Comparing Parallel Surrogate-Based and Surrogate-Free Multi-objective Optimization of COVID-19 Vaccines Allocation

The simulation-based and computationally expensive problem tackled in this paper addresses COVID-19 vaccines allocation in Malaysia. The multi-objective formulation considers simultaneously the total number of deaths, peak hospital occupancy and relaxation of mobility restrictions. Evolutionary algorithms have proven their capability to handle multi-to-many objectives but require a high number of computationally expensive simulations. The available techniques to raise the challenge rely on the joint use of surrogate-assisted optimization and parallel computing to deal with computational expensiveness. On the one hand, the simulation software is imitated by a cheap-to-evaluate surrogate model. On the other hand, multiple candidates are simultaneously assessed via multiple processing cores. In this study, we compare the performance of recently proposed surrogate-free and surrogate-based parallel multi-objective algorithms through the application to the COVID-19 vaccine distribution problem. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Teaching High-Performance Computing in Developing Countries: A Case Study in Mexican Universities

Teaching High-Performance Computing (HPC) to undergraduate programs represents a significant challenge in most universities in developing countries like Mexico. Deficien-cies in the required infrastructure and equipment, inadequate curricula in computer engineering programs (and resistance to change them), students' lack of interest, motivation, or knowledge of this area are the main difficulties to overcome. The COVID-19 pandemic represents an additional challenge to these difficulties in teaching HPC in these programs. Despite the detriments, some strategies have been developed to incorporate the HPC concepts to Mexican students without necessarily modifying the traditional curricula. This paper presents a case study over four public universities in Mexico based on our experience as instructors. We also propose a course that introduces the HPC principles considering the heterogeneous background of the students in such universities. The results are about the number of students enrolling in related classes and participating in extra-curricular projects. © 2022 IEEE.

Usage of ML and IoT in Healthcare Diagnose During Pandemic

The pandemic crisis has obliterated human existence as we know it, as well as regional, social, and commercial action, as well as compelled human civilization in living inside the defined perimeter. Uses of IoT with ML in health care applications is described in this article. The created ML with IoT dependent observation prototype assists for tracing COVID-19 positive detected persons using prior information and isolates them from non-infected individuals. By anticipating as well as analyzing information with AI, proposed ML-IoT system employs parallel computing to track pandemic sickness and also to avoid pandemic disease. The use of machine learning-dependent IoT for COVID in health conditions diagnose likely to be demonstrated the effectiveness for detection and prevention of CORONAVIRUS transmission. It still effects in better way on lowering preventive expenditures also leds to better treatment for infected individuals. In terms of monitoring and tracking, the recommended technique is 95% accurate. The findings will aid for stopping the pandemic's spread and providing assistance to the healthcare sector. © 2022 IEEE.