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In the current COVID-19 pandemic era, Learning Management Systems (LMS) are commonly used in e-learning for various learning activities in Higher Education. Learning Analytics (LA) is an emerging area of LMS, which plays a vital role in tracking and storing learners’ activities in the online environment in Higher Education. LA treats the collections of students’ digital footprints and evaluates this data to improve teaching and learning quality. LA measures the analysis and reports learners’ data and their activities to predict decisions on every tier of the education system. This promising area, which both teachers and students can use during this pandemic outbreak, converges LA, Artificial Intelligence, and Human-Centered Design in data visualization techniques, semantic and educational data mining techniques, feature data extraction, etc. Different learning activities of learners for each course are analyzed with the help of LA plug-ins. The progression of learners can be monitored and predicted with the help of this intelligent analysis, which aids in improving the academic progress of each learner in a secured manner. The Object-Oriented Programming course and Data Communication Network are used to implement our case studies and to collect the analysis reports. Two plug-ins, local and log store plug-ins, are added to the sample course, and reports are observed. This research collected and monitored the data of the activities each students are involved in. This analysis provides the distribution of access to contents from which the number of active students and students’ activities can be inferred. This analysis provides insight into how many assignment submissions and quiz submissions were on time. The hits distribution is also provided in the analytical chart. Our findings show that teaching methods can be improved based on these inferences as it reflects the students’ learning preferences, especially during this COVID-19 era. Furthermore, each student’s academic progression can be marked and planned in the department.

Due to mandatory social distancing brought about by COVID-19 and observed inefficiency of the current barcodebased payment systems, there is a pressing need to alleviate the current condition of overcrowding of the cashier area in shops and supermarkets. This study proposed a model that primarily aims to shorten queuing time in scanning individual items. With the use of a mobile application, shoppers can obtain product information such as prices, discounts, expiration date, nutrition facts, etc. through barcodes or QR codes. These items are placed in a virtual cart and view the invoice prior to payment. This information is sent to the cashier and identified by a transaction ID. The cashier scans the transaction ID and deactivates all the RFID associated with the products purchased. In case of intentional or unintentional carrying out of unscanned products, the RFID kiosk prompts the shopper either to pay or to return them.

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Medical patient data need to be published and made available to researchers so that they can use, analyse, and evaluate the data effectively. However, publishing medical patient data raises privacy concerns regarding protecting sensitive data while preserving the utility of the released data. The privacypreserving data publishing (PPDP) process attempts to keep public data useful without risking the medical patients' privacy. Through protection methods like perturbing, suppressing, or generalizing values, which lead to uncertainty in identity inference or sensitive value estimation, the PPDP aims to reduce the risks of patient data being disclosed and to preserve the potential use of published data. Although this method is helpful, information loss is inevitable when attempting to achieve a high level of privacy using protection methods. In addition, the privacy-preserving techniques may affect the use of data, resulting in imprecise or even impractical knowledge extraction. Thus, balancing privacy and utility in medical patient data is essential. This study proposed an innovative technique that used a hybrid protection method for utility enhancement while preserving medical patients' data privacy. The utilized technique could partition information horizontally and vertically, resulting in data being grouped into columns and equivalence classes. Then, the attributes assumed to be easily known by any attacker are determined by upper and lower protection levels (U P L and LP L). This work also depends on making the false matches and value swapping to make sure that the attribute disclosure is less likely to happen. The innovative technique makes data more useful. According to the results, the innovative technique delivers about 93.4% data utility when the percentage of exchange level is 5% using LP L and 95% using U P L with a 4.5K medical patient dataset. In conclusion, the innovative technique has minimized risk disclosure compared to other existing works.