Apart from the data collection technique used by the current authors, a method consisting of both the primary and secondary research would be used as it is required to understand in-depth that what the current literature suggests about the current research concern.
H1- There is a significant relationship between user acceptance of social network services and perceived usefulness
H2- There is a significant relationship between user acceptance of social network services and Actual use
As the statistical analysis, the results could be measured through conducting a correlation test among the factors of the measurement model and the variable studied as the dependent variable.
Perceived encouragement is referred to the shared perception of a group of a user that is communicated through frequency and effectiveness. Perceived orientation is the perception of the individual users regarding the referral. These two factors are responsible for influencing the users to accept the social network service.
The research’s internal validation is low as the behavioural examination is not controlled by the researchers and it can change over the changes in social network uses and concerns. This research is high in its External validity as the data collection has been conducted in a practical and implementing manner.
H1. It could be hypothesized that the features of the skeleton is a significant fact in creating invariant body shape.
H2. There is a significant relationship between illumination changes along with the change of colour.
Here, the statistical data analysis is dependent upon the TGB-based method used here. The role of Eigen values have been measured through covariance matrices. C − 1 discriminant vectors would be extracted by LDA. It has been depicted in the study that when the depth features are not available a “kernelized implicit feature transfer scheme” can be used in order to estimate Eigendepth feature in implicit manner. It has also been found that the visual ambiguities can more be reduced with the help of the combined features of the RGB-based image and the estimated features of depth. It is considered to be true even in the cases of changed illumination and changed clothes that have been considered to be highly affecting the image of the body. It is may be due to the perceptual differences that the validation of the images differs from the kernelized implicit feature.
The research’s internal validation is high as the authors have studied the variables of the research in a controlled manner. It is likely for the grouping of estimated depth feature and RGB-based depth features to reduce the ambiguity. However, the research has low external validity as the other situation such as changes of temperature and presence of other variables has not been studied.
H1- The removal of fully connected layers is responsible for increasing the parameters of Network.
The conclusion in the abstract includes that the developed network is responsible for localising the discriminative regions of image. It has been further concluded in the study that “Class Activation Mapping (CAM) for CNNs with global average pooling” can enable CNNs that are classification trained CNNs. The class scores are visualized and predicted on any image through discriminating the objective parts that CNNs detect.
The external validity of the current study is low as the study has been conducted within a controlled situation where the variables are manipulated by the researchers. However, its internal validity is high as arrangement of the variables in the same manner may provide similar results in the similar setting.
Gauld, C. S., Lewis, I., White, K. M., Fleiter, J. J., & Watson, B. (2017). “Smartphone use while driving: what factors predict young drivers' intentions to initiate, read, and respond to social interactive technology?” Published in Computers in Human Behavior, Vol. 76, Issue 1, p. 174-183,(2017).
Bulling, A., & Kunze, K. (2016). Eyewear computers for human-computer interaction. interactions, 23(3), 70-73.
Karianakis, N., Liu, Z., Chen, Y., & Soatto, S. (2018). Reinforced temporal attention and split-rate transfer for depth-based person re-identification. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 715-733).
Haque, A., Alahi, A., & Fei-Fei, L. (2016). Recurrent attention models for depth-based person identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1229-1238).
Perera, P., & Patel, V. M. (2019). Learning deep features for one-class classification. IEEE Transactions on Image Processing, 28(11), 5450-5463.
Ouyang, W., Zhou, H., Li, H., Li, Q., Yan, J., & Wang, X. (2017). Jointly learning deep features, deformable parts, occlusion and classification for pedestrian detection. IEEE transactions on pattern analysis and machine intelligence, 40(8), 1874-1887.
Remember, at the center of any academic work, lies clarity and evidence. Should you need further assistance, do look up to our Computer Science Assignment Help
Proofreading and Editing$9.00Per Page
Consultation with Expert$35.00Per Hour
Live Session 1-on-1$40.00Per 30 min.
Doing your Assignment with our resources is simple, take Expert assistance to ensure HD Grades. Here you Go....
Min Wordcount should be 2000 Min deadline should be 3 days Min Order Cost will be USD 10 User Type is All Users Coupon can use Multiple