Using advanced algorithms to conduct a thematic analysis reduces the time taken and increases the efficiency of the analysis. Long short-term memory (LSTM) is effective in the field of text classification and natural language processing (NLP). In this study, we adopt LSTM for text classification in order to perform a thematic analysis using concordance lines that are taken from a corpora of news articles. However, the statistical and quantitative analyses of corpus linguistics are not enough to fully identify the semantic shift of terms and concepts. Therefore, we suggest that a corpus should be classified from a linguistic theoretical perspective, as this would help to determine the level of the linguistic patterns that should be applied in the experiment of the classification process. We suggest investigating the concordance lines of the articles rather than only the relationship between collocates, as this has been a limitation for many studies. The findings of this research work highlight the effectiveness of the proposed methodology for the thematic analysis of media coverage, reaching 84% accuracy. This method provides a deeper thematic analysis than only applying the classification process through the collocational analysis.

In this paper, we present a modern standard Arabic dataset based on Arabic news articles collected over a one-year period from 01/01/2021 to 12/31/2021. In total, from 12 Arabic news websites, over 500,000 articles were collected, the selection of which was driven by a variety of topics, including sports, economies, local news, politics, tech, tourism, entertainment, cars, health, and art. The development of this dataset will enable data scientists to explore and experiment effectively in the field of natural language processing, and the dataset can also be used to develop machine learning and deep learning models to classify articles according to topic. The dataset is available for download at

User-generated online ratings have become a prominent tool for hotels to enhance overall customer satisfaction. Prior research on online ratings has mainly considered commercial destinations, whereas research on religious destinations is limited. This study uses the IPA technique and beta regression analysis to investigate the destination’s (commercial and religious) varying effects on the relationship between hotel service quality attributes and customer satisfaction. In total, data from 338 hotels representing 82,704 customer reviews in two Saudi Arabian cities (Alkhobar and Makkah) were collected from Booking.com and analyzed. Makkah was selected as a key religious heritage destination, whereas Alkhobar was chosen as a commercial destination as it hosts major sea resorts, recreational parks, and shopping destinations. The results indicate that commercial and religious destination tourists do not have similar perceptions of the selected eight hotel service quality attributes. While comfort, facilities, and value-for-money service quality attributes were considered important for commercial destination tourists, religious destination tourists viewed location, cleanliness, and breakfast as important. Since effective management of customer satisfaction is essential for hotels’ profitability and sustainability, therefore it is imperative to identify and prioritize service quality attributes related to each group of customers. This will enable the efficient application of limited resources.

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.

In recent years, several research and development initiatives have focused on developing secure and trustworthy systems for the healthcare industry via pervasive and mobile healthcare (mHealth) solutions. State-of-the-art mHealth solutions primarily rely on centralized storage, such as cloud computing servers, which may escalate the maintenance costs, require ever-increasing storage infrastructure, and pose privacy and security risks to the health-critical data produced, consumed, and transmitted over ad hoc networks. To overcome these limitations, we conducted this study intending to synergize mobile computing (devices to process health-critical data) and blockchain technology (infrastructure to secure storage and retrieval of health-critical data), specifically addressing data security and privacy using a blockchain mHealth system. The research employs an incremental method by (i) developing a framework that acts as a blueprint to architect blockchain-enabled mHealth systems, (ii) implementing a suite of algorithms as a proof-of-concept to automate the framework, and (iii) experimental evaluations to validate the scalability, computation, and energy efficiency of the proposed solution. The proposed framework has been implemented as a frontend using a mobile application interface that exploits the backend via the InterPlanetary File System (IPFS) system and Ethereum blockchain for secure management of mHealth data. We use a case-study-based approach demonstrating how health units, medics, and patients can securely access and distribute health-critical data. For evaluation, we deployed a smart contract prototype on the Ethereum TESTNET network in a Windows environment to test the proposed framework. Results of the evaluation indicate (a) scalability with query response time (range: 10–41 ms), (b) computational performance (CPU utilization: 1.5% – 2.5%), and (c) energy efficiency (gas consumption: 40000 units for 1000 bytes). The proposed solution – framework, algorithms, and experimental evaluation – aims to advance state-of-the-art architecting and implementing cybersecurity mHealth solutions using blockchain technology.

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.