This paper presents a cross-sectional study that demonstrates how King Abdulaziz University has responded to the lockdown imposed by the Ministry of Education in Saudi Arabia due to the Covid-19 pandemic. The purpose of this study was to examine the perceptions of students and faculty towards assessment that had to take place online due to physical or social distancing rules and lockdowns. A descriptive mixed-method study was conducted with two different self-administered questionnaires that were developed for students and faculty, respectively. A total of 547 responses were received from undergraduate students and 213 from faculty. The main finding suggests the need for a multilevel approach to the problems of cheating and plagiarism, including raising student awareness and ethics, training teachers to detect cheating methods, and institutions activating their code of practice and applying severe sanctions on those who engage in such practices.

Purpose: Through harnessing the benefits of the internet, e-learning systems provide flexible learning opportunities that can be delivered at a fixed cost at a time and place to suit the user. As such, e-learning systems can allow students to learn at their own pace while also being suitable for both distance and classroom-based learning activities. Adaptive educational hypermedia systems are e-learning systems that employ artificial intelligence. They deliver personalised online learning interventions that extend electronic learning experiences beyond a mere computerised book through the use of intelligence that adapts the content presented to a user according to a range of factors including individual needs, learning styles and existing knowledge. The purpose of this paper is to describe a novel adaptive e-learning system called dyslexia adaptive e-learning management system (DAELMS). For the purpose of this paper, the term DAELMS will be employed to describe the overall e-learning system that incorporates the required functionality to adapt to students’ learning styles and dyslexia type. Design/methodology/approach: The DAELMS is a complex system that will require a significant amount of time and expertise in knowledge engineering and formatting (i.e. dyslexia type, learning styles, domain knowledge) to develop. One of the most effective methods of approaching this complex task is to formalise the development of a DAELMS that can be applied to different learning styles models and education domains. Four distinct phases of development are proposed for creating the DAELMS. In this paper, we will discuss Phase 3 which is the implementation and some adaption algorithms while in future papers will discuss the other phases. Findings: An experimental study was conducted to validate the proposed generic methodology and the architecture of the DAELMS. The system has been evaluated by group of university students studying a Computer Science related majors. The evaluation results proves that when the system provide the user with learning materials matches their learning style or dyslexia type it enhances their learning outcomes. Originality/value: The DAELMS correlates each given dyslexia type with its associated preferred learning style and subsequently adapts the learning material presented to the student. The DAELMS represents an adaptive e-learning system that incorporates several personalisation options including navigation, structure of curriculum, presentation, guidance and assistive technologies that are designed to ensure the learning experience is directly aligned with the user's dyslexia type and associated preferred learning style.

(1) This study demonstrates how a Saudi university has responded to the COVID-19 lockdown in order to examine the success factors and highlight any challenges. The main purpose was to determine the perceptions of students and faculty towards emergency online distance learning from a teaching and learning perspective; (2) A cross-faculty study was conducted: two different self-administered questionnaires were developed for students and faculty, respectively. In addition, data was collected from official reports; (3) The results show that students had a more positive perception of e-Learning despite the difficulties that they may have faced, while faculty results leaned slightly towards a negative perception. However, there was not a definite positive or negative perception, depending on the aspect of teaching that was being evaluated. The study also indicated that faculty and students’ gender had no significant effect on their perceptions. Overall results showed that the university performed well in accordance with three of the five pillars of online learning quality framework in terms of student satisfaction, access and scalability. On the other, improvements are needed to achieve better results for faculty satisfaction and learning effectiveness; (4) The findings present a number of suggestions for increasing satisfaction to improve the online learning experience post COVID-19.

Mobile edge computing (MEC) is a paradigm novel computing that promises the dramatic effect of reduction in latency and consumption of energy by computation offloading intensive; these tasks to the edge clouds in proximity close to the smart mobile users. In this research, reduce the offloading and latency between the edge computing and multiusers under the environment IoT application in 5G using bald eagle search optimization algorithm. The deep learning approach may consume high computational complexity and more time. In an edge computing system, devices can offload their computation-intensive tasks to the edge servers to save energy and shorten their latency. The bald eagle algorithm (BES) is the advanced optimization algorithm that resembles the strategy of eagle hunting. The strategies are select, search, and swooping stages. Previously, the BES algorithm is used to consume the energy and distance; to improve the better energy and reduce the offloading latency in this research and some delays occur when devices increase causes demand for cloud data, it can be improved by offering ROS (resource) estimation. To enhance the BES algorithm that introduces the ROS estimation stage to select the better ROSs, an edge system, which offloads the most appropriate IoT subtasks to edge servers then the expected time of execution, got minimized. Based on multiuser offloading, we proposed a bald eagle search optimization algorithm that can effectively reduce the end-end time to get fast and near-optimal IoT devices. The latency is reduced from the cloud to the local; this can be overcome by using edge computing, and deep learning expects faster and better results from the network. This can be proposed by BES algorithm technique that is better than other conventional methods that are compared on results to minimize the offloading latency. Then, the simulation is done to show the efficiency and stability by reducing the offloading latency.

In this paper, advanced features of 6G networks by examining security of consumer electronic products are discussed. With rapid growth of consumer electronic products the network features are updated thus providing fast response to end users but security of transmission remains a major concern. Hence a collaborative framework is formulated in proposed method that solves all uncertainties in consumer electronic products if it is recommended to provide operation using 6G networks. The major significance of proposed method is to identify all problems that occurs in consumer electronic products that operated with advanced technological networks such as 6G and advanced 6G communications. Hence foremost importance is provided to identify all problems by using advanced artificial intelligence algorithm where electronic products can be identified by using natural language processors to convert machine language to identifiable ones thereby expert solutions are achieved. Here, a unique AI model known as Deep Adaptive Neuro Convoluted Chameleon Classifier (DANC3) is used for data classification, which aids in the identification and categorization of consumer data acquired from 6G networks.In each case, the objective functions are optimized with maximized security of data transmission for every consumer electronic product in 6G is reduced below 1%.

The World Wide Web comprises of huge web databases where the data are searched using web query interface. Generally, the World Wide Web maintains a set of databases to store several data records. The distinct data records are extracted by the web query interface as per the user requests. The information maintained in the web database is hidden and retrieves deep web content even in dynamic script pages. In recent days, a web page offers a huge amount of structured data and is in need of various web-related latest applications. The challenge lies in extracting complicated structured data from deep web pages. Deep web contents are generally accessed by the web queries, but extracting the structured data from the web database is a complex problem. Moreover, making use of such retrieved information in combined structures needs significant efforts. No further techniques are established to address the complexity in data extraction of deep web data from various web pages. Despite the fact that several ways for deep web data extraction are offered, very few research address template-related issues at the page level. For effective web data extraction with a large number of online pages, a unique representation of page generation using tree-based pattern matches (TBPM) is proposed. The performance of the proposed technique TBPM is compared to that of existing techniques in terms of relativity, precision, recall, and time consumption. The performance metrics such as high relativity is about 17-26% are achieved when compared to FiVaTech approach.