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The conclusion drawn from the present study shows that there was an
increase in bulk density, particle density, EC and soil available N
and K while per cent pore space, water holding capacity, organic
carbon while soil available P, Ca, Mg and S got declined. An
increase in bacterial population and reduction in fungal and
actinomycetes population was recorded. The fire has made a serious
impact on vegetational composition due to the continuous promotion
of retrogression by repeated fire occurrence. The effect of forest
fire has heavily influenced the vegetation composition of the study
site. The diversity indices and the vegetational study help to
conclude that the type of fire occurring in the study site is crown
fire and seriously damaging the tree strata in the vegetation. The
analysis in the burnt and unburnt area showed that the forest has
good regeneration potential especially after the monsoon period.
However, the regeneration potential has been seriously affected due
to regular occurrence of forest fire.The diversity indices and
vegetational study helps to conclude that the regeneration
potential of the study site is good enough to regain its original
vegetation.
This book focuses on design and development of a unified framework
for radiographic image analysis and interpretation with emphasis on
defect classification. The framework is constructed by creating two
ontologies namely process ontology and domain ontology. Process
ontology structures information for performing gray scale image
analysis and it is maintained as a knowledgebase. The images are
analyzed by using the process plan which resulted in the enhanced
images from which geometrical, statistical and textural features
are extracted to construct domain ontology. In addition to that,
details of welding defects in radiographic image are conceptualized
and maintained in the domain ontology. The framework includes a
flexible user interface through which the experts' knowledge of the
domain can be uploaded into the knowledgebase. The knowledgebase
search is minimized into single selection on the domain ontology
using which the entire content of defects and type of defect
appearing in the image can be visualized. The performance study
shows that the developed unified framework outperformed the other
two models viz. neural and statistical with respect to
classification of defects.
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