Upsurge of IoT (Internet of Things) in engineering education: A case study

2019 ◽

Author(s):

Alexandra Davidson ◽

Lisa Romkey ◽

Allison Van Beek

Keyword(s):

Engineering Education ◽

Online Discussion ◽

Discussion Board ◽

Online Discussions ◽

Asynchronous Learning ◽

Discussion Boards ◽

Graduate Course ◽

Post Secondary ◽

Online Discussion Board

Due to the increasing prevalence of asynchronous learning platforms, the development and implementation of online discussion boards have become important considerations in the design of post-secondary learning environments. This research is conducted as a case study of the online discussion board use in a small engineering education graduate course, consisting of in-class and online discussion components. By varying the structure of the online discussion board to allow different types of student interaction, the study identifies trends in discussion board use, specifically pertaining to student participation, student collaboration, and the integration between in-class and online discussions. As a result, the study provides insight into the utility and limitations of online discussion boards in post-secondary courses.

Download Full-text

Convergence of Artificial Intelligence and Internet of Things in Smart Healthcare: A Case Study of Voice Pathology Detection

IEEE Access ◽

10.1109/access.2021.3090317 ◽

2021 ◽

pp. 1-1

Author(s):

Ghulam Muhammad ◽

Musaed Alhussein

Keyword(s):

Artificial Intelligence ◽

Internet Of Things ◽

Smart Healthcare ◽

Voice Pathology Detection

Download Full-text

Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

Electronics ◽

10.3390/electronics10141615 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1615

Author(s):

Zeeshan Ali Khan ◽

Ubaid Abbasi ◽

Sung Won Kim

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Healthcare Services ◽

Healthcare Sector ◽

K Nearest Neighbors ◽

Iot Applications ◽

Processing Resources ◽

Multiple Services ◽

Applications Of Machine Learning

Low power wide area networks (LPWAN) are comprised of small devices having restricted processing resources and limited energy budget. These devices are connected with each other using communication protocols. Considering their available resources, these devices can be used in a number of different Internet of Things (IoT) applications. Another interesting paradigm is machine learning, which can also be integrated with LPWAN technology to embed intelligence into these IoT applications. These machine learning-based applications combine intelligence with LPWAN and prove to be a useful tool. One such IoT application is in the medical field, where they can be used to provide multiple services. In the scenario of the COVID-19 pandemic, the importance of LPWAN-based medical services has gained particular attention. This article describes various COVID-19-related healthcare services, using the the applications of machine learning and LPWAN in improving the medical domain during the current COVID-19 pandemic. We validate our idea with the help of a case study that describes a way to reduce the spread of any pandemic using LPWAN technology and machine learning. The case study compares k-Nearest Neighbors (KNN) and trust-based algorithms for mitigating the flow of virus spread. The simulation results show the effectiveness of KNN for curtailing the COVID-19 spread.

Download Full-text