Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "Ashour, A. A." wg kryterium: Autor

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-3 z 3
    Tytuł:
    Effect of different balance training programs on postural control in chronic ankle instability: a randomized controlled trial
    Autorzy:
    Youssef, N. M.
    Abdelmohsen, A. M.
    Ashour, A. A.
    Elhafez, N. M.
    Elhafez, S. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/307467.pdf
    Data publikacji:
    2018
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    trening równowagi
    przewlekła niestabilność kostki
    kontrola postawy ciała
    balance training
    postural control
    chronic ankle instability
    Opis:
    Chronic ankle instability (CAI) is a common sequence of ankle sprain. Conservative treatment of CAI is currently accepted as a primary mean of managing the condition and preventing of recurrence. Objectives: The purpose of the current study was to evaluate the effect of Weight-bearing Exercise for Better Balance (WEBB) program and unilateral balance training exercises on postural control in females with CAI. Methods: Thirty-five females with CAI participated in this study. The participants’ mean values of age, body mass, height, and body mass index were 21.76 ± 1.96 y, 64.82 ± 9.37 kg, 161.85 ± 7.24 cm, and 24.76 ± 3.76 kg/m², respectively. They were randomly assigned into three groups: experimental group A (n = 13) who received WEBB program, experimental group B (n = 12) who received unilateral balance training, and control group C (n = 10) using statistical random tables. Balance parameters were measured for all participants via Biodex Balance System (BBS) before and after four weeks of balance training. They included overall stability index (OASI), antero-posterior stability index (APSI), and medio-lateral stability index (MLSI). Results: There was a significant improvement in the post testing mean values of OASI and APSI, compared with the pre testing values in both experimental groups (A and B). There was no significant difference between the pre and post testing mean values of MLSI in the experimental group (A). Conclusions: Both WEBB program and unilateral balance training have positive effects on postural control in females with CAI.
    Źródło:
    Acta of Bioengineering and Biomechanics; 2018, 20, 2; 159-169
    1509-409X
    2450-6303
    Pojawia się w:
    Acta of Bioengineering and Biomechanics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Optimization of traveling salesman problem using affinity propagation clustering and genetic algorithm
    Autorzy:
    El-Samak, A. F.
    Ashour, W.
    Powiązania:
    https://bibliotekanauki.pl/articles/91810.pdf
    Data publikacji:
    2015
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    combinatorial optimization problem
    travel salesman problem
    genetic algorithm
    evolutionary computation algorithm
    affinity propagation clustering technique
    AP
    problem optymalizacji kombinatorycznej
    algorytm genetyczny
    obliczenia ewolucyjne
    Opis:
    Combinatorial optimization problems, such as travel salesman problem, are usually NPhard and the solution space of this problem is very large. Therefore the set of feasible solutions cannot be evaluated one by one. The simple genetic algorithm is one of the most used evolutionary computation algorithms, that give a good solution for TSP, however, it takes much computational time. In this paper, Affinity Propagation Clustering Technique (AP) is used to optimize the performance of the Genetic Algorithm (GA) for solving TSP. The core idea, which is clustering cities into smaller clusters and solving each cluster using GA separately, thus the access to the optimal solution will be in less computational time. Numerical experiments show that the proposed algorithm can give a good results for TSP problem more than the simple GA.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 4; 239-245
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Clustering large-scale data based on modified affinity propagation algorithm
    Autorzy:
    Serdah, A. M.
    Ashour, W. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/91694.pdf
    Data publikacji:
    2016
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    clustering
    clustering algorithm
    data clustering algorithm
    propagation algorithm
    Affinity Propagation
    AP
    klasteryzacja
    algorytm klastrowania
    algorytm propagacji
    Opis:
    Traditional clustering algorithms are no longer suitable for use in data mining applications that make use of large-scale data. There have been many large-scale data clustering algorithms proposed in recent years, but most of them do not achieve clustering with high quality. Despite that Affinity Propagation (AP) is effective and accurate in normal data clustering, but it is not effective for large-scale data. This paper proposes two methods for large-scale data clustering that depend on a modified version of AP algorithm. The proposed methods are set to ensure both low time complexity and good accuracy of the clustering method. Firstly, a data set is divided into several subsets using one of two methods random fragmentation or K-means. Secondly, subsets are clustered into K clusters using K-Affinity Propagation (KAP) algorithm to select local cluster exemplars in each subset. Thirdly, the inverse weighted clustering algorithm is performed on all local cluster exemplars to select well-suited global exemplars of the whole data set. Finally, all the data points are clustered by the similarity between all global exemplars and each data point. Results show that the proposed clustering method can significantly reduce the clustering time and produce better clustering result in a way that is more effective and accurate than AP, KAP, and HAP algorithms.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 1; 23-33
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies