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  1. Artikel: Analysis of Linear Scaling Method in Downscaling Precipitation and Temperature

    Azman, Azreen Harina / Tukimat, Nurul Nadrah Aqilah / Malek, M. A.

    Water resources management. 2022 Jan., v. 36, no. 1

    2022  

    Abstract: Climate change is one of the greatest challenges in the 21ˢᵗ century that may influence the long haul and the momentary changeability of water resources. The vacillations of precipitation and temperature will influence the runoff and water accessibility ... ...

    Abstract Climate change is one of the greatest challenges in the 21ˢᵗ century that may influence the long haul and the momentary changeability of water resources. The vacillations of precipitation and temperature will influence the runoff and water accessibility where it tends to be a major issue when the interest for consumable water will increase. Statistical downscaling model (SDSM) was utilized in the weather parameters forecasting process in every 30 years range (2011-2040, 2041-2070, and 2071-2100) by considering Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5). The Linear Scaling (LS) method was carried out to treat the gaps between ground/ observed data and raw/ simulated results after SDSM. After the LS method was executed to raw/ simulated data after SDSM, the error decrease reaches over 13% for rainfall data. The Concordance Correlation Coefficient (CCC) value clarifies the correlation of rainfall amount among observed and corrected data for all three (3) RCPs categories. There are very enormous contrasts in rainfall amount during the wet season where CCC-values recorded are 0.22 and beneath (low correlation). The findings demonstrated that the rainfall amount during the dry season will contrast for all RCPs with the CCC-values are between 0.44-0.53 (moderate correlation). RCP8.5 is the pathway with the the most elevated ozone-depleting substance emanations and demonstrated that the climate change impact is going on and turn out to be more awful step by step.
    Schlagwörter administrative management ; climate change ; dry season ; meteorological data ; models ; rain ; runoff ; temperature ; wet season
    Sprache Englisch
    Erscheinungsverlauf 2022-01
    Umfang p. 171-179.
    Erscheinungsort Springer Netherlands
    Dokumenttyp Artikel
    ZDB-ID 59924-4
    ISSN 1573-1650 ; 0920-4741
    ISSN (online) 1573-1650
    ISSN 0920-4741
    DOI 10.1007/s11269-021-03020-0
    Datenquelle NAL Katalog (AGRICOLA)

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  2. Artikel ; Online: Wavelet packet entropy for heart murmurs classification.

    Safara, Fatemeh / Doraisamy, Shyamala / Azman, Azreen / Jantan, Azrul / Ranga, Sri

    Advances in bioinformatics

    2012  Band 2012, Seite(n) 327269

    Abstract: Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was ... ...

    Abstract Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.
    Sprache Englisch
    Erscheinungsdatum 2012-11-25
    Erscheinungsland Egypt
    Dokumenttyp Journal Article
    ZDB-ID 2448875-6
    ISSN 1687-8035 ; 1687-8035
    ISSN (online) 1687-8035
    ISSN 1687-8035
    DOI 10.1155/2012/327269
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel: Multi-level basis selection of wavelet packet decomposition tree for heart sound classification

    Safara, Fatemeh / Doraisamy, Shyamala / Azman, Azreen / Jantan, Azrul / Abdullah Ramaiah, Asri Ranga

    Computers in Biology and Medicine. 2013 Oct. 1, v. 43, no. 10

    2013  

    Abstract: Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve ... ...

    Abstract Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold. MLBS achieved an accuracy of 97.56% for classifying normal heart sound, aortic stenosis, mitral regurgitation, and aortic regurgitation. MLBS is a promising basis selection to be suggested for signals with a small range of frequencies.
    Schlagwörter energy ; heart ; heart sounds ; heart valve diseases ; wavelet
    Sprache Englisch
    Erscheinungsverlauf 2013-1001
    Umfang p. 1407-1414.
    Erscheinungsort Elsevier Ltd
    Dokumenttyp Artikel
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2013.06.016
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  4. Artikel ; Online: Multi-level basis selection of wavelet packet decomposition tree for heart sound classification.

    Safara, Fatemeh / Doraisamy, Shyamala / Azman, Azreen / Jantan, Azrul / Abdullah Ramaiah, Asri Ranga

    Computers in biology and medicine

    2013  Band 43, Heft 10, Seite(n) 1407–1414

    Abstract: Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve ... ...

    Abstract Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold. MLBS achieved an accuracy of 97.56% for classifying normal heart sound, aortic stenosis, mitral regurgitation, and aortic regurgitation. MLBS is a promising basis selection to be suggested for signals with a small range of frequencies.
    Mesh-Begriff(e) Heart Sounds/physiology ; Heart Valve Diseases/physiopathology ; Humans ; Phonocardiography/classification ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; Support Vector Machine
    Sprache Englisch
    Erscheinungsdatum 2013-10
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2013.06.016
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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