The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchain-enabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system.

In the era of smart healthcare, Internet of Medical Things (IoMT)-based Cyber-Physical Systems (CPS) play an important role, while accessing, monitoring, assessing, and prescribing patients ubiquitously. Efficient authentication and secure data transmission are the influential impediments of these networks that need to be addressed to maintain credence among clients, healthcare specialists, pharmacologists, and other associated entities. To address the authentication and data privacy issues in smart healthcare, in this paper we propose a lightweight hybrid deep learning protocol to achieve security and privacy. To achieve better results, we enabled the decentralized authentication of legitimate patient wearable devices to minimize computation cost, authentication time, and communication overheads with the help of an ML technique to predicate and forward the authentication attributes of patient wearable devices to the next concerned trusted authority, when it is shifted from region to another region. Simulation upshots of the ML scheme exhibited extraordinary security features with the cost-effective validation of legal patient wearable devices accompanied by worthwhile communication functionalities compared with previous work. However, the application of IoT-based medical devices and managing such a broad, sophisticated medical IoT system on standard Single Cloud platforms (CP) would be extremely tough. We propose a scalable FC with a blockchain-based architecture for a 5G-enabled IoMT platform. To work on an FC architecture with flowing effects, low overheads, and secure storage (SS), this research proposes a secured blockchain-based fogBMIoMT communication mechanism.

The development of mobile learning apps might fail due to poor selection of the suitable technical requirements for mobile devices. This will affect the quality of mobile learning applications and, thus, will increase the development cost of mobile learning apps. Due to the above issues, we need to determine the most appropriate technical quality requirements for the development of mobile learning apps that meet user requirements. To achieve that, we propose a comprehensive framework to capture the most suitable technical quality requirements for mobile learning apps. A Delphi method was used to collect, evaluate, and analyze the data for this study. As a result of our Delphi study, we have identified nineteen technical quality requirements, divided into six quality dimensions, for the development of mobile learning applications. The proposed framework is expected to be a guideline for mobile apps designers and developers to successfully develop mobile learning apps.

This study focuses on hybrid synchronization, a new synchronization phenomenon in which one element of the system is synced with another part of the system that is not allowing full synchronization and nonsynchronization to coexist in the system. When lim t ⟶ ∞ Y − α X = 0 , where Y and X are the state vectors of the drive and response systems, respectively, and Wan ( α  =  ∓ 1)), the two systems’ hybrid synchronization phenomena are realized mathematically. Nonlinear control is used to create four alternative error stabilization controllers that are based on two basic tools: Lyapunov stability theory and the linearization approach.

Artificial intelligence applications (AIA) increase innovative interaction, allowing for a more interactive environment in governmental institutions. Artificial intelligence is user-friendly and embraces an effective number of features among the different services it offers. This study aims to investigate users’ experiences with AIA for governmental purposes in the Gulf area. The conceptual model comprises the adoption properties (namely trialability, observability, compatibility, and complexity), relative advantage, ease of doing business, and technology export. The novelty of the paper lies in its conceptual model that correlates with both personal characteristics and technology-based features. The results show that the variables of diffusion theory have a positive impact on the two variables of ease of doing business and technology export. The practical implications of the current study are significant. We urge the concerned authorities in the governmental sector to understand the significance of each factor and encourage them to make plans, according to the order of significance of the factors. The managerial implications provide insights into the implementation of AIA in governmental systems to enhance the development of the services they offer and to facilitate their use by all users.