When the world was engulfed by a COVID-19 pandemic crisis, various activities could not be carried out normally. Activities may continue from home during a crisis by the use of a smartphone through the internet, because almost all people have their own smartphone, without requiring additional purchase of hardware. For formal learning, people can take the opportunity to use social media platforms to undertake conversations easily by using smart phones, even tablets. Teenagers or adults who are still studying in higher education institutions can undertake continuous learning for their work assignments. For example, they can use these tools to communicate through social media such as the WhatsApp, Telegram, Zoom, Microsoft Team, and Edmodo apps to connect with friends and lecturers. This research reveals trends of digital social media security and usability in the time of the COVID-19 pandemic situation, such as working from home, establishing a start-up, improving business processes and conducting online business within the digital ecosystem. The result is interesting and there are many uses of social media which have been addressed in this study during the COVID-19 pandemic for business purposes. The various social media platforms have different features that are available for use by subscribers, and also make it easier for people to do business. This is mainly due to the fact that they open up the global market and also make it cheaper to advertise. The government as well as the private sector has been in the forefront when it comes to the use of social media. Maintaining a good online presence is one of the key aspects that determine the success of start-up companies. This is due to the fact that most customers usually rely on the customer reviews in determining the ability of a company to meet the needs of clients. The main reason why most companies set up a customer relations department that is mandated with the responsibility of responding to customer feedback on various online platforms. On the other hand, the increased use of social media has brought new challenges when it comes to the security of information. Users must, therefore, secure their servers and technology from external and internal threats. One of the strategies used is the use of passwords to log into a portal where each person authorized to access the portal is provided with a password that is unique and known only by the user. The study has covered all these areas in detail including the use of database management systems in an organization or individual.

The COVID19 pandemic has indirectly changed the landscape of the business environment system through cloud intelligence within the digital ecosystem that has as a goal increasing the access, efficiency, effectiveness, equity and quality of business processes through cloud intelligent systems. Cloud intelligent systems are becoming revolutionary in today’s world pandemic causing a complete and drastic change to a variety of industries, including, security, transportation, business, logistics and manufacturing. The main purpose of cloud intelligence systems is to facilitate the ease of access from any location and the management of practical computing resources. One of the challenges faced by cloud technology today is scheduling. The role of scheduling algorithms is very important, since tasks are executed by orders that may need more attention. Here, scheduling algorithms intended to minimize monetary cost and minimize makespan time to execute the workflow are presented. This study proposes cloud intelligent systems apps through an approach to cloud computing scheduling that may lead to great benefits and efficiency. The result is very promising. It showed that there are numerous applications of intelligent systems due to the more advanced hardware being built nowadays, plus business processes advancing to become smarter and more efficient in growing profitably over a destructive digital ecosystem during the COVID19 pandemic. The results indicate that intelligent systems over the cloud play a big role not just for interacting with the world helping businesses grow, but as well as in the advancement for a better tomorrow.

Contribution: This study applies supervised and unsupervised machine learning (ML) techniques to discover which significant attributes that a successful learner often demonstrated in a computer course. Background: Students often experienced difficulties in learning an introduction to computers course. This research attempts to investigate how successful students regulate their learnings in this course. The answer to these questions will provide teachers with useful information to better comprehend how students learn and which strategies are effective in learning. Research Questions: 1) Which algorithm in supervised learning is the best one for predicting students' final performance? and 2) What attributes are key to succeed in this course? Methodology: Seven supervised ML algorithms and ensembles are conducted to compare the performance of classifiers regarding the levels of accuracy, precision, and sensitivity. The association rule and clustering are also employed to discover the key attributes for successful students. Because the present study used a convenience sample for data analysis, the number of students in each cluster was a potential limitation. Findings: The results show that Naïve Bayes is the most appropriate one for predicting students' final performance. The measure in accuracy and sensitivity of this classifier achieves 83.26% and 92.88%, respectively. In addition, the association rule indicates that ``to make sure keep up with the weekly progress in the class'' and self-efficacy beliefs play important roles on final performances for learners. The clustering findings reveal similar results.

Smart production as one of the key issues for the world to advance toward Industry 4.0 has been a research focus in recent years. In a smart factory, hundreds or even thousands of sensors and smart devices are often deployed to enhance product quality. Generally, sensor data provides abundant information for artificial intelligence (AI) engines to make decisions for these smart devices to collect more data or activate some required activities. However, this also consumes a lot of energy to transmit the sensor data via networks and store them in data centers. Data compression is a common approach to reduce the sensor data size so as to lower transmission energies. Literature indicates that many Bounded-Error Piecewise Linear Approximation (BEPLA) methods have been proposed to achieve this. Given an error bound, they make efforts on how to approximate to the original sensor data with fewer line segments. In this paper, we furthermore consider resolution reduction, which sets a new restriction on the position of line segment endpoints. Swing-RR (Resolution Reduction) is then proposed. It has O(1) complexity in both space and time per data record. In other words, Swing-RR is suitable for compressing sensor data, particularly when the volume of the data is huge. Our experimental results on real world datasets show that the size of compressed data is significantly reduced. The energy consumed follows. When using minimal resolution, Swing-RR has achieved the best compression ratios for all tested datasets. Consequently, fewer bits are transmitted through networks and less disk space is required to store the data in data centers, thus consuming less data transmission and storage power.