Cross-lingual sentiment analysis has developed as a significant area of research in linguistics, especially for languages having diverse syntactic and morphological structures. The objective of this study emphasizes creating a sophisticated sentiment analysis model that connects English and Arabic datasets, two languages with distinct linguistic problems. Using cutting-edge transformer architectures, we utilize pre-trained models—BERT for English and AraBERT for Arabic—to address the challenges of morphologically rich but resource-limited languages such as Arabic. The foundation of this study is the IMDB movie review dataset, which is similarly structured and large for both languages. To find the best deep learning architecture, we conducted extensive experiments using Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and attention methods. While LSTM-based models produced competitive results, transformer-based models such as proposed BERT-LA that included bidirectional and attention layers outperformed them substantially, particularly on Arabic and English data. Furthermore, ablation research was conducted to evaluate the models' performance using important measures such as accuracy, precision, recall, and the F1-score. Our model got an impressive 97.04% accuracy on the English dataset and 98.02% on the Arabic dataset. This study contributes to understanding how language-specific embeddings and transformer models affect under-represented languages.

Abstract In the rapidly evolving digital era, the growing trend of conducting real estate e-business transactions through online platforms has led to escalated challenges in ensuring transactional security and trust. These challenges underscore the importance of balancing transparency with data privacy and enhancing accountability in this field. As an extension of our previously published work (Abualhamayl AJ, Almalki MA, Al-Doghman F, Alyoubi AA, Hussain FK (2023) Towards fractional NFTs for joint ownership and provenance in real estate. In: 2023 IEEE international conference on e-business engineering (ICEBE), p. 143–8. 10.1109/ICEBE59045.2023.00022.), this paper introduces the Global Real Estate Platform (GREP), a novel hybrid blockchain system that utilizes real estate provenance to establish a secure and trustworthy environment for real estate e-business, specifically focusing on two key challenges: ensuring data authenticity and effectively managing access rights. Integral to GREP's design is the involvement of government entities, which is essential for maintaining the required balance between transparency, privacy, and high levels of accountability. This proposed framework is explained conceptually and demonstrated practically, offering an innovative perspective on the integration of hybrid blockchain technology in the real estate system. Furthermore, our research encompasses a detailed implementation, using various tools, and an in-depth examination of three use cases. This combined analysis effectively demonstrates GREP's efficacy in addressing the targeted challenges in the field. While acknowledging the system's limitations, including challenges in user adoption and performance variability under different network conditions, our findings open new avenues for further exploration, such as landlords' payment histories and utility bills, and using blockchain as a secondary user identifier. These features collectively highlight the transformative potential of blockchain technology in real estate e-business.

The paradigm of edge computing has formed an innovative scope within the domain of the Internet of Things (IoT) through expanding the services of the cloud to the network edge to design distributed architectures and securely enhance decision-making applications. Due to the heterogeneous, distributed and resource-constrained essence of edge Computing, edge applications are required to be developed as a set of lightweight and interdependent modules. As this concept aligns with the objectives of microservice architecture, effective implementation of microservices-based edge applications within IoT networks has the prospective of fully leveraging edge nodes capabilities. Deploying microservices at IoT edge faces plenty of challenges associated with security and privacy. Advances in Artificial Intelligence (AI) (especially Machine Learning), and the easy access to resources with powerful computing providing opportunities for deriving precise models and developing different intelligent applications at the edge of network. In this study, an extensive survey is presented for securing edge computing-based AI Microservices to elucidate the challenges of IoT management and enable secure decision-making systems at the edge. We present recent research studies on edge AI and microservices orchestration and highlight key requirements as well as challenges of securing Microservices at IoT edge. We also propose a Microservices-based edge computing framework that provides secure edge AI algorithms as Microservices utilizing the containerization technology to offer automated and secure AI-based applications at the network edge.

The Internet of Things (IoT) epitomizes the upcoming eminent transition in the world's economy and human lifestyle where people and various objects are correlated within networks. Data Aggregation is a technique which can be used to mitigate Big Data challenges within IoT. This paper provides an overview of various approaches for aggregation of data in IoT infrastructure. A new class of reliable Data Aggregation algorithm is discussed as well. This new class of algorithm uses a consensus based aggregation with fault tolerance methodology in Fog Computing. The new approach allows promoting adaptive behavior and more efficient delivery of the aggregation outcomes to the ascendant node(s). The proposed method is fault tolerant and deals with nodes reliability issues.

Link quality protocols employ link quality estimators to collect statistics on the wireless link either independently or cooperatively among the sensor nodes. Furthermore, link quality routing protocols for wireless sensor networks may modify an estimator to meet their needs. Link quality estimators are vulnerable against malicious attacks that can exploit them. A malicious node may share false information with its neighboring sensor nodes to affect the computations of their estimation. Consequently, malicious node may behave maliciously such that its neighbors gather incorrect statistics about their wireless links. This paper aims to detect malicious nodes that manipulate the link quality estimator of the routing protocol. In order to accomplish this task, MINTROUTE and CTP routing protocols are selected and updated with intrusion detection schemes (IDSs) for further investigations with other factors. It is proved that these two routing protocols under scrutiny possess inherent susceptibilities, that are capable of interrupting the link quality calculations. Malicious nodes that abuse such vulnerabilities can be registered through operational detection mechanisms. The overall performance of the new LQR protocol with IDSs features is experimented, validated and represented via the detection rates and false alarm rates.

Out of the many computing and software oriented models that are being adopted by Computer Networking, Fog Computing has captured quite a wide audience in Research and Industry. There is a lot of confusion on its precise definition, position, role and application. The Internet of Things (IOT), todays' digitized intelligent connectivity domain, demands real time response in many applications and services. This renders Fog Computing a suitable platform for achieving goals of autonomy and efficiency. This paper is a justification of the concepts, interest, approaches, and practices of Fog Computing. It describes the need for adopting this new model and investigate its prime features by elucidating the scenarios for implementing it, thereby outlining its significance in the IoT world.