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This study demonstrates the effectiveness of multivariate statistical methods in recognizing temporal trends and interdependency of air pollutants from large and complex air quality datasets.
When considering each field separately, majority voting consistently outperformed the single methods in recognizing duration and reason.
We discuss new methods of investigating foodborne viral and parasitic disease and the future of these methods in recognizing, identifying, and controlling disease agents.
The DFT method combined with sequence profiles has been shown to be competitive with other state of the art methods in recognizing difficult structural repeats.
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The efficiency and effectiveness of the proposed method in recognizing and classifying the different herbs species is demonstrated by experiments.
The results reveal the viability of the method in recognizing patterns consistent with the reality of the electricity sector.
Finally, the efficiency and effectiveness of the proposed method in recognizing different plants is demonstrated by experiments.
To check the utility and accuracy of the method in recognizing measures' differences, other aims were comparison of two different groups of people and statistical validation.
Computer simulation results obtained with the proposed method in recognizing geometrically distorted versions of a target in cluttered and noisy scenes are discussed and compared in terms of recognition performance and complexity with existing state-of-the-art filters.
The conducted experimental results demonstrate the properness of the system design and the efficacy of the proposed method in recognizing the social cues among individuals of the group as well as the nature of the social situations concerning the group and the robot.
Overall, the results of this study show that the power of using different environments for peer review exists not in duplicating and imitating traditional methods, but in recognizing and understanding that f2f and online environments function in different ways to support peer review of writing.
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CEO of Professional Science Editing for Scientists @ prosciediting.com