Predicting the severity of COVID-19 patients using a multi-threaded evolutionary feature selection algorithm

The COVID-19 pandemic has huge effects on the global community and an extreme burden on health systems. There are more than 185 million confirmed cases and 4 million deaths as of July 2021. Besides, the exponential rise in COVID-19 cases requires a quick prediction of the patients' severity for better treatment. In this study, we propose a Multi-threaded Genetic feature selection algorithm combined with Extreme Learning Machines (MG-ELM) to predict the severity level of the COVID-19 patients. We conduct a set of experiments on a recently published real-world dataset. We reprocess the dataset via feature construction to improve the learning performance of the algorithm. Upon comprehensive experiments, we report the most impactful features and symptoms for predicting the patients' severity level. Moreover, we investigate the effects of multi-threaded implementation with statistical analysis. In order to verify the efficiency of MG-ELM, we compare our results with traditional and state-of-the-art techniques. The proposed algorithm outperforms other algorithms in terms of prediction accuracy

Improving The Performance Of Hadoop Hive By Sharing Scan And Computation Tasks

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A Novel Grouping Genetic Algorithm for the One-Dimensional Bin Packing Problem on GPU

One-dimensional Bin Packing Problem (1D-BPP) is a challenging NP-Hard combinatorial problem which is used to pack finite number of items into minimum number of bins. Large problem instances of the 1D-BPP cannot be solved exactly due to the intractable nature of the problem. In this study, we propose an efficient Grouping Genetic Algorithm (GGA) by harnessing the power of the Graphics Processing Unit (GPU) using CUDA. The time consuming crossover and mutation processes of the GGA are executed on the GPU by increasing the evaluation times significantly. The obtained experimental results on 1,238 benchmark 1D-BPP instances show that our proposed algorithm has a high performance and is a scalable algorithm with its high speed fitness evaluation ability. Our proposed algorithm can be considered as one of the best performing algorithms with its 66 times faster computation speed that enables to explore the search space more effectively than any of its counterparts

Evolutionary Multiobjective Query Workload Optimization of Cloud Data Warehouses

With the advent of Cloud databases, query optimizers need to find paretooptimal solutions in terms of response time and monetary cost. Our novel approach minimizes both objectives by deploying alternative virtual resources and query plans making use of the virtual resource elasticity of the Cloud. We propose an exact multiobjective branch-and-bound and a robust multiobjective genetic algorithm for the optimization of distributed data warehouse query workloads on the Cloud. In order to investigate the effectiveness of our approach, we incorporate the devised algorithms into a prototype system. Finally, through several experiments that we have conducted with different workloads and virtual resource configurations, we conclude remarkable findings of alternative deployments as well as the advantages and disadvantages of the multiobjective algorithms we propose

A study of bi-space search for solving the one-dimensional bin packing problem

Traditionally search techniques explore a single space to solve the problem at hand. This paper investigates performing search across more than one space which we refer to as bi-space search. As a proof of concept we illustrate this using the solution and heuristic spaces. In previous work two approaches for combining search across the heuristic and solution spaces have been studied. The first approach, the sequential approach, firstly explores the heuristic space to obtain complete solutions and then applies local search to explore the solution space created by these solutions. The second approach, the interleaving approach, alternates search in the heuristic and solution space on partial solutions until complete solutions are produced. This paper provides an alternative to these two approaches, namely, the concurrent approach, which searches the heuristic and solution spaces simultaneously. This is achieved by implementing a genetic algorithm selection hyper-heuristic that evolves a combination of low-level construction heuristics and local search move operators that explore the space of solutions (both partial and complete). The performance of the three approaches are compared, to one another as well as with a standard selection construction hyper-heuristic, using the one dimensional bin packing problem. The study revealed that the concurrent approach is more effective than the other two approaches, with the interleaving approach outperforming the sequential approach. All 3 approaches outperformed the standard hyper-heuristic. Given the potential of searching more than one space and the effectiveness of the concurrent approach, future work will examine additional spaces such as the design space and the program space, as well as extending the bi-space search to a multi-space search.The Multichoice Research Chair in Machine Learning at the University of Pretoria, South Africa.http://link.springer.combookseries/558hj2021Computer Scienc