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Many research has been conducted to examine the acceptance factors to use mobile learning (m-learning) for regular students. During the COVID-19 most of the higher education institutions around the world were converted to m-learning especially for regular students, in order to continue supporting the educational stage for these students. This situation, allow researches to tested the use of m-learning for regular students while they are studying in distance learning environment. However, limited researches, especially in developing countries, have been tested the acceptance factors to use m-leaning for distance learning students. In this study the behavioral intention to use mobile learning (m-learning) were examined as well as the m-learning factors that affecting its acceptance amongst the distance learning students were outlined. The study framework was depended on the model of Unified Theory of Acceptance and Use of Technology (UTAUT). A quantitative approach was used to analyze the data that collected from a random sample of 154 male and female participants from Saudi universities. The results indicated that significant factors influencing distance learning students’ behavioral intention include quality of service, effort expectancy, facilitating conditions, gender, educational level, and type of device. The regulations governing distance learning programs and the implementation of mobile learning by Saudi universities under the direction of the Ministry of Higher Education are having a good impact and encouraging widespread use of m-learning.

Cloud computing is a massive amount of dynamic ad distributed resources that are delivered on request to clients over the Internet. Typical centralized cloud computing models may have difficulty dealing with challenges caused by IoT applications, such as network failure, latency, and capacity constraints. One of the introduced methods to solve these challenges is fog computing which makes the cloud closer to IoT devices. A system for dynamic congestion management brokerage is presented in this paper. With this proposed system, the IoT quality of service (QoS) requirements as defined by the service-level agreement (SLA) can be met as the massive amount of cloud requests come from the fog broker layer. In addition, a forwarding policy is introduced which helps the cloud service broker to select and forward the high-priority requests to the appropriate cloud resources from fog brokers and cloud users. This proposed idea is influenced by the weighted fair queuing (WFQ) Cisco queuing mechanism to simplify the management and control of the congestion that may possibly take place at the cloud service broker side. The system proposed in this paper is evaluated using iFogSim and CloudSim tools, and the results demonstrate that it improves IoT (QoS) compliance, while also avoiding cloud SLA violations.

As the number of video streaming platforms is growing, the risk factor associated with illegal and inappropriate content streaming is increasing exponentially. Therefore, mon- itoring such content is essential. Many researches have been conducted on classifying encrypted videos. However, most existing techniques only pass raw traffic data into clas- sification models, which is an ineffective way of training a model. This research proposes a bucket-based data pre-processing technique for a video identification in network traffic. The bucketed traffic is then incorporated with a fine-tuned word2vec-based neural net- work to produce an effective encrypted video classifier. Experiments are carried out with different numbers and sizes of buckets to determine the best configuration. Furthermore, previous research has overlooked the phenomenon of concept drift, which reduces the effec- tiveness of a model. This paper also compares the severity of concept drift on the proposed and previous technique. The results indicate that the model can predict new samples of videos with an overall accuracy of 81% even after 20 days of training.

In recent years, video identification within encrypted network traffic has gained popularity for many reasons. For example, a government may want to track what content is being watched by its citizens, or businesses may want to block certain content for productivity. Many such reasons advocate for the need to track users on the internet. However, with the introduction of the secure socket layer (SSL) and transport layer security (TLS), it has become difficult to analyze traffic. In addition, dynamic adaptive streaming over HTTP (DASH), which creates abnormalities due to the variable-bitrate (VBR) encoding, makes it difficult for researchers to identify videos in internet traffic. The default quality settings in browsers automatically adjust the quality of streaming videos depending on the network load. These auto-quality settings also increase the challenge in video detection. This paper presents a novel ensemble classifier, E-Ensemble, which overcomes the abnormalities in video identification in encrypted network traffic. To achieve this, three different classifiers are combined by using two different combinations of classifiers: the hard-level and soft-level combinations. To verify the performance of the proposed classifier, the classifiers were trained on a video dataset collected over one month and tested on a separate video dataset captured over 20 days at a different date and time. The soft-level combination of classifiers showed more stable results in handling abnormalities in the dataset than those of the hard-level combination. Furthermore, the soft-level classifier combination technique outperformed the hard-level combination with a high accuracy of 81.81%, even in the auto-quality mode.

The use and utilization of social media applications, tools, and services enables advanced services in daily routines, activities, and work environments. Nowadays, disconnection from social media services is a disadvantage due to their increasing use and functionality. The use of social media applications and services has provided different methods and routines for communications that ranges from posting, reposting, commenting, interacting, and live communication that can reach a mass population with minimum time, effort, and expenses compared with traditional media systems and channels. The current benefits of using social media can assist in providing better services in terms of communication and guidance for civil protection services within governmental sectors, as reported by different research studies. The use of social media has been found to be critically important by governmental agencies in different situations for directing, educating, and engaging people during different events. This study investigates the use of social media services in Saudi Arabia in governmental sectors to outline the opportunities and challenges faced, given the challenging situations faced annually during the Hajj and Ramadan rituals, and sporadic flood crisis events. This research focuses on defining the current stand and challenges of using social media services for providing mass communication and civil engagement during hazardous and challenging events in Saudi Arabia. The results of this study will be used as a roadmap for future investigation in this regard.