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Browsing by Subject "optimization algorithm"

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    Item type:Article, Access status: Open Access ,
    Applying Hunger Game Search (HGS) for selecting significant blood indicators for early prediction of ICU COVID-19 severity
    (Wydawnictwa AGH, 2023) Sayed, Safynaz AbdEl-Fattah; ElKorany, Abeer; Sayed, Sabah
    This paper introduces an early prognostic model for attempting to predict the severity of patients for ICU admission and detect the most significant features that affect the prediction process using clinical blood data. The proposed model predicts ICU admission for high-severity patients during the first two hours of hospital admission, which would help assist clinicians in decision-making and enable the efficient use of hospital resources. The Hunger Game search (HGS) meta-heuristic algorithm and a support vector machine (SVM) have been integrated to build the proposed prediction model. Furthermore, these have been used for selecting the most informative features from blood test data. Experiments have shown that using HGS for selecting features with the SVM classifier achieved excellent results as compared with four other meta-heuristic algorithms. The model that used the features that were selected by the HGS algorithm accomplished the topmost results (98.6 and 96.5%) for the best and mean accuracy, respectively, as compared to using all of the features that were selected by other popular optimization algorithms.
  • thumbnail.journal.alt
    Item type:Thesis, Access status: Restricted ,
    Implementacja metody roju cząstek w OpenCL
    (Data obrony: 2014-12-11) Iwaniak, Michał
    Wydział Geologii, Geofizyki i Ochrony Środowiska
    Presented thesis describes Particle Swarm Optimization algorithm, a type of a heuristic algorithm used for many optimization problems. The objective of this paper was to implement PSO utilizing computing power of graphics processing units (GPU) emphasizing research of the impact of selected configuration parameters. Cross-platform availability was the main reason why OpenCL was chosen as the framework in which the algorithm was implemented. Main bottlenecks of GPU parallel implementation were high global memory access time and high execution time of random number generator functions. The former was eliminated by removal of the critical section from the algorithm, while the latter by implementing own RNG algorithm, that executed directly on the kernel.