During the Coronavirus COVID19 crisis, most of the affected countries forced the stay-home policy, which causes workers from many sectors to continue their work from home. Schools, colleges, and universities are conducting teaching activities through the internet. The online education used for many years to support the regular education. However, in this crisis, educational institutions are switched into 100% online teaching. There are many challenges associated with this transition, which makes us rethink about prioritizing education spending and put online education upfront. Also, it is time to rewrite the risk policies and consider biological threats as one of the possible risks that obstruct regular education. In this study, we highlighted the experience of a mid-size college in Saudi Arabia with this circumstance. We evaluated this experience from its beginning to the end of the semester. We summarized many lessons learned from this experience how to mitigate the effect associated with the crisis.

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The emergence of smart and innovative applications in diverse domains has inspired our lives by presenting many state-of-the art applications ranging from offline to smart online systems, smart communication system to tracking systems, and many others. The availability of smart internet enabled systems has made the world as a global village where people can collaborate, communicate, and share information in secure and timely manner. Innovation in information technology focuses on investigating characteristics that make it easier for the people to accept and distribute innovative IT-based processes or products. To provide elastic services and resource the Internet service provider developed cloud computing to support maximal number of users. Cloud computing is a subscription paradigm in which users do not buy various resources permanently, but they purchase it with block chain-driven payment schemes (credit cards). A flexible, on-demand, and dynamically scalable computer infrastructure is offered by cloud providers to its clients on charging some amount of subscription. This research article provides an introduction of cloud computing and the integration of IoT concept, its impacts on crowd and organizations, provision of various services, and analyzing and selecting the appropriate features using probability distribution function for enhancing cloud-based IoT capabilities. In ambiguous and complex situations, decision makers use quantitative techniques combined with traditional approaches to select the appropriate one among a group of features. Probability distribution function is used to evaluate the appropriate features that will enhance the capabilities of cloud-based IoT application.

At the beginning of the testing phase and before the deployment phase of a project's development cycle, we need to predict files with a high chance of change. Software products are always prone to change due to several reasons, including fixing errors or improvements. In this work, we used the Eclipse (releases from 2.0 to 3.5) to investigate how prediction models can perform when learning from a release and predicting in the subsequent one, which contains new files that models have not seen. We compared the performance of these models with models that are trained and tested on the same release. We found no differences between predicting the same release or subsequent release on two pre Europa releases. Predicting change in newly created files helps improve maintenance planning for software project managers and reduce cost. It will also help to enhance the quality of software by improving the practices of developers.This study used the Adaptive Boost classifier with the decision tree J48 algorithm and combined it with the re-sampling method. We find this to be better than using a meta classifier alone or combine the re-sampling with the standard classification. We compared our results with related works and found that our results are outperforming.

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