Despite the increasing reliance on the Internet of Things (IoT) as a basic infrastructure for future applications. Integrating blockchain technology with IoT extensively helps provide basic security requirements owing to the built-in security structure of the blockchain. Healthcare IoT applications are one of the industries that will be revolutionized by using IoT-blockchain technology. This review aims to provide an integrated understanding of blockchain IoT healthcare applications. The methodology of our paper is to review the literature from 2018 to 2023, paying more attention to research that intersects and integrates IoT, Blockchain, and healthcare applications. This review addresses IoT-based blockchain types and technologies, healthcare applications, sensors, hardware tools, application databases, programming languages, software tools, challenges and open issues, and solutions with their advantages and limitations. We then compared our work with existing works on common metrics. The study in this paper includes 14 blockchain IOT healthcare applications: remote patient monitoring, patient tracking, disease prediction, tracking COVID-19, image retrieval, security of medical records, smart telemedicine, securing the Internet of Medical Things, big data, blockchain and sensors, accurate medical decisions, health monitoring, tracking soldiers, and solar energy.

The Internet of Things (IoT) is emerging from its infancy and establishing itself as a component of the future Internet. However, the ability to manage a huge number of IoT devices is one of the IoT technical challenges. To implement access control, traditional schemes typically rely on a trusted central organization. Traditional centralized access control systems of the IoT lead to the shortcomings of a single point of failure, low overall system efficiency, and ethical and privacy issues. To overcome such challenges, an attribute‐based access control model using Hyperledger Fabric blockchain (ABAC‐HLFBC) is proposed in this paper. By adopting ABAC, it is no longer to create access control lists (ACLs) or assign roles to all system users. Instead, ABAC grants access based on the attributes presented by the target. No one is permitted access unless he/she possesses sufficient attributes that correspond to the access policy. The Hyperledger Fabric Raft consensus mechanism has been used to verify the transaction on the proposed model because it has a demanding feature for faster and less complicated consensus in comparison to the Kafka ordering service. To evaluate the proposed model, it has been tested against the most recent previous work, called fabric‐iot model. The proposed model has been tested and evaluated in two parts. The first part tests the cost time using a client test program written in Golang. The second part tests the latency and throughput using the Hyperledger Caliper benchmark tool. Results show that the proposed model efficiently outperforms the previous work in terms of the performance metrics mentioned above.

The emergence of 6G cognitive radio IoT networks introduces both opportunities and complexities in spectrum access and security. Blockchain technology has emerged as a viable solution to address these challenges, offering enhanced security, transparency, and efficiency in spectrum management. This survey paper offers a thorough analysis of recent advancements in blockchain-enabled security mechanisms specifically for spectrum access within 6G cognitive radio IoT networks. Covering literature from 2019 to the present, the paper highlights significant contributions and developments in integrating blockchain technology with cognitive radio and IoT systems. It reviews spectrum access security and shows how blockchain’s decentralized approach can solve related issues. Key areas of focus include secure authentication systems, tamper-resistant spectrum sensing, decentralized databases, and smart contracts for spectrum management. The paper also addresses ongoing challenges like interoperability, scalability, and the need for comprehensive security frameworks. Future research directions are proposed, emphasizing the development of advanced blockchain protocols, integration with machine learning, and addressing regulatory and standardization concerns. This paper provides valuable insights for researchers and practitioners aiming to leverage blockchain technology, alongside ML/AI, to enhance security and efficiency in next-generation cognitive radio IoT networks.

Websites can improve their security and protect against harmful Internet attacks by incorporating CAPTCHA verification, which assists in distinguishing between human users and robots. Among the various types of CAPTCHA, the most prevalent variant involves text-based challenges that are intentionally designed to be easily understandable by humans while presenting a difficulty for machines or robots in recognizing them. Nevertheless, due to significant advancements in deep learning, constructing convolutional neural network (CNN)-based models that possess the capability of effectively recognizing text-based CAPTCHAs has become considerably simpler. In this regard, we present a CAPTCHA recognition method that entails creating multiple duplicates of the original CAPTCHA images and generating separate binary images that encode the exact locations of each group of CAPTCHA characters. These replicated images are subsequently fed into a well-trained CNN, one after another, for obtaining the final output characters. The model possesses a straightforward architecture with a relatively small storage in system, eliminating the need for CAPTCHA segmentation into individual characters. Following the training and testing of the suggested CNN model for CAPTCHA recognition, the experimental results demonstrate the model’s effectiveness in accurately recognizing CAPTCHA characters.

In recent years, Arabic natural language processing has achieved remarkable advancements, particularly with the advent of large language models that have enhanced the analysis of diverse Arabic texts, including literary and artistic works such as poetry. However, these models encounter specific challenges when dealing with Arabic poetry, which is characterized by complex metrical structures, varied themes, and unique linguistic intricacies. Currently available poetry corpora are often limited in scope and depth, and typically lack the comprehensive coverage required for sophisticated computational analysis tasks. To address this gap, this paper introduces “Diwan”, the largest and most precise annotated Arabic poetry dataset/corpus, designed to support scientific research and facilitate the development of AI applications in this domain. "Diwan" comprises approximately 14 million verses of poetry across 16 major categories and includes detailed annotations related to poetic genres, prosodic structures, thematic content, linguistic patterns, and poet-specific metadata. This dataset/corpus has been meticulously curated using advanced data collection methods, rigorous normalization and annotation protocols, and expert oversight from specialists in Arabic prosody and poetry. By leveraging intelligent annotation algorithms, Diwan serves as a foundational resource and benchmark dataset for advancing research in fields such as automatic poetry generation, metrical analysis, thematic classification, and plagiarism detection. "Diwan" has been compared against 4 leading corpora for Arabic poetry; this comparison proves that "Diwan" outperforms all of them in terms of scope, and depth. Besides, "Diwan" is constructed and structured in a way enables AI-powered analysis and deep-learning-based analysis to work accurately. By providing an unprecedented foundation for computational exploration of Arabic poetry, Diwan opens up new avenues and possibilities for intelligent applications and promotes the digital analysis of this rich literary heritage.