This study compares the effectiveness of e-learning and face-to-face education in the previously neglected context of Saudi Arabia. This is done by examining Najran University’s e-learning experience after the institution suspended traditional course delivery due to the ongoing war between Saudi Arabia, the Arab Coalition, and Yemeni rebel groups. The analysis also considers the potential benefits offered by e-learning in crisis zones such as the southern border region of Najran, Saudi Arabia. The results indicate that there is no statistical or practical difference between online and face-to-face learning with respect to student performance. The study also demonstrated that e-learning is capable of delivering the educational goals of higher learning institutions to areas wrecked by wars. E-Learning offers students a safe learning environment, engaging platforms, and most importantly, a quality education. The findings of this study contribute to a growing body of scholarship on the effectiveness and implementation of e-learning in the Middle East.

This research proposes a generic smart cloud-based system in order to accommodate multiple scenarios where agriculture farms using Internet of Things (IoTs) need to be monitored remotely. The real-time and stored data are analyzed by specialists and farmers. The cloud acts as a central digital data store where information is collected from diverse sources in huge volumes and variety, such as audio, video, image, text, and digital maps. Artificial Intelligence (AI) based machine learning models such as Support Vector Machine (SVM), which is one of many classification types, are used to accurately classify the data. The classified data are assigned to the virtual machines where these data are processed and finally available to the end-users via underlying datacenters. This processed form of digital information is then used by the farmers to improve their farming skills and to update them as pre-disaster recovery for smart agri-food. Furthermore, it will provide general and specific information about international markets relating to their crops. This proposed system discovers the feasibility of the developed digital agri-farm using IoT-based cloud and provides solutions to problems. Overall, the approach works well and achieved performance efficiency in terms of execution time by 14%, throughput time by 5%, overhead time by 9%, and energy efficiency by 13.2% in the presence of competing smart farming baselines.

Blockchain is a peer-to-peer trustless network that keeps records of digital assets without any central authority. With the passage of time, the sustainability issue of blockchain is rising. This paper discusses two major sustainability issues of blockchain: power consumption and scalability. It discusses the challenge of power consumption by analyzing various approaches to estimating power consumption in the literature. A case study of bitcoin is presented for this purpose. The study presents a review of the growing energy consumption of bitcoin along with a solution for immersion cooling in blockchain mining. The second challenge addressed in this research is scalability. With the increase in network size, scalability issues are also increasing as the number of transactions per second is decreasing. In other words, blockchain is observing low throughput with its increase in size. The paper discusses research studies and techniques proposed in the literature. The paper then investigates how to scale blockchain for better performance.

The amount of data captured is expanding day by day which leads to the need for a monitoring system that helps in decision making. Current technologies such as cloud, machine learning (ML) and Internet of Things (IoT) provide a better solution for monitoring automation systems efficiently. In this paper, a prediction model that monitors real-time data of sensor nodes in a clinical environment using a machine learning algorithm is proposed. An IoT-based smart hospital environment has been developed that controls and monitors appliances over the Internet using different sensors such as current sensors, a temperature and humidity sensor, air quality sensor, ultrasonic sensor and flame sensor. The IoT-generated sensor data have three important characteristics, namely, real-time, structured and enormous amount. The main purpose of this research is to predict early faults in an IoT environment in order to ensure the integrity, accuracy, reliability and fidelity of IoT-enabled devices. The proposed fault prediction model was evaluated via decision tree, K-nearest neighbor, Gaussian naive Bayes and random forest techniques, but random forest showed the best accuracy over others on the provided dataset. The results proved that the ML techniques applied over IoT-based sensors are well efficient to monitor this hospital automation process, and random forest was considered the best with the highest accuracy of 94.25%. The proposed model could be helpful for the user to make a decision regarding the recommended solution and control unanticipated losses generated due to faults during the automation process.

This paper deals with the challenging task of computing accurate contours from CT and MRI scans using B-spline curve approximation. To date bio-modeling and visualization have been performed primarily on voxel and facet (triangle) based models. On the other hand, traditional CAD has reached a level of sophistication where just about any object can be designed, prototyped and manufactured using well-refined CAD modeling and manufacturing tools. NURBS, the de facto standard to represent geometry in CAD systems, have been the building blocks of CAD modeling and will be used in this paper to perform a critical task for bio-fabrication of human components. Although at first glance it may seem that contour fitting is a trivial task, the details presented in this paper reveal that traditional techniques are not readily adaptable to medical data and several fundamental algorithms had to be completely rewritten to account for the characteristics of human organs. The emphasis of this research is placed on accuracy and shape fidelity (precise surgical operation), speed (real-time simulation), smoothness (the discrete volumetric or faceted data should be replaced by smooth curves), and data reduction (the large amount of image data must be reduced by at least 60–80%).