Background: The e-learning system has gained a phenomenal significance than ever before in the present COVID-19 crisis. The E-learning delivery mechanisms have evolved to enhanced levels facilitating the education delivery with greater penetration and access to mass student population worldwide. Nevertheless, there is still scope to conduct further research in order to innovate and improve higher quality delivery mechanism using the state-of-the-art information and communication technologies (ICT) available today. In the present pandemic crisis all the stakeholders in the higher education system, i.e., the governments, institutions, and the students expect seamless and efficient content delivery via e-learning platforms. This study proposes the adoption of the e-learning system by the integration of the model proposed by Delon and Mcclean “Information System Success Model” in Jazan University, Kingdom of Saudi Arabia (KSA) and further attempts to identify the factors affecting E-learning applications' success among the students. Methods: The data were gathered from 568 respondents. The Statistical Package for the Social Sciences version 26 (SPSS v.26.0) was used for the data analysis and one-way ANOVA is applied to test the hypothesis. Result: The overall results of this study allude to the fact that there is a significant relationship between Information system Success Model factors and the adoption of e-learning systems. The research results indicated that the information system success model has a strong associating cost-benefit value towards the adoption of e-learning systems across the Jazan University that may be further expanded to the other Saudi universities.

Big Data courses in which students are asked to carry out Big Data projects are becoming more frequent as a part of University Engineering curriculum. In these courses, instructors and students must face a series of special characteristics, difficulties and challenges that it is important to know about beforehand, so the lecturer can better plan the subject and manage the teaching methods in order to prevent students’ academic dropout and low performance. The goal of this research is to approach this problem by sharing the lessons learned in the process of teaching e-learning courses where students are required to develop a Big Data project as a part of a final degree/master course. In order to do so, a survey was carried out among a group of students enrolled in those kinds of courses during the last years. The quantitative and qualitative analysis of the obtained data led us to present a series of lessons learned that may help other participants (both students and lecturers) to better study, design and teach similar courses. In addition, the results shed light on possible existing open problems in the area of Big Data project development. Both the methodology used and the survey designed in this research were validated by a group of experts in the area using a formal statistical approach, which support the validity of the lessons learned.

Abstract: An enhanced e-learning network based on integration between cloud environment and Web is probably suggested for the existing and upcoming e-learning tools that are considered as Software as a Service (SaaS). This paper examines three most recent patents that were awarded between February 2012 and May 2014 in the area of e-learning. In addition, we have proposed a new direction which is the possibility of integration of the E-learning processes with Cloud Processes. This kind of integration needs to enable the Software Engineering concepts. Thus it is possible to design and develop the E-learning processes and then the Cloud Computing processes are integrated with the e-learning application process to formulate a new platform is called a Cloud E-learning system/platform. It is expected that such integration is an iterative, incremental development process leads to faster delivery of more constructive software. To demonstrate the proposed approach, a proposed deployment of Cloud e-learning software has been presented.

The rapid proliferation of Internet of Things (IoT) devices in healthcare, from wearable sensors to implantable medical devices, has revolutionised patient monitoring, personalised treatment, and remote care delivery. However, the resource-constrained nature of IoT devices, coupled with the sensitivity of medical data, presents critical security challenges. Traditional encryption methods, while robust, are computationally intensive and unsuitable for IoT environments, leaving sensitive patient information vulnerable to cyber threats. Addressing this gap, lightweight encryption methods have emerged as a pivotal solution to balance security with the limited processing power, memory, and energy resources of IoT devices. This paper explores lightweight encryption methods tailored for IoT healthcare applications, evaluating their effectiveness in securing sensitive data while operating under resource constraints. A comparative analysis is conducted on encryption techniques such as AES-128, LEA, Ascon, GIFT, HIGHT, PRINCE, and RC5-32/12/16, based on key performance metrics including block size, key size, encryption and decryption speeds, throughput, and security levels. The findings highlight that AES-128, LEA, ASCON, and GIFT are best suited for high-sensitivity healthcare data due to their strong security features, while HIGHT and PRINCE provide balanced protection for medium-sensitivity applications. RC5-32/12/16, on the other hand, prioritises efficiency over comprehensive security, making it suitable for low-risk scenarios where computational overhead must be minimised. The paper underscores the significant trade-offs between efficiency, security, and resource consumption, emphasising the need for careful selection of encryption methods based on the specific requirements of IoT healthcare environments. Additionally, the paper highlights the growing demand for lightweight encryption methods that balance energy efficiency with robust protection against cyber threats. These insights offer valuable guidance for researchers and practitioners seeking to enhance the security of IoT-based healthcare systems while ensuring optimal performance in resource-constrained settings.