Internet of Things (IoT) is a computing concept facilitating the management of collaborative activities from one central area. Millennial learners, growth in enrolment numbers in universities, and the need for equity and quality learning necessitate the use of IoT technologies in education. The focus of this paper is to examine IoT implementations in learning institutes, their application areas, the themes presented, the models and methodologies used, and the benefits. This study concentrated on publications from 2008 to 2017. The outcomes revealed that the utilization of IoT for tracking and tracing a learner’s attendance had been one of the application areas of IoT in education. This study further categorized the papers and presents novel research opportunities based on concentrated themes and areas that had not been fully exhausted. Most research studies employed qualitative methods, with a few utilizing a quantitative approach with surveys. Research themes exhibited a shortcoming in other important themes, such as the models and methodologies used for implementing IoT. Finally, the results of this study agree that IoT implementation could help solve some issues in learning institutions like equity and quality learning. The results from this research also provide a base for future research works on the successful implementation of IoT in learning institutions.
Particle swarm optimization (PSO) is a high-quality, nature-inspired global optimization algorithm that can be applied to a variety of real-world optimization problems. PSO, on the other hand, has some flaws, such as slow convergence, premature convergence, and the ability to become stuck at local optimum solutions. This research aims to address the issue of population diversity in the PSO search process, which leads to premature convergence. As a result, in this study, a method is introduced to PSO in order to avoid early stagnation, which leads to premature convergence. A chaotic dynamic weight particle swarm optimization (CTPSOA) is proposed, in which a chaotic logistic map is delivered to increase the population range within the PSO search technique by editing the inertia weight of PSO to avoid premature convergence. This study also looks into the overall performance and viability of the proposed CTPSOA as a set of function selection rules for solving optimization issues. There are eight traditional benchmark functions that are used to assess the overall results and obtain the accuracy of the proposed (CTPSOA) algorithms when compared to a few other meta-heuristics optimization rules. The test results reveal that the CTPSOA outperforms other meta-heuristics algorithms in solving optimization problems by 85% and has established itself as a reliable and superior metaheuristics algorithm for feature selection.
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