Online E-learning Mobile Application for Self-learning

This research work is focused on the accessible Mobile user application developments to facilitate student and faculty communication through native android applications. Covid'19 and this pandemic brings E-learning systems as majority education levels. Mobile technology has efficient learning systems in many countries like the United States where the students use google paths such as the classroom to extend learning effectively. Limitations in existing apps are that students are not appreciated and monitored for self learning, schedule sharing is only at the end of the course and also knowing the mapping concepts of teaching pedagogy is also less approachable. To overcome these problems mobile technology support is proposed in this work with these three modules such as i) Authentication self learning and performance (ASLP) - Authentication for right users along with improvement of monitoring self learning to analyze performance ii) Syllable Schedule (SS) - prior scheduling on syllable and organization of time table matching based on outcome iii) Authorized facilitator (AF) - Set permission based on designation such that facilitator communicates based on needs. To achieve the above highlighted terms Google API is applied by peer reviews and interactions enabled such that efficient mobile applications development is proved. Also the ion hierarchy is improved when setting the interaction module implies less complexity, less storage. Thus the scope of research work is to use classroom overall performance, interaction, development process to upgrade the results of students. Education level is also enhanced through online E-learning mobile technology (OELMT) that has native applications to develop students' knowledge in a better way. © 2022 IEEE.

Framework for detecting the patients affected by COVID-19 at early stages using Internet of Things along with Machine Learning approaches with improved Accuracy

COVID-19 has been affecting the entire world from the year 2019 and in order to handle this pandemic situation, it is necessary to follow the measures till valid medicine has been found. The proposed approach helps in detecting as well as monitoring the COVID-19 on real time basis. Data is collected with the help of IoT devices for detecting this disease at the early stage. The components of the system are, (i) Collection of symptom data, (ii) Center of Isolation, (iii) Machine Learning approaches for analysis, (iv) Healthcare analysts and (v) Cloud. The three algorithms in machine learning used for detection of the virus are Decision tree, Support vector machine and Neural Network. These three algorithms are tested with the real time dataset and it is observed that all these algorithms have accuracy greater than 91%. Identifying the disease accurately with the three machine learning algorithms, effective results are produced. The response of treatment for every person who gets affected by the virus is then documented. © 2022 IEEE.