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We have presented a review of the work published in the last 10 years on radar m-D target classification using SAR and ISAR systems as well as the use of this method in key emerging radar applications such as bistatic SAR, through-the-wall radars and ultrasound radars.
This paper focuses on measuring the efficiency and effectiveness of two diagramming methods employed in key informant interviews with clinicians and health care administrators.
We group the novel methods in five key categories, namely: methods using the energy of fossil fuels and biofuels to conduct hydrogen production process, novel water decomposition processes, methods that use solar energy to generate hydrogen from various sources, novel biological methods for hydrogen production.
Our approach expands on these methods in several key areas, however.
These two diagramming methods were applied in key informant interviews and their value in efficiently and effectively gathering data was assessed based on quantitative measures and qualitative observations.
These methods can differ in key aspects such as the preference elicitation technique used or the sample whose values are measured [ 20].
This review will highlight some of the most important findings that have been made using this method in several key areas of neurological disease research.
The experimental results demonstrate the efficacy of the method in identifying key regulatory networks related to the progression and recurrence of breast cancer.
Both qualitative and quantitative methods of key informant interviews, in-depth interviews and rating of the items generated by experts were conducted.
In order to decode, the encrypted image is produced by applying the same chaotic system and utilization of an equal initial value and by considering inversion methods in the decoding key.
Machine learning methods – supervised learning methods in particular – are key in building predictive models from observations, therefore facilitating knowledge discovery for complex systems.
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