distributed Hash function-based security algorithm to enhance the efficiency, security, and reliability of data transmission in IoT-based smart city networks. This platform leverages the capabilities of secure edge computing and MAC protocols to achieve its goals, by implementing encryption, authentication, and access control mechanisms that ensure the wireless communication is secure, and the data is protected against unauthorized access. Overall, the IB-SEC framework provides an advanced approach to secure IoT networks and enable smart city applications. Moreover, the developed platform enables the integration of heterogeneous edge devices, sensors, and systems which is effectively managed and adapted by Median Access Control (MAC) protocols. The IB-SEC is implemented in MATLAB and validated through cutting-edge security algorithms in terms of improved security, reliability, throughput, and reduced latency and energy consumption.

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.

Background: Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging (MRI) dataset is critical for evaluating global and regional cardiac functions and diagnosing cardiovascular diseases. LV clinical metrics such as LV volume, LV mass and ejection fraction (EF) are frequently extracted based on the LV segmentation from short-axis MRI images. Manual segmentation to assess such functions is tedious and time-consuming for medical experts to diagnose cardiac pathologies. Therefore, a fully automated LV segmentation technique is required to assist medical experts in working more efficiently. Method: This paper proposes a fully convolutional network (FCN) architecture for automatic LV segmentation from short-axis MRI images. Several experiments were conducted in the training phase to compare the performance of the network and the U-Net model with various hyper-parameters, including optimization algorithms, epochs, learning rate, and mini-batch size. In addition, a class weighting method was introduced to avoid having a high imbalance of pixels in the classes of image’s labels since the number of background pixels was significantly higher than the number of LV and myocardium pixels. Furthermore, effective image conversion with pixel normalization was applied to obtain exact features representing target organs (LV and myocardium). The segmentation models were trained and tested on a public dataset, namely the evaluation of myocardial infarction from the delayed-enhancement cardiac MRI (EMIDEC) dataset. Results: The dice metric, Jaccard index, sensitivity, and specificity were used to evaluate the network’s performance, with values of 0.93, 0.87, 0.98, and 0.94, respectively. Based on the experimental results, the proposed network outperforms the standard U-Net model and is an advanced fully automated method in terms of segmentation performance. Conclusion: This proposed method is applicable in clinical practice for doctors to diagnose cardiac diseases from short-axis MRI images.

Cloud computing is a potential platform transforming the health sector by allowing clinicians to monitor patients in real-time using sensor technologies. However, the users tend to transmit sensitive and classified medical data back and forth to cloud service providers for centralized processing and storage. This presents opportunities for hackers to steal data, intercept data in transit, and deprive patients and healthcare providers of private information. Consequently, Security and privacy are the primary concerns that must be addressed for the healthcare organization to trust and adopt the cloud computing platform. We present data sanitization and restoration processes to generate the keys from the acquired data and develop a multi-objective function for the hiding ratio, degree of modification, and information preservation ratio. We then employed the Bee-Foraging Learning-based Particle Swarm Optimization (BFL-PSO) algorithm to acquire the optimal key while transferring healthcare data into the cloud to ensure high Security. The experiment is carried out on the UHDDS dataset. The performance is assessed in terms of Security, delay time, encryption time, error rate, and convergence speed, with the results contrasted to state-of-the-art works. The performance study demonstrates that the suggested algorithm has higher Security than cutting-edge security algorithms.

Early diagnosis of diseases such as cancer, cardiovascular, diabetes, HIV, AIDS, Lyme, and tuberculosis can enable timely treatment, which enhances efficacy and helps reduce the severity and mortality while lowering overall healthcare costs. Predictive analytics is an emerging approach that comprehends trends in patient data to identify patterns in patient data, detect subtle signs of diseases, and enable early diagnosis for more effective treatment. However, traditional predictive diagnosis approaches rely on statistical models that are contingent on the data’s average pattern and are unable to capture the data’s inherent patterns, leading to ineffective and inaccurate diagnoses. In this paper, we introduce a novel Artificial Spider Monkey-based Random Forest (ASM-RF) hybrid framework that combines the predictive analytics of a Random Forest algorithm with Artificial Intelligence to evaluate patient health data, spot patterns, diagnose modest indications, and automate intelligent decisions for enhancing the healthcare system. The proposed ASM-RF hybrid framework employs a fitness function to evaluate the spider monkey’s performance at the classification layer and update accuracy and recall, resulting in more accurate patient disease diagnoses and automating timely treatment decisions for improving the overall healthcare system. Moreover, we use Identity-based Encryption (IBE), which enables the encryption of data with private and public keys coupled with users’ identities as the encryption keys, assisting in enhancing the security of the healthcare system. A dataset collected from three different IoT sensor devices, ten participants, and twelve activities is employed to simulate the proposed ASM-RF hybrid framework, which is then contrasted to cutting-edge predictive diagnostic algorithms in terms of accuracy, precision, Area under the Curve, execution time, recall, and F-measure. The proposed method exhibits superior performance when compared to conventional methods, as evidenced by its exceptional accuracy (99.52%), precision (99.12%), Area Under the Curve (AUC) (99.00%), recall (99.22%), F-measure (97.12%), and significantly reduced execution time (6s).