The vast amount of unstructured data spread on a daily basis rises the need for developing effective information retrieval and extraction methods. Named Entity Recognition is a challenging ...classification task for structuring data into pre-defined labels, and is even more complicated when being applied on the Arabic language due to its special traits and complex nature. This article presents a novel Deep Learning approach for Standard Arabic Named Entity Recognition that proved its out-performance when being compared to previous works. The main aim of building a new model is to provide better fine-grained results for use in the Natural Language Processing fields. In our proposed methodology we utilized transfer learning with deep neural networks to build a Pooled-GRU model combined with the Multilingual Universal Sentence Encoder. Our proposed model scored about 17% enhancement when being compared to previous work.
Due to the increasing awareness and use of cloud and edge computing, society and industries are beginning to understand the benefits they can provide. Cloud and Edge are the future of information ...management, and they have transformed the Internet into an innovative and interactive computing platform. The ultimate goal of edge/cloud computing is to reduce the use of computing resources in the network, as well as support information sharing and intercommunication efforts within the network. Secure edge computing methodologies are applied in both open and heterogeneous network systems to protect them from many potential security threats. However, these approaches only provide passive protection for normal edge computing operations, and fail to address the security measures of several applications, particularly forensics in industrial settings. Forensics applications running on edge computing must be capable of support taking legal action against invaders for malicious damage or information theft. This paper proposes an efficient and reliable forensics framework (ERFF) to address industrial intelligent edge computing critical for the industry 4.0 implementation plan. The proposed ERFF consists of a detective module and validation model, with the detective module responsible for detecting the interaction between the client terminal and the edge resource, which means the investigator is capable of gathering the evidence securely. The security-validation model integrated with ERFF is far safer than sharing common key-based cryptographic approaches. The proposed conceptual framework is tested with Live Digital Forensic Framework for a Cloud (LDF2C), and results are compared with other existing industrial frameworks that fulfill fundamental ISO/IEC 17025 accreditation requirements, including Legal Reliable Forensic Framework (LRFF), Source Identification Network Forensics Framework (SINFF) and Logging Framework for Cloud Computing Forensic (LFCCF)). These frameworks were designed to support the digital forensic requirements of industry and academia, and experimental results validate the effectiveness of the proposed framework from reliability and efficiency perspectives as well as realistic scenarios
•A novel edge forensic framework to identify the criminal (i.e. digital) activities.•Proposes an efficient model to detect digital crime at the edge.•Provides better reliability and efficiency rates at the edge as compared to other models.
In e-commerce, user reviews can play a significant role in determining the revenue of an organisation. Online users rely on reviews before making decisions about any product and service. As such, the ...credibility of online reviews is crucial for businesses and can directly affect companies' reputation and profitability. That is why some businesses are paying spammers to post fake reviews. These fake reviews exploit consumer purchasing decisions. Consequently, the techniques for detecting fake reviews have extensively been explored in the past twelve years. However, there still lacks a survey that can analyse and summarise the existing approaches. To bridge up the issue, this survey paper details the task of fake review detection, summing up the existing datasets and their collection methods. It analyses the existing feature extraction techniques. It also summarises and analyses the existing techniques critically to identify gaps based on two groups: traditional statistical machine learning and deep learning methods. Further, we conduct a benchmark study to investigate the performance of different neural network models and transformers that have not been used for fake review detection yet. The experimental results on two benchmark datasets show that RoBERTa performs about 7% better than the state-of-the-art methods in a mixed domain for the deception dataset with the highest accuracy of 91.2%, which can be used as a baseline for future studies. Finally, we highlight the current gaps in this research area and the possible future directions.
Nowadays, the economy of countries highly depends on the agriculture productivity which has a great effect on the development of human civilization. Sometimes, plant diseases cause a major reduction ...in agricultural products. This paper proposes a new approach for the automatic detection and classification of plant leaf diseases based on using the ELM deep learning algorithm on a real dataset of plant leaf images. The proposed approach uses the k-means clustering algorithm for image segmentation and applies the GLCM for feature extraction. The BDA optimization algorithm is employed for feature selection, and lastly the ELM algorithm is used for plant leaf diseases classification. The presented approach optimizes the input weights and hidden biases for ELM. The dataset used in this study includes 73 plant leaf images, such that we tested our approach on four diseases that usually affect plants, including: Alternaria alternata, Anthracnose, Bacterial blight, and Cercospora leaf spot. The experimental results show that the proposed approach has achieved encouraging results in terms of these classification measures: accuracy, error rate, recall, F score, and AUC which are 94%, 6%, 92%, 95%, and 96% respectively. Babu
Global urbanization has vastly enhanced the quality of people’s lives in various domains. Nevertheless, the global rise in urban dwellers is often accompanied by additional difficulties and ...challenges such as traffic jams, air pollution, greenhouse gas emissions, and waste production. The term ‘Smart City’ is introduced to address these challenges by pushing residents to concentrate on the innovative solutions for economic growth of their cities as well as enhancing people’s standard of living. The distributed ledger technology, also known as Blockchain, is best known for its instrumental role in forming the cryptocurrency space (e.g. Bitcoin and Ethereum). Due to features that include decentralization, transparency, democracy, security, and immutability; the deployment of Blockchain technologies in smart cities drastically improves data integrity, openness in city maintenance, and fosters the execution of reliable, transparent, safe, and democratized services and applications. In this survey, we perform an exhaustive study of research works that include the use of Blockchain technologies for smart city services and applications. First, we will examine the brief history of smart cities and Blockchain. We will then delve into the various ways that Blockchain technologies can be integrated into various smart city domains that include smart governance, smart transportation, smart grids, smart management, trade & finance, smart healthcare, smart home, e-commerce, and others that have a scope of development. Finally, this paper will provide areas of interest where Blockchain can be analyzed further to promote the development of smart cities application and services using Blockchain.
The internet of things (IoT) represent the current and future state of the Internet. The large number of things (objects), which are connected to the Internet, produce a huge amount of data that ...needs a lot of effort and processing operations to transfer it to useful information. Moreover, the organization and control of this large volume of data requires novel ideas in the design and management of the IoT network to accelerate and enhance its performance. The software defined systems is a new paradigm that appeared recently to hide all complexity in traditional system architecture by abstracting all the controls and management operations from the underling devices (things in the IoT) and setting them inside a middleware layer, a software layer. In this work, a comprehensive software defined based framework model is proposed to simplify the IoT management process and provide a vital solution for the challenges in the traditional IoT architecture to forward, store, and secure the produced data from the IoT objects by integrating the software defined network, software defined storage, and software defined security into one software defined based control model.
Providing an acceptable level of security for Internet of Things (IoT)-based critical infrastructures, such as the connected vehicles, considers as an open research issue. Nowadays, blockchain ...overcomes a wide range of network limitations. In the context of IoT and blockchain, Byzantine Fault Tolerance (BFT)-based consensus protocol, that elects a set of authenticated devices/nodes within the network, considers as a solution for achieving the desired energy efficiency over the other consensus protocols. In BFT, the elected devices are responsible for ensuring the data blocks' integrity and preventing the concurrently appended blocks that might contain some malicious data. In this paper, we evaluate the fault-tolerance with different network settings, i.e., the number of connected vehicles. We verify and validate the proposed model with MATLAB/Simulink package simulations. The results show that our proposed hybrid scenario performed over the non-hybrid scenario taking throughput and latency in the consideration as the evaluated metrics.
Finding a framework that provides continuous, reliable, secure and sustainable diversified smart city services proves to be challenging in today’s traditional cloud centralized solutions. This ...article envisions a Mobile Edge Computing (MEC) solution that enables node collaboration among IoT devices to provide reliable and secure communication between devices and the fog layer on one hand, and the fog layer and the cloud layer on the other hand. The solution assumes that collaboration is determined based on nodes’ resource capabilities and cooperation willingness. Resource capabilities are defined using ontologies, while willingness to cooperate is described using a three-factor node criteria, namely: nature, attitude and awareness. A learning method is adopted to identify candidates for the service composition and delivery process. We show that the system does not require extensive training for services to be delivered correct and accurate. The proposed solution reduces the amount of unnecessary traffic flow to and from the edge, by relying on node-to-node communication protocols. Communication to the fog and cloud layers is used for more data and computing-extensive applications, hence, ensuring secure communication protocols to the cloud. Preliminary simulations are conducted to showcase the effectiveness of adapting the proposed framework to achieve smart city sustainability through service reliability and security. Results show that the proposed solution outperforms other semi-cooperative and non-cooperative service composition techniques in terms of efficient service delivery and composition delay, service hit ratio, and suspicious node identification.
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