The usage of blockchain technology has been significantly expanded with smart contracts and blockchain oracles. While smart contracts enables to automate the execution of an agreement between untrusted parties, oracles provide smart contracts with data external to a given blockchain, i.e., off-chain data. However, the validity and accuracy of such off-chain data can be questionable that compromises the transparency and immutability chacteristics of blockchain. Despite many studies on the trustworthiness of blockchain oracles, more precisely, off-chain data, their solutions are often ‘short-sighted’ and dependent on binary decisions. In this paper, we present a novel graph-based profiling method to determine the trustworthiness of blockchain oracles. We construct a graph with oracles as nodes and cumulative average discrepancies of validity and accuracy of data as edge weights. Our profiling method continues to update the graph, edge weights in particular, to distinguish trustworthy oracles. Clearly, this discourages the provision of false and inaccurate data. We have conducted an evaluation study to see the effectiveness of our proposed method, in which we have run the experiments utilizing the Ethereum network. Additionally, we have also calculated the cost of running these experiments. Consequently, our experiment results show that the proposed method achieves around 93% accuracy in identifying the trustworthiness of data sources.

Recently, indoor systems for growing plants have emerged as a promising approach to address the problems related to extreme weather conditions outdoors. However, such systems must manage the plants surrounding environments to satisfy the environmental and the economical requirements. In this line, any proposed solution must address challenging factors such as plants diseases and unordinary climate situations. In this paper, propose an internet of things (IoT) indoor system that can be used to facilitate the plant growing process. The proposed system is designed to provide alternatives for outdoor climate dependency such as the vitamins provided through sunlight. Moreover, renewable energy sources (sunlight) are employed to reduce the impact on the environment. With the help of several types of sensors, the system continuously monitors the plants through their growing journey. Whereas actuator devices are employed to control the plant-feeding process based on the sensors’ reported values. All the collected data will be uploaded to the cloud for analysis, utilizing a website. Additionally, the architecture of the provided system eliminates the need for human involvement, which has a degrading effect on the plant growing process.

To address the critical need for enhancing Quality of Service (QoS) monitoring in the logistics service delivery domain, this paper introduces a blockchain-based QoS monitoring framework that aims to automate service delivery and dispute resolution processes. Traditional QoS monitoring solutions rely heavily on human judgment and intervention, which may increase operational costs and reduce service reliability. Such behaviour highlights the demand for more efficient and transparent logistics services, which underlines the importance of utilizing blockchain technology’s immutable and decentralized nature. The proposed framework in this paper employs a graph-based approach to transform QoS requirements into Deterministic Finite Automata (DFA) format. This strategy simplifies delivery monitoring and the identification of service violations through efficient DFA traversal. By using the Ethereum network as the deployment environment, we demonstrate Traversing a DFA is computationally efficient and reduces operational costs. Extensive experiments were conducted to evaluate the cost-effectiveness of the framework, showing that monitoring a delivery using this framework costs approximately $2.59. This finding underscores the framework’s advantages in operational cost optimization compared to traditional human-based methods. Moreover, the decentralized nature of our proposed framework allows customers and businesses to jointly define and monitor QoS parameters. Therefore, a transparent and trust-based relationship between the business partners can be established.

Academic advising (AA) is an integral part of university education, as it has an indispensable role in helping students fulfil their goals in higher education, become responsible for their own learning, and formulate meaningful educational plans that perfectly match the abilities of each student. For this reason, educational institutions around the world are striving to upgrade their AA systems (AAS) to provide their students with personalized experiences. Meanwhile, modern technology can improve the advising process and facilitate the accomplishment of the corresponding tasks. Therefore, the integration of technology into AA not only offers more flexibility for the students but also improves the delivery of advising services. This study aimed i) to identify those factors that mainly affect the AAS services being offered in universities; and ii) to examine how the satisfaction of students with AAS is affected by service quality. A total of 400 students from the Information Technology College of Al-Hussein Bin Talal University (AHU) were invited to participate in an online survey for data collection, and a response rate of 90.50% was recorded. Results show that the satisfaction of students with the AAS is affected by trust, network quality, service quality, system quality, information quality, and perceived risk.

Typically, the problem of scheduling exams for universities aims to determine a schedule that satisfies logistics constraints, including the number of available exam rooms and the exam delivery mode (online or paper-based). The objective of this problem varies according to the university’s requirements. For example, some universities may seek to minimize operational costs, while others may work to minimize the schedule's length. Consequently, the objective imposed by the university affects the complexity of the problem. In this study, we present a grouping-based approach designed to address the problem of scheduling the exam timetable. The approach begins by profiling the courses’ exams based on their requirements, grouping exams with similar requirements to be scheduled at the same time. Then, an insertion strategy is used to obtain the exam schedule while satisfying the imposed constraints of the targeted university. We applied this approach to the problem of exam scheduling at Al-Hussein Bin Talal University in Jordan and achieved a balanced exam schedule that met all the imposed constraints.

The field of instructional technology has experienced significant growth in recent times. Due to the rapid shift towards online courses, the technology-based learning system is facing challenges in ensuring quality and assurance. The aim of this study was to develop online course evaluation tools by proposing a new algorithm to assess the success of the provided online courses and address quality assurance issues. The proposed algorithm is based on quality matters (QM) attributes and the use of fuzzy soft expert sets (FSESs). One key advantage of the proposed algorithm is that it incorporates experts’ opinions, which significantly contributes to achieving the study objective. The proposed algorithm was successfully implemented using the ASP.NET programming language. It resulted in the development of an EOC-FSES prototype system. The experimental evaluation of the prototype system confirms that it requires low effort and achieves high levels of performance, satisfaction, and behavioral intention to use. This paper includes several recommendations and suggestions.