research aim :
- The research aimed to investigate the effect of two treatment
methods in the gaining of fourth grade students in geography
object.
- Research hypothesis
there are no statistically significant differences at the level of ( 0.05 )
in the average level of achievement in geography between the first
experimental group ( strengthening lessons ) and the second group
( re- teaching )
no individual differences statically significant at the level of ( 0.05 )
in the average level achievement in geography object of the second
experimental group ( re- teaching ) and the first experimental group
( strengthening lesson )
the research sample : the researcher selected randomly Baghdad
secondary for girls to represent the research community , which
return to the second directorate of Rusafa , and the number of
students assembled for the two research groups (67 ) student the
experience applied on them .
the result cause a rejection of hull hypothesis this means that the
student of first experimental group were succeeded , which were
studied by strengthen lessons on the second group which used the
method of re-teaching . These results are attributed to one or more
of the following reasons:
1- The steps that followed in teaching geography in the method of
strengthen lessons had attracted the minds of the first experimental
group and increased their interest in the article and to follow up the
object.
2- the use of the technique has move the psychological motives toward
the object which make them thinking , remembering and working
on production ideas reach them to the right results , and
deportation them of concern and collective laziness and walk
together in lesson .
This book includes four main chapters: 1. Indefinite Integral. 2. Methods of Integration. 3. Definite Integral. 4. Multiple Integral. In addition to many examples and exercises for the purpose of acquiring the student's ability to think correctly in solving mathematical questions.
A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different m
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