License Plate Recognition (LPR) is essential for the Internet of Vehicles (IoV) since license plates are a necessary characteristic for distinguishing vehicles for traffic management. As the number of vehicles on the road continues to grow, managing and controlling traffic has become increasingly complex. Large cities in particular face significant challenges, including concerns around privacy and the consumption of resources. To address these issues, the development of automatic LPR technology within the IoV has emerged as a critical area of research. By detecting and recognizing license plates on roadways, LPR can significantly enhance management and control of the transportation system. However, implementing LPR within automated transportation systems requires careful consideration of privacy and trust issues, particularly in relation to the collection and use of sensitive data. This study recommends a blockchain-based approach for IoV privacy security that makes use of LPR. A system handles the registration of a user’s license plate directly on the blockchain, avoiding the gateway. The database controller may crash as the number of vehicles in the system rises. This paper proposes a privacy protection system for the IoV using license plate recognition based on blockchain. When a license plate is captured by the LPR system, the captured image is sent to the gateway responsible for managing all communications. When the user requires the license plate, the registration is done by a system connected directly to the blockchain, without going through the gateway. Moreover, in the traditional IoV system, the central authority has full authority to manage the binding of vehicle identity and public key. As the number of vehicles increases in the system, it may cause the central server to crash. Key revocation is the process in which the blockchain system analyses the behaviour of vehicles to judge malicious users and revoke their public keys.

Introduction: Wind speed and solar radiation are two of the most well-known and widely used renewable energy sources worldwide. Coal, natural gas, and petroleum are examples of fossil fuels that are not replenished and are thus non-renewable energy sources due to their high carbon content and the methods by which they are generated. To predict energy production of renewable sources, researchers use energy forecasting techniques based on the recent advances in machine learning approaches. Numerous prediction methods have significant drawbacks, including high computational complexity and inability to generalize for various types of sources of renewable energy sources. Methodology: In this paper, we proposed a novel approach capable of generalizing the prediction accuracy for both wind speed and solar radiation forecasting data. The proposed approach is based on a new optimization algorithm and a new stacked ensemble model. The new optimization algorithm is a hybrid of Al-Biruni Earth Radius (BER) and genetic algorithm (GA) and it is denoted by the GABER optimization algorithm. This algorithm is used to optimize the parameters of the proposed stacked ensemble model to boost the prediction accuracy and to improve the generalization capability. Results: To evaluate the proposed approach, several experiments are conducted to study its effectiveness and superiority compared to other optimization methods and forecasting models. In addition, statistical tests are conducted to assess the significance and difference of the proposed approach. The recorded results proved the proposed approach’s superiority, effectiveness, generalization, and statistical significance when compared to state-of-the-art methods. Conclusions: The proposed approach is capable of predicting both wind speed and solar radiation with better generalization.

Clustering of multi-view data has got broad consideration of the researchers. Multi-view data is composed through different domain which shows the consistent and complementary behavior. The existing studies did not draw attention of over-fitting and sparsity among the diverse view, which is the considerable issue for getting the unique consensus knowledge from these complementary data. Herein article, a multi-view clustering approach is recommended to provide the consensus solution from the multi-view data. To accomplish this task, we exploit non-negative matrix factorized method to generate a cost function. Further, manifold learning model is used to build the graph through the nearest neighbor strategy, which is effective to save the geometrical design for data and feature matrix. Furthermore, the over-fitting problem, sparsity is handled through adaption of frobenious norm, and $L_{1}$ -norm on basis and coefficient matrices. The whole formulation is done through the mathematical function, which is optimized through the iterative updating strategy to get the optimal solution. The computational experiment is carried on the available datasets to exhibits that the proposed strategy beats the current methodologies in terms of clustering execution.

Ongoing researches on multiple view data are showing competitive behavior in the machine learning field. Multi-view clustering has gained widespread acceptance for managing multi-view data and improves clustering efficiency. Large dimensionality in data from various views has recently drawn a lot of interest from researchers. How to efficiently learns the appropriate lower dimensional subspace which can manage the valuable information from the diverse views is challenging and considerable issue. To concentrate on the mentioned issue, we asserted a novel clustering approach for multiple view data through low-rank representation.We consider the importance of each view by assigning the weight control factor.We combine consensus representation with the degree of disagreement among lower rank matrices. The single objective function unifies all factors. Furthermore, we give the efficient solution to update the variable and to optimized the objective function through the Augmented Lagrange’s Multiplier strategy. Real-world datasets are utilized in this study to exemplify the efficiency of the introduced technique, and it is contemplated to preceding algorithms to demonstrate its superiority.

These days, technology has a significant role in the success of selling and buying goods through m-commerce applications. Therefore, this study aims to study the impact of dimensions of M-interactivity on customers’ engagement in m-commerce and the effect of customers’ attention in m-commerce on customer loyalty towards m-commerce applications. The questionnaire was distributed among Saudi citizens. The five hundred questionnaire links were distributed randomly among Saudi citizens. The partial least squares (PLS) were implemented to analyse the data to test the proposed hypotheses and get the rest of the results. The results revealed that the questionnaire used in this study was valid and reliable. Also, it revealed that active control, connectedness, responsiveness, and synchronicity positively impact customers’ engagement in m-commerce applications. Furthermore, it showed that ubiquitous connectivity and personalization do not positively impact customers’ attention in m-commerce. In addition, the results revealed that customer engagement positively impacts customer loyalty (LO) in m-commerce applications.

In this work, we provide an overview of the most common risks and threats related to information technology faced by high school students in Saudi Arabia. In this context, this work starts from the concept that using information technology is the basis of contemporary life once it has penetrated and become part of our daily activities, which also makes us challenged by the threats and risks that result from these technologies. Thus, we have written and applied a questionnaire to groups of high school students in all provinces of Saudi Arabia. The study sample of high school students who participated and completed the questionnaire was 2,312, divided into 1,128 male and 1,184 female students. In addition, these students were randomly selected from all 13 administrative regions in KSA. Specifically, we have examined the risks and threats of secondary school students using information technology, along with methods for securing their use of it in terms of awareness, envisioning, and response. Our results indicate a necessity to raise the awareness level regarding information security and the risks that come along with the use of technology through school guidance and counseling programs. Finally, we suggest that holding informational meetings and strengthening the partnership between the school and the family can be an effective way of mitigating the risks directly caused by the unsupervised use of information technology.